Large Depth Completion Model from Sparse Observations
Authors: Zhu Yu
zhengyi zhao
Runmin Zhang
Lingteng Qiu
Kejie Qiu
Yisheng He
Siyu Zhu
Zilong Dong
Si-Yuan Cao
Hui-liang Shen
Venue: ICLR 2026 Poster (2026)
Keywords: Depth Completion
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: This work presents the Large Depth Completion Model (LDCM), a simple, effective, and robust framework for single-view metric depth estimation with sparse observations. Without relying on complex architectural designs, LDCM generates metric-accurate dense depth maps use a transformer. It outperforms existing approaches across diverse datasets and sparse observations. We achieve this from two key perspectives: (1) leveraging existing monocular foundation models to improve the quality of sparse depth inputs, and (2) reformulating training objectives to better capture geometric structure and metric consistency. Specifically, a Poisson-based depth initialization strategy is firstly introduced to generate a uniform coarse dense depth map from diverse sparse observations, providing a strong structural prior for the network. Regarding the training objective, we replace the conventional depth head with a point map head that regresses per-pixel 3D coordinates in camera space, enabling the model to directly learn the underlying 3D scene structure instead of performing pixel-wise depth map restoration. Moreover, this design eliminates the need for camera intrinsic parameters, allowing LDCM to naturally produce metric-scaled 3D point maps. Extensive experiments demonstrate that LDCM consistently outperforms state-of-the-art methods across multiple benchmarks and varying sparsity levels in both depth completion and point map estimation, showcasing its effectiveness and strong generalization to unseen data distributions. Code and models are publicly available at \href{https://pkqbajng.github.io/ldcm/}{pkqbajng.github.io/ldcm/}.

Published as a conference paper at ICLR 2026
LARGE DEPTH COMPLETION MODEL FROM SPARSE OBSERVATIONS
Zhu Yu' Zhengyi Zhao ?
Runmin Zhang'
Lingteng Qiu?
Kejie Qiu
Yisheng He
Siyu Zhu
Zilong Dong2t
Si-Yuan Cao 1,5,61
Hui-Liang Shen
Zhejjiang University *Tongyi Lab, Alibaba Group
"Fudan University Tech University
Ningbo Innovation Center, Zhejiang University
JJinuua Institute of Zhejiang University
[yu_zhu, cao_s i yuan)@zju. edu.cn
Project Page: https: / /tppa/////aaaaggg//gg gittegiithh.s i dcm/
Input Image
Sparse Prior
LDOM Depth
LDCM Point
KITTI 0.026
XITTI 0.055
ETH3D
Bims-1
ETH3D
Bims-I 0.026
0.008
0.012
0.058
DIODE Outdoer 0.031
DIODE Indoor
DIODE Outdoor 0.044
DIODE Indoor 0,027
0.008
NoGo
OMNE-DC
LDOM
LDCM (Ours)
UniDepth V2
PrioDDA
UniDepth V2
PriorDA
(a) Depth Completion
(b) Point Map Estimation
Figure 1: We present LDCM, a simple and effective model for depth completion. Without complex module design, LDCM achieves state-of-the-ar performance in zero-shot depth completion and metric point map estimation. On the leaderboard, larger areas indicate lower relative error (REL). LDCM ranks first across diverse datasets.
"Internship at Tongyi Lab 1 "Correspnniigg author

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Published as a conference paper at ICLR 2026
ABSTRACT
This work presents the Large Depth Completion Model (LDCM), a simple, ef- fective, and robust framework for single-view metric depth estimation with sparse observations. Without relying on complex architectural designs, LDCM generates metric-accurate dense depth maps use transformer. It outperforms existing ap- proaches across diverse datasets and sparse observations. We achieve this from two key perspectives: (1) leveraging existing monocular foundation models improve the quality of sparse depth inputs, and (2) reformulating training objec- tives to better capture geometric structure and metric consistency. Specifically, a Poisson-base based depth initialization strategy is firstly introduced to generate a uni- form coarse dense depth map from diverse sparse observations, providing strong structural prior for the network. Regarding the training objectíve, we replace the conventional depth head with a point map head that regresses per-pixel 3D coor- dinates in camera space, enabling the model to directly learn the underlying 3D scene structure instead performing pixel-wise depth map restoration. More- over, this design eliminates the need for camera intrinsic parameters, allowing LDCM to naturally produce metric-scaled 3D point maps. Extensive experiments demonstrate that LDCM consistently outperforms state-of-the- methods across multiple benchmarks and varying sparsity levels in both depth completion and point map estimation, showcasing its effectiveness and strong generalization unseen data distributions.
INTRODUCTION
Dense depth maps are essential for applications in robotics Wang et al. (2024c), autonomous driv- ing An et al. (2022), and augmented reality Krajancich et al. (2020). However, capturing dense and accurate depth data requires expensive active sensors such as LiDAR or structured light cameras, which are often limited by cost and hardware constraints. Thus, depth completion, which estimates a dense depth map from low-cost sparse depth observations and a corresponding RGB image, pro- vides a cost-effective and efficient alternative.
While prior approaches Cheng et al. (2018; 2019); Yan et al. (2022; 2025a); Park et al. (2020); Yu al. (2023); Zhou et al. (2023) perform well on in-domain datasets such as NYUv2 Silberman et al. (2012) and KITTI Uhrig et al. (2017), they often fail to generalize to unseen environments and irregular sparse depth maps (e.g., Structure-from from--ootooooo points with non non-uniform density and large missing regions), limiting their real-world applicability. Driven by the success of foundation models trained on large-scale datasets Yang et al. (2024a;b); Yin et al. (2023); Hu et al. (2024), recent works Zuo et al. (2024); Wang et al. (2023a; 2025g; 2024a; 2025a) have focused on architectural innovations and training with larger, more diverse data to improve robustness under domain shifts and varying sparsity. More recently, inspired by advances in natural language models Achiam et al. (2023); Yang et al. (2025), prompt-based approaches Lin ct al. (2025); Viola ct al. (2024); Liu et al. (2024); Park et al. (2024); Wang et al. (2025g) treat the sparse depth map as a conditioning signal for transformer-base
Yang et al. (2024a;b) or diffusion-based Ke et al. (2024); Viola et al. (2024); Liu et al. (2024) depth foundation models, guiding the prediction toward metric-scale geometry. Despite their promising results, these methods fundamentally address depth completion as depth restoration task, where the model learns to interpolate denoise depth values conditioned on the sparse observation. This paradigm prioritizes local smoothness and texture-aware completion but lacks explicit 3D geometric reasoning, leading to unsatisfactory performance under severe domain shifts and highly irregular sparse depth maps.
In this work, we introduce the Large Depth Completion Model (LDCM), which produces dense, metric-accurate depth maps even from highly sparse and irregular observations. We achieve this by enhancing the input preprocessing pipeline and reformulating the training objective. To ad- dress the challenge of sparse and irregular depth maps, we leverage a monocular depth foundation model Yang et al. (2024a;b) to enrich the geometric prior. Specifically, we construct dense gradient field by combining sparse depth map with relative depth cues predicted by the foundation model. We demonstrate that this hybrid gradient field serves as an proxy for solving a Poisson-basse op- timization problem, enabling the reconstruction of an initial coarse depth map that preserves fine
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geometric structures and exhibits metric-consistem depth values. Regarding the training objective, we replace the conventional depth regression head with a point map regression head, inspired by recent advances in 3D reconstruction Wang et al. (2024b); Leroy et al. (2024); Wang et al. (2025b); Fang et al. (2025). This reformulation explicitly encourages the network to predict metric-scale 31D coordinates, rather than focusing on pixel-wise restoration. The final depth map is obtained by extracting the z-component of the predicted point map, leading to more geometrically faithful and globally consistent predictions. Moreover, benefiting from this design, LDCM naturally predicts 3D point maps without requiring camera intrinsics, facilitating robust deployment in uncalibrated environments.
We perform extensive experiments to evaluate LDCM across six diverse benchmarks. The results demonstrate that our model surpasses all previous state-of-the-ard methods both depth completion and point map estimation, achieving top rankings across all tasks and metrics, as displayed in Fig. 1. Our contribution can be summarized as follows:
We propose the Large Depth Completion Model (LDCM), which replaces the conventional depth regression head with point map regression head to directly predict metric-scale 3D coordinates from a monocular image and sparse observations. This formulation facilitates 3D structures, leading to superior performance
more effective learning of in dense depth completion.
We introduce depth cues from a monocular depth foundation model and sparse observations. This strat- egy generates high-quality initial depth maps, providing a geometrically faithful structural prior for subsequent feature learning.
baason- coarse depth completion strategy that leverages relative
We demonstrate through extensive experiments that LDCM outperforms previous state-of- the-art methods both depth completion and metric point map estimation across diverse benchmarks and varying sparsity levels, showcasing its robust generalization to unseen data.
RELATED WORK
Depth Completion. Depth completion aims to infer a dense depth map from a monocular im- age and sparse depth map. which can be readily obtained from sources such as Structure-from Motion Schops et al. (2017) or low-cost depth cameras Silberman et al. (2012). Recent deep learning-based approaches have achieved significant progress by proposing numerous spatial prop- agation network variants Liu et al. (2017); Cheng et al. (2018; 2019); Park et al. (2020); Lin et al. (2022) or exploiting visual structural guidance from images for guided restoration. To better exploit the 3D geometric information in sparse inputs, several 2D-3D joint depth completion approaches have also been proposed Yu et al. (2023); Yan et al. (2024; 2025b); Zhou et al. (2023). Despite achieving impressive performance single-domain datasets (e.g.. NYUv2 Silberman et al. (2012) and KITTI Uhrig et al. (2017)), these methods often struggle with cross cross-domain generalization, particularly when deployed in unseen environments and varying sparse observations.
Inspired by the success of foundation models Kirillov et al. (2023); Oquab et al. (2023); Yang et al. (2024a;b); Yin et al. (2023); Hu et al. (2024); Wang et al. (2025a) trained on large-scale datasets, recent works Zuo et al. (2024); Wang et al. (2023a; 2024a; 2025g) have focused on architectural innovations and training with larger, more diverse datasets to improve generalization. More re- cently, drawing inspiration from large language models Achiam et al. (2023); Yang et al. (2025), prompt-based approaches Lin et al. (2025); Viola et al. (2024); Park et al. (2024); Jeong et al. (2025) have emerged that treat auxiliary priors as prompts to condition depth foundation models, effec- tively guiding predictions toward metric-scale outputs. PromptDA Lin et al. (2025) introduces a compact prompt fusion architecture specifically designed for the DPT head Ranfil et al. (2021), enabling the integration of low-resolution depth cues. TestPrompiDC Jeong et al. (2025) presents a test-time prompt tuning method that adapts foundation models during inference without modify- ing their parameters, achieving sensor-specific depth scale adaptation while preserving foundational knowledge. MarigoldDC Viola et al. (2024) prompts the sparse depth diffusion- Ke et al. (2024) foundation model. However, these methods fundamentally address depth completion as a depth restoration task, where the model learns interpolate denoise depth values conditioned on
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sparse inputs. The performance remains unsatisfactory under severe domain shifts and highly inreg- ular sparse depth maps. In this work, we introduce a Poisson-bart based depth initialization module to effectively maximize the potential of depth foundation models to generate coarse dense depth map, which serves as a strong structural prior for the following geometric feature learning. Besides, we reformulate the training objective as point maps, providing more structurally faithful supervision for the network.
Monocular Depth Estimation. A variety of monocular depth estimation foundation models Yang et al. (2024a;b); Piccinelli et al. (2024; 2025); Yin et al. (2023); Ke et al. (2024); Wang et al. (2025d;c) have been proposed. These models learn rich, generalizable priors from large-scale data and serve as strong backbones for downstream tasks such as stereo matching Wen et al. (2025); Jiang et al. (2025a); Cheng et al. (2025), depth super-resolution Yan et al. (2025c), depth comple- tion Park et al. (2024); Lin et al. (2025); Liu et al. (2024); Viola et al. (2024); Wang et al. (2025g), and autonomous driving et (2024); Li at. (2025); eu al. (2025); Li at. (2023a); An al. (2022). For instance, FoundationStereo Wen et al. (2025) introduces a side-tuning feature adapter that leverages monocular priors to bridge the sim-to-real domain gap. DuCos Yan et al. (2025c) treats foundation model outputs as structural priors for depth super-resolution (DSR) and seamlessly integrates them into Lagrangian duality framework. PriorDA Wang et al. (2025g) employs a local weighted linear regression (LWLR) module Xu et al. (2022) to align the scale of relative depth with sparse observations, where the result is then refined by a structure-aware network to produce dense depth map. However, this local alignment strategy often fails under highly sparse observations. In contrast, we propose a novel caasson-based initialization strategy to better exploit the potential of foundation models by enforcing gradient consistency constraints, yielding a significantly more geometrically coherent coarse depth map.
Geometry Estimation Foundation Models. Point map Wang et al. (2024b; 2025b;e); Fang et al. (2025); Gao et al. (2025); Jang et al. (2025) representation has demonstrated strong potential for holistic scene understanding. Unlike depth maps, which indeedly encode 2.51) geometry tied to camera intrinsics, point maps explicitly model 3D structure. Several approaches Yin et al. (2021); Piccinelli ct al. (2024; 2025) decouple this task into depth prediction and camera parameter esti- mation. In contrast, DUS(3R Wang et al. (2024b) bypasses explicit camera modeling by directly regressing a iivae-iivvriant point map in an end-to-end fashion, with its successor Mast3R Leroy et al. (2024) enabling metric-scale reconstruction. VGGT Wang et al. (2025b) introduces a feed- forward neural network capable of 3D reconstruction from one, a few, or even hundreds of input views of scene. AnySplat Jiang et al. (2025b) extends VGGT Wang et al. (2025b) support novel view synthesis from uncalibrated image collections. To facilitate single-view geometry learn- ing, MoGe Wang al. (2025e:f) predicts an affine-invariant point map and recovers metric scale using a global scaling factor derived from contextual cues. More recently, several approaches Liu et al. (2025); Keetha et al. (2025); Jang et al. (2025) have introduced additional priors to enhance geometry estimation. Notably, Pow3R Jang et al. (2025) extends the DUSt3R Wang et al. (2024b) paradigm by incorporating complementary modalities; however, it remains limited to relative ge- ometry. In this work, we introduce point map representations for depth completion, enabling the model to directly learn the underlying 3D scene structure and produce metric quantities. Our con- current work, MapAnything Keetha et al. (2025), also estimates metric 3D geometry from images and additional priors
METHOD
3.1 OVERALL FRAMEWORK
The framework of the proposed LDCM is illustrated in Fig. 2. Given an RGB image I R xx and sparse depth map S E HxW L.DCM predicts a metric point map P E H& x3 in camera space, from which the dense depth map is derived by extracting the z-channel component. The framework consists of two main stages. In the first stage, we harness the power of monocular depth foundation model to generate an initial coarse depth map C E "IW via Poisson reconstruction. In the second stage, a ViT-based Dosovitskiy al. (2020) depth completion network takes the image I and the coarse depth C as input to predict the final metric 3D point map P. The details of each stage are elaborated in the following sections.
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(a) Poisson based Coarse Depth Alignment
(b) Depth Completion
Depth Anything
Depth Encoder
Neck
Image
Relative Depth
Depth
Linear
Paisson Reconstraction
Erooder
Image
Nock
DPT Head
Crarse
Point
Figure 2: Schematics and detailed architecture of LDCM. Given a single image and sparse depth map, LDCM begins with a Poisson-based coarse depth alignment strategy. This process leverages pretrained depth foundation model to generate an initial coarse depth map through gradient-domain optimization. This coarse depth, together with the input image, is then fed into the following point map prediction network to regress a dense, metric-scale 3D point map.
3.2 COARSE DEPTH ALIGNMENT
Different types of sparse depth priors exhibit distinct spatial distributions, ranging from random points and Structureffrom-Motiie keypoints to LiDAR-like structured sparsity, posing significant challenges for generalization. straightforward approach involves direct interpolation depth map Liu et al. (2024); however, it often introduces severe artifacts due the absence of strong geometric priors. With the advent of depth foundation models Ranftl et al. (2020; 2021), which capture scene-level structure from large-scale training, leveraging them to provide robust geometric guidance has emerged as a promising direction.
the sparse
To integrate sparse observations with foundation model predictions, evaluate several coarse alignment strategies, including global affine alignment, local weighted linear regression (LWLR), and Poisson-based optimization. While the former two offer simple parametric alignment, they ex- hibit critical limitations. Global affine alignment assumes a uniform scale and shift across the entire image, making unable recover per-pixel metric values. LWLR improves spatial adaptivity by fitting local models, but its performance is highly sensitive to the distribution and density of sparse depth maps. contrast, Poisson-basse optimization formulates the alignment as gradient-field reconstruction problem, demonstrating superior geometric coherence and metric accuracy across di- verse sparse observations. Therefore, we adopt Poisson reconstruction the first stage of L.DCM to generate the coarse depth map C.
Specifically, given a sparse depth input S and relative depth cues D, from foundation model, we aim generate coarse dense depth map C that aligns with the geometric structure of D,, while preserving the observed values in The problem can be formulated as minimizing the following optimzation function:
C arg min
Σ Vo D. G DD;
D
SED
where G is target log-gradient field that encodes structural fidelity and metric consistency, Ω denotes the set valid sparse depth points, and À balances two terms. A naive choice is G V log D,, but this ignores the unknown scale and shift of relative depth and may lead to misaligned gradients in metric space. Instead, we construct a more informed target by incorporating metric priors from sparse observations. Let (a,,3) be the global affine transformation that best aligns D, with S:
(a,, 3) = arg min at ,
Σ (S. d' (Dr)) -
and define B/a. We then set:
G V log(D,
This choice is motivated by the fact that during training, the relative depth ground truth D, is derived from the metric ground truth D via affine transformation: D,, (D* -8)/.. While this ideal
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relationship may not strictly hold for the predicted D, during inference, introducing shift y helps align its gradient structure with the metric space. Empirically, V log(D, + 7) serves as robust proxy for the target log-gradien field, preserving fine geometric details while being anchored to the metric scale through sparse inputs. Thus, the final formulation becomes:
C = arg min
Σ Vog D; - ID, + +),11+ + A A(D;
DD S,)² -S)) ),y) ER
(4)
D
which can be solved through the conjugate gradient method Hestenes et al. (1952). In this formula- tion, each sparse point anchors the global energy, and due to the nature of gradient-domain recon- struction, its influence propagates across the entire image via the structural constraints encoded in the gradient field.
3.3 DEPTH COMPLETION NETWORK
The architecture is illustrated in Fig. 2(b). We employ dual encoders extract features from the coarse depth map C and the RGB image, respectively. Features are fused using the prompt fusion block Lin et al. (2025). For the final output, instead of regressing a depth map, we replace the stan- dard depth regression head with point map head that directly predicts per-pixel 3D coordinates P. This enables the model to learn the underlying 3D scene structure holistically, rather than per- forming pixel-wise depth restoration. Ablation studies demonstrate that this design leads to better accuracy. Moreover, thanks to this end-to-end formulation, the model naturally produces metric point maps, facilitating robust deployment in uncalibrated environments.
3.4 TRAINING
Training Losses. We train the LDCM using three complementary losses on the predicted 3D point map P, with the ground-truth metric point map denoted as P.
L =
Lglobal + Noocat + A normall
(5)
where the individual terms are defined as follows. The global point map loss enforces overall struc- tural consistency:
Lglobal
E. P. D.
Σ
P.ll.
(6)
REM
where M denotes the region of valid ground-truth. The local point map loss captures fine-grained geometry by operating 3D neighborhoods. Following Wang et al. (2025e), we sample anchor points and define spherical regions S, in 3D space:
Liocal -I. - D
jEH. iES,
This encourages local coherence independent image perspective. The normal loss promotes sur- face smoothness and alignment:
N ||
Lnormal
Σ
arecos
(8)
SE.M
where N; and Ñ; are surface normals estimated from P and P,, respectively.
Implementation Details. We train the LDCM on public RGB-D datasets Roberts et al. (2021); Wang et al. (2020; 2019); Zheng al. (2023); Gómez et al. (2025); Wrenninge & Unger (2018); Li et al. (2023b); LightwheelAl contributors (2024); Huang et al. (2018); Ros et al. (2016); Yesh- wanth al. (2023), approximately 2.7 million samples. The combined data covers diverse indoor and outdoor scenes; further details are provided in the suppl. material.
LDCM uses ViT-B Dosovitskiy et al. (2020) pretrained with DINOv2 Oquab et al. (2023) as the image encoder. For coarse depth alignment, we use DepthAnything V2-S Yang et al. (2024b) as the foundation model. Training runs for 200 K iterations using the AdamW optimizer Loshchilov &

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Published as a conference paper at ICLR 2026
Table 1: Quantitative comparison of depth completion methods on benchmark datasets. All
methods are evaluated under zero-shot settings. Methods marked with i produce relative depth, and metric depth recovered by optimizing global scale and shift via least squares regression using the same sparse depth prior. Methods marked with & use -ataset-specific configurations for indoor and outdoor scenes, respectively. The best and second-bee best results are highlighted.
Method
KIVTI
iBin-1
DIODE Inkku
RMSFL
MAF]
RFL.]
6, 1
RL.
RMSF]
MAR]
RFI.]
6,7
Bk.
RMSF]
MAF]
RFI.
411
Rk...
DeathiPro
4.149
10.17
0.731
0:601
0:156
13:750
0.857
0.702
0.193
14:114
UniDeyth
3,335
0.118
0.928
1.365
1,052
0.370
0.236
16.000
0.939
0.540
0.158
13,523
UniDepth
3.150
0.960
0:445
0.100
0.925
11.932
0.811
0.678
0.165
13.023
A1
0.916
0.033
0.975
0.045
7.295
4.219
0.158
0,957
0.294
0.086
10:773
0.687
0.236
0035
MaCle
0.458
15.
0.16
0.707
0.235
Ch-MinoDDppht
0.949
0:327
0028
0.973
0.208
0.198
0.067
6.341
OMNN-DC
2,502
0.760
0.42
0.963
3.045
0.192
0.063
0.05
0.982
2,932
0.141
0.064
0.022
0.565
2.982
PA
2,564
0.361
0.044
0.971
4.150
0.176
0.065
0018
0.990
3.777
0.093
0.017
0.02
0.994
3/233
SPNed
2.365
0000
0.966
3.000
0.189
0016
0,987
0.157
0.078
0.028
3:275
PremnpDA
3,040
0.067
0.946
0.349
0.116
0.33
0.975
0.213
0.115
0.077
6.068
4.439
0.142
0.824
11.818
0.352
0.192
0051
0.963
9.305
0.386
0.243
0.084
9.361
ManAmwthing
12,974
0.350
0.588
0.968
0.104
0.909
13.295
0.009
0.458
0.104
Pow3Rt
3,515
0.341
0.832
10.750
0.338
0.153
0.089
0.965
0.353
0.240
0.078
9,000
LDCM rOun))
E911
0:337
0,026
0.985
1.088
0.361
0.014
0.012
0.991
1,689
0.084
0.025
0.008
0.993
1.545
Methoal
DIDDE Oundoar
ETH3D
Avcrage
RMSEL
MAEL
RBC]
6,1 1.1
R3.1
RMSCL
MAE1
REL.
6,0
Rt.
RMSE1
MAE1
RCL.]
6.1
Rk...
DepthPro
9,539
7.635
0.405
0.177
14.636
3.199
2.562
0.302
0.477
15,025
3.666
2,835
0.346
0.576
[4.19]
UniDepth
5.782
3.841
0.189
0.561
11.795
3.482
3.170
0.579
0.116
15.728
2.961
2.189
0.283
0.395
13.136
UniDeyth
11.145
0.515
0.526
15.250
1.630
1.169
0.200
3.456
2.540
0.214
12.015
5.400
0.124
0.869
5.659
2.091
0.089
0.979
2.555
3.092
0.071
8.563
VOGTT
4.898
0.237
0,772
10.591
0.540
0.060
0.949
1.243
0.121
10.739
MoGe Vi-
10.576
8,300
0.405
0.599
14.250
1
0.550
0.082
0.943
8,750
3.157
2.201
0.142
0.872
9,000
MaGe V2
4,807
0.382
0.580
10,477
0.347
0.114
0.339
11.784
2.344
1.763
0.150
13:357
Mepo
2,995
0.375
0/02
0.918
4.652
0.428
0.044
0.969
5.005
1.197
0.462
0.049
0.942
5,465
OMNLDC DC
2.522
0.049
0.955
3,341
0.290
0.100
0016
0
2.932
1.049
0.343
0.029
0.971
3.036
PrinDA
2,510
0.858
0.601
0.957
3.932
0:274
0.105
0.07
(0.990
3.445
11943
0.385
0.073
0.900
3.607
SPNett
2.131
6.465
0.959
0.419
0.139
0.019
0.334
0.030
2.934
PromptDA
3,604
0,912
0.644
0.276
0.041
0.967
0.666
0.053
6.396
WorldMinoot
4,464
0.151
0.828
0.524
0.051
0.962
7,761
2,053
1.097
0.096
9,239
MapAAnthhng
1,675
0.219
0.731
1.952
0.108
0.904
12.525
4.845
2,443
0.177
12,777
Pow3Rkt
3,682
0.169
0.640
7.558
0.480
0.043
0.964
7,545
0.972
0.057
0.509
8.791
LDCM (Ouns)
0.910
0.187
0.237
0.017
1.398
Hutter (2017) with a cosine learning rate schedule and linear warmup over the first 5% of iterations. The peak learning rates are x 10-5 5 for the encoder and 1 x 10-4 for all other layers. We use global batch size of 128, with mini-batchi sampling an approximately equal number of images from each dataset. During training, images are resized such that their aspect ratios range uniformly from 1 2 to 2- 1, and total pixel counts fall between 250 K and 500 K. Data augmentation includes random cropping, color jittering, Gaussian blur, JPEG compression-decompessii perspective-aware cropping to align the principal point with the image center. Sparse depth inputs are synthetically generated by subsampling dense ground-truth depth maps with varying patterns, following the protocol of OMNI-DC Zuo et al. (2024). The training is conducted on 16 H20 GPUs and takes approximately six days to complete.
and
4 EXPERIMENTS
4.1 QUANTITATIVE EVALUATIONS
We evaluate the zero-shot performance of LDCM and compare it with several state-of-the- ap- proaches for depth completion Wang et al. (2023a): Zuo et al. (2024); Wang et al. (2024a); Lin al. (2025); Wang et al. (2025g), monocular depth estimation Yang et al. (2024a;b); Wang et al. (2025b); Bochkovskiy et al. (2025), and monocular point map estimation Piccinelli et al. (2024; 2025); Wang et al. (2025e:f); Liu et al. (2025); Jang et al. (2025); Keetha et al. (2025). Additional details on the compared approaches and evaluation protocols are provided in the suppl. material. As demonstrated in the experiments, LDCM achieves superior performance across multiple benchmarks.
Depth completion. We evaluate depth completion on KITTI Uhrig et al. (2017), ETH3D Schops et al. (2017), iBims-1 Koch et al. (2018), and DIODE Vasiljevic et al. (2019), covering both indoor and outdoor scenarios. To assess robustness under diverse sparse sampling patterns, we synthesize sparse depth inputs using the following strategies:
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Published as a conference paper at ICLR 2026
Noisy random sampling: uniformly sampled points at varying densities (e.g.. 1%, 3%, 5%, 10%), with mild noise simulation;
Reypoint-based sampling: depth values extracted SIFT or ORB keypoints:
LiDAR-simulated sampling: synthetic LiDAR scans with varying numbers of vertical beams (e.g., 64, 32, 16 lines).
On KITTI, the simulation is applied to raw single-frame LiDAR measurements Zuo et al. (2024); Wang et al. (2023b). For all other datasets, they generated from dense ground-truth depth maps. We evaluate the predicted depth maps using standard metrics: Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Relative Error (REL), and the accuracy threshold òn. For methods that produce relative depth maps Wang et al. (2025b); Yang et al. (2024b); Wang et al. (2025e), we recover the global scale and shift via least squares regression using the sparse depth prior. Table reports the average RMSE, MAE, REL, and 5, across all synthetic patterns per dataset, along with the mean ranking over competing methods. As shown the table, LDCM achieves state-of-the-an performance. Notably, it maintains high accuracy even under extreme sparsity, demonstrating strong robustness and generalization across diverse sparse input configurations.
Table 2: Quantitative comparison of point map estimation methods on benchmark datasets. All methods are evaluated under zero-shot settings. The best and second-best results are highlighted.
Metiod
KITTI
iBime-
DIDDE Inaiorr
MAE'
RMSF*
RFI |
4'1
[R.]
MAP
RMSFP
REIP
6''
[L.]
MAF
RMSPP
RFI." L
6''
RL.1
UniDepth
3.540
0.120
1.134
0.911
0.379
7.318
UniDepth
3.540
0.036
0.730
0.164
0.694
7.273
MonoDDept
3.118
0.098
4.841
0.186
0.287
4.700
0.305
0.875
4.841
OMNT-DC
2.528
0.092
3.409
0.164
0.356
0.900
0.174
0.967
2.977
PrierDA
1.573
2.836
0.091
0.96
4.341
0.159
0.240
0.043
0.989
3,500
0.140
0.190
0.034
0.994
2.909
SPNet
3.507
2,881
11089
0.964
3.068
0.152
0.299
0.942
0.988
2:455
0.172
0.236
0.046
0.93
2.636
PrompIDA
3,933
3,012
0.110
0.338
0.059
0.199
0.309
0.241
0.975
5.545
0.204
0,301
0.006
0.903
5.523
'LDCM (Oum)
1.827
2.388
IL1S8
0.82
LO45
0.092
8.194
00036
1U992
1.000
0.127
0.179
08,27
0.992
1.199
Method
DIODE Oundoor
ETH3D
Average
MAP
KMS17
RBLF
41
K3.1
MAD
REL/
4'
R1.1
RMINE
RUL7
611
RK_1
UniDepth
5:100
0.461
0.145
9/000
3.54]
3.575
0.551
0.106
5.000
2.495
2.956
0.332
0.445
8.218
Unillqphh V2
1,500
2.544
0.216
0.712
8.000
1.252
1.785
0.191
0.369
8.000
1.208
1.906
0.355
0.314
7.118
MaGi V2
0.931
1,306
0.115
0.800
5.977
0.16
0.913
0.119
0.855
6.409
1.361
1.734
0.168
0.678
7.668
G2 MonoDDept
0.794
1.129
11100
03511
4.964
0.503
0.26
0.105
0.911
5.100
1.731
1.153
0.000
0.919
4.891
OMNI-ICC
0.714
0.965
01985
0.15
2.785
0.50
0.99
0.79
5756
0.6.9
0.9966
0.075
0.990
5161
PrioDDM
0.908
0.095
01622
0.352
3.177
PrompiDA
1.422
0.100
0.911
5.591
0.592
0.750
0.944
5.495
LOUM (Ours)
6.427
0.580
11:18
0.995
1.000
0.547
0.456
0.058
0.996
1.000
11.084
0.748
0.842
0.991
1.041
Point map estimation. We adopt the same benchmarks used for depth completion to evaluate monocular point map estimation. The predicted point maps are evaluated using point-wise metries: RMSE", MAEP, REL" and op. Table 2 reports the average performance across all synthetic patterns per dataset for each metric. For depth completion methods, we use the camera intrinsics from UniDepth V2 Piccinelli et al. (2025) to back-project the completed depth maps into 3D point maps. As shown in the table, LDCM consistently outperforms all competing methods, achieving the best results across all datasets and metrics.
Aftine-inarraant point map estimation. We adopt the same benchmarks evaluate monocular affine-invariant point map estimation. Following MoGe Wang et al. (2025e), we resolve the scale and shift of the predicted point map using the proposed ROE solver to align it with the ground truth. Table 3 reports the average performance in terms of REL and 8.. As shown in the table, our method achieves superior performance compared to baseline approaches and outperforms state-of-the-art relative geometry estimation methods, including VGGT Wang et al. (2025b) and WorldMirror Liu ct al. (2025). This demonstrates that our model preserves preserves-rather rather than compromises -tee accuracy of relative geometry estimation.
4.2 ABLATION STUDY
We conduct ablation studies to evaluate the effectiveness of the Poisson-based coarse depth align- ment strategy and the training objectives. For simplicity, we adopt (64, 32, 16, and 8 lines) outdoor datasets, and keypoint-based sampling indoor datasets.
sparse patterns
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Published as a conference paper at ICLR 2026
Table Quantitative comparison of affine-invariant point map estimation methods on bench- mark datasets. All methods are evaluated under zero-shot settings. The results are highlighted.
best
and second-best
KITTI
iBims-I
DIODE Indoor
Method
REL"
8f f
Rk.1
REL"
8
Rk.4
REL"
8"
Rk.1
VGGT
0.147
0.823
4.500
0.048
0.967
3,909
0.107
0.926
4.636
MoGe V2
0.056
0.968
1.909
0.046
0.972
2.455
0.052
0.972
1.955
WorldMirror
0.108
0.886
3.136
0.044
0.965
2.864
0.073
0.953
3,091
MapAnything
0.366
0.344
6,000
0.233
0,611
6.000
0.172
0.758
6.000
Pow3R
0.152
0.850
4.318
0.064
0.965
4.318
0.108
0.947
4.273
LDCM (Ours)
0.039
0.983
1.091
0.017
0.992
1.000
0.014
0.995
1.000
DIODE Outdoor
ETH3D
Average
Method
REL"
of f
Rk.1
REL"
of t
Rk.4
REL*
8" 1
Rk.4
VGGT
0.215
0.700
5,000
0.053
0.978
3.591
0.114
0.879
4.327
MoGe V2
0.124
0.841
2,000
0.044
0.980
2.637
0.064
0.947
2.191
WorldMirror
0.155
0.788
3.045
0.049
0.976
3.023
0.086
0.914
3.032
MapAnything
0.302
0.501
6.000
0.265
0.549
6,000
0.268
0.553
6.000
Pow3R
0.197
0.750
3.955
0.074
0.982
3.796
0.119
0.899
4.132
IDCMM (Ours)
0.077
0.949
1.000
0.039
0.994
1.728
0.037
0.983
1.164
Table 4: ablation study on the coarse depth alignment strategy. We report the relative error (REL)
for coarse depth and final prediction. The best and second-bes results are highlighted.
Configuration
Cors Depth (REL
Estimated Depth (REL
KITTI
iBims-I
DIODE
ETH3D
Average
KITTI
iBims-1
DIODE
ETH3D
Average
Sparse
0.026
Global alignment
0.095
0.075
0.102
0.078
0.087
0.020
0.019
0.035
0.023
0.024
LWLR
0.078
0.108
0.108
0.061
0.088
0.019
0.022
0.036
0.021
0.025
w/o global alignment
0.208
0.174
0.138
0.147
Poisson
0.088
0.044
0.059
0.019
0.033
Coarse Depth Alignment Strategy. We ablate various coarse depth alignment strategies for robust geometric guidance. First, we assess the accuracy of the generated coarse depth maps. As shown on the left side of Table 4, Poisson-based alignment achieves the best performance, demonstrating its effectiveness. Notably, global alignment is essential its omission leads to a significant performance drop. By comparison, LWLR performs worse than sparsity, highlighting its sensitivity to sparse and irregular inputs. A qualitative ablation example is provided in Fig. 3, where the Poisson-based method not only achieves the highest accuracy but also best preserves geometric structure. On the right side of Table 4, we use these coarse depth maps as inputs our completion model; again, the Poisson- based variant yields the best results.
simple global alignment under extreme
Table 5: Ablation study the output representation. We report the relative error (REL) for depth completion and REL" for point map estimation. The best and second-best results are highlighted.
Depth Completion (REL 1)
Point Mup Estimatiin (REI, ())
Configuration
KITTI
DIODE
ETH3D
Average
iBims-1
DIODE
ETH3D
Average
SI-Log Depth
0.023
0.023
0.037
0.021
0.026
SI-Log Depth + Ray map
0.022
0.022
0.038
0.021
0.026
0.073
0.050
0.084
0.097
0.067
Point Map
0.019
0.018
0.033
0.019
0.022
0.047
0.032
0.070
0.059
0.045
Output Representation. We ablate the output representation by replacing the point map with either conventional depth map the concatenation of depth and dense ray maps (depth ray map). As shown in Table 5, both alternatives lead to performance degradation, demonstrating that the point map provides more effective 3D structural guidance than depth-based representations.

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Published as a conference paper at ICLR 2026
0.031
0.035
0.023
RGB
Sparse
Global align
LWLR
Poisson
Qualitative comparison between three coarse alignment stragies. We report the relative emror for each result.
Figure 3:
4.3 DEPTH COMPLETION RESULTS STANDARD BENCHMARKS
Table 6: Quantitative comparison of depth completion methods on real-pattern benchmark datasets. All methods are evaluated under zero-shot settings. Methods marked with f produce relative depth, and metric depth is recovered by optimizing global scale and shift via least squares regression using the same sparse depth prior. Methods marked with $ use sataset-sppeific con- figurations for indoor and outdoor scenes, respectively. The best and second best results are highlighted.
Mathod
NYLv22
VOID
ETH3D
RMSE!
MAEI
RELI
3,
RE_1
RMSEL
MAEI
RELI
01.7
RE..
RMSEL
MAE]
REL.
0g
Rk...
DeathPro
0.189
0.726
UniDepth
0.107
3.170
0.579
UniDepth
0.93
0.948
0.651
0.115
0,000
0.200
13.000
AxpthAnythnng211
0.220
0.128
0.45
0.917
11,250
0.605
0.214
0.63
0.958
8,250
1915
0.693
0.063
0.66
6,500
VGGTT)
0.33
0.985
B.044
0.432
0.095
MoGe V1-
0.180
0.979
0.577
0.044
0.952
7,500
2.777
0.450
0.108
6.500
MoGe V2
0.261
0.000
0
0.779
0.202
0.557
0.619
0.114
9.375
MopoD
0.166
0.025
0.985
0.507
0.555
0.912
7,500
1.425
0.136
10.375
OMNI-DC
0.147
0.000
0.987
4.375
0.574
0040
0.962
0.222
0.317
0.079
4,625
PaoDA
0:122
0.0-7
0.017
0.993
2.750
0.571
0.171
0.099
0.968
3.335
0.671
0.250
0.061
0.662
2.500
5.250
6.625
PrompeDA
0.162
0.079
0,025
0.989
6,000
0.365
0.182
B.899
0.965
4,000
0.11
0.483
0.090
0.396
6,375
WodldMiroct
0.217
0.121
0,042
0.979
10.125
0.595
0.208
01077
0.46
9,833
0.988
0.668
0.15
0.33
9.250
MapAnsthing
0.724
0.321
0.132
0.885
16,000
0.782
0.282
0.110
0.900
13350
2.283
0.874
0.15
0.661
12.375
Pow3kt
0.155
0.081
0.031
0.988
5.60 5.625
0.571
0.196
0.077
0.949
7,303
0.861
0.656
0.154
0.33
9.875
LDCM (Ouna
0.113
0.037
00.13
8.994
1.000
0.536
0.145
0.025
0.977
1.000
0.468
6.154
0.033
0.978
1.250
To further evaluate zero-shot depth completion under real-world sparse patterns, we follow prior work in evaluating methods on the NYUv2 Silberman al. (2012), VOID Wong al. (2020), and ETH3D Schops et al. (2017) datasets. For NYUv2, we adopt the sampling protocol from OMNI- DC Zuo al. (2024), extracting 500 and 100 sparse depth points per image, respectively. For VOID, we use the provided sparse depth maps derived from a visual-inertial odometry system, which come in three sparsity levels: 1500, 500, and 1.50 points per frame. For ETH3D, we project the sparse 3D points from COLMAP SfM reconstructions into the image plane to generate sparse depth maps. Table reports the quantitative results on each dataset. shown the table, LDCM significantly outperforms all comparison methods, ranking first all the datasets.
5 CONCLUSION
We have presented the Large Depth Completion Model (LDCM), simple yet powerful framework for metric depth estimation from sparse observations. LDCM is both effective and robust, leverag- ing boisson-based alignment strategy to maximize the potential of existing monocular foundation models by preprocessing input sparse observations into strong geometric priors for subsequent fea- ture learning. Furthermore, LDCM replaces the conventional depth map representation with point map representation, enabling direct learning of the underlying 3D structure rather than per-pixel depth restoration. Our method achieves superior zero-shot performance across multiple bench- marks, demonstrating robustness under varying sparse observation patterns. Moreover, the point map design allows LDCM naturally output metric-scaled 3D geometry without requiring camera intrinsics, facilitating reliable deployment in uncalibrated environments. We believe LDCM marks significant advancement in depth completion and can serve as a robust foundational model for downstream 3D vision tasks.
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Published as a conference paper at ICLR 2026
ACKNOWLEDGMENTS
This work was supported in part by the National Natural Science Foundation of China under grant 62301484, part by the Jinhua Science and Technology Bureau Project under grant 2026-1-022, in part by the Young Talent Fund of Zhejiang Association for Science and Technology under grant ZISKXQT2026135, and in part by the Ningbo Natural Science Foundation of China under grant 2024J454.
ETHICS STATEMENT
Our study focuses on depth completion, core problem in the field of computer vision. The exper- imental evaluation is based exclusively on public datasets that have been curated without inclusion of any personally identifiable or sensitive content. We assert that this research has been carried out in accordance with the code of ethics.
REPRODUCIBILITY STATEMENT
To facilitate verification and extension of our work, we include the implementation code in the supplementary materials. Furthermore, we provide key training and evaluation procedures in the paper, and will make the complete code and trained models publicly available after publication to support full experimental reproducibility.
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APPENDIX
A DATASETS
A.1 TRAINING DATASETS
We collected 11 open-source RGB-D datasets to train LDCM, comprising 10 synthetic and 1 real- world dataset. An overview the training datasets is provided in Table 7, spanning four distinct domains: indoor, outdoor, in-the-wild, and driving scenarios. The combined training set contains approximately 2.6 million images. The number of RGB-D pairs each dataset may slightly differ manually excluded some invalid frames.
from the originally released versions,
Table 7: An overview of the training datasets.
Dataset
Domain
Statistic
Type
Hypersim Roberts et al. (2021)
Indoor
75K
Synthetic
TartanAir Wang et al. (2020)
In ln-the-wild
306K
Synthetic
IRS Wang et al. (2019)
Indoor
101K
Synthetic
PointOdyssey Zheng et al. (2023)
Indoor
303K
Synthetic
UrbanSyn Gómez et al. (2025)
Outdoor/Driving
7K
Synthetic
Synscapes Wrenninge & Unger (2018)
Outdoor/Driving
25K
Synthetic
MatrixCity Li et al. (2023b)
Outdoor/Drivin
424K
Synthetic
LightwheelOce LightwheelAA & contributors (2024)
Outvoor/Driving
204K
Synthetic
MVS-Synth Huang et al. (2018)
Outdoor/Driving
12K
Synthetic
Synthia Ros al. (2016)
Outdoor/Driving
140K
Synthetic
ScanNet++ Yeshwanth et al. (2023)
Indoor
1M
Real
Total
2.6M
A.2 EVALUATION DATASETS
We use six datasets that are excluded from the training set for to compare the performance between LDCM and previous state-of-the-ee methods. Below, provide details for each dataset.
NYUv2 Dataset. The NYUv2 dataset Silberman et al. (2012) is an indoor dataset captured using a Microsoft Kinect sensor, containing RGB and depth sequences from 464 indoor scenes. The official test split contains 654 samples. Following Marigold Ke et al. (2024), we crop the images to a resolution of 426 560 for consistent input dimensions.
KITTI Dataset. The KITTI Depth dataset Geiger et al. (2012); Uhrig et al. (2017) is a large- scale outdoor dataset collected from moving vehicle. The official validation split consists of 1,000 samples. Depth maps are acquired using an HDL-64 LiDAR sensor, with raw depth maps containing fewer than 6% valid pixels. The provided ground truth generated by fusing multiple consecutive LiDAR scans, resulting in a denser depth map with approximately 14% valid pixels. For depth completion, input images are center-cropped to the bottom region of 252 x 1216 exclude the sky and regions with unreliable depth due to the limited vertical field of view of the LiDAR.
DIODE Dataset. The DIODE dataset Vasiljevic et al. (2019) contains thousands of lighrresolution RGB images with accurate, dense, and long-range depth measurements, captured using FARO Foeus $350 laser scanner. The official validation split includes indoor and 3 outdoor scenes, comprising 325 and 446 samples, respectively. To reduce noise occlusion boundaries, we filter out depth values where the maximum relative difference to any neighboring pixel exceeds 5% (indoor) and 15% (outdoor). Input images resized to 480 x 640.
iBims-1 1 Dataset. The iBims-1 dataset Koch et al. (2018) is an indoor benchmark captured in diverse environments, providing high-resolution RGB images and highly accurate depth maps derived from laser scans. The official evaluation split contains 100 samples, with images at a native resolution of
480 x 640.
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VOID Dataset. The VOID dataset Wong et al. (2020) is indoor dataset captured using the Intel RealSense D435i camera. The official validation split consists of 800 samples, each paired with sparse depth maps at three sparsity levels (approximately 1500, 500, and 150 valid pixels) and RGB images at a resolution of 480 x 640. These varying sparsity levels allow for robust evaluation under different input conditions.
ETH3D Dataset. The ETH3D dataset Schops et al. (2017) consists of multi-view stereo images and dense depth maps captured using a high-precision laser scanner and DSLR cameras, covering diverse viewpoints and scene types. The official validation set contains 13 scenes with a total of 454 image pairs. The original image resolution is 4032 6048. Input images are resized to 480 x 640.
B EVALUATION DETAILS
B. COMPARISON METHODS
We compare LDCM against comprehensive set state-of-the-art approaches: Depth- Pro Bochkovskiy et al. (2025), UniDepth VI & V2 Piccinelli et al. (2024; 2025), Depth Anything V2 Yang et al. (2024b), VGGT Wang et (2025b), MoGe VI V2 Wang et al. (2025e:f),
G2-MonoDepth Wang et al. (2023a), OMNI-DC' Zuo et al. (2024), PriorDA Wang et al. (2025g),
SPNet Wang et al. (2024a), PromptDA 10 Lin al. (2025), Marigold-DC" Viola et al. (2024), DepthLab Liu et al. (2024), Pow3R Jang et al. (2025), MapAnything" Keetha et al. (2025), WorldMirro Liu et al. (2025), spanning the key tasks of monocular depth estimation, monocular geometry estimation, depth completion. All methods are evaluated using their publicly available implementations and pre-trained checkpoints. Notably, G2-MonoDepth Wang et al. (2023a) and SPNet Wang et al. (2024a) employ different configurations for indoor and outdoor scenarios, while LDCM and the remaining methods do not use scenario-specific hyperparameters. PromptDA Lin al. (2025) is specifically designed leverage dense, low-resolution priors; therefore, we apply Poisson surface reconstruction to the input sparse depth map to obtain dense prior before inference. Pow3R Jang et al. (2025) and WorldMirror Liu al. (2025) produce relative geometry, even when sparse depth priors are provided.
B.2 EVALUATION PROTOOL
To clarity the notations in this section:
P and P are the predicted and ground truth points, respectively.
and D are the predicted and ground truth depths, which are the z-coordinate of corre-
sponding points.
.M is the mask of valid ground truth.
Depth Completion. In the manuscript, use four standard metrics for depth completion evalua- tion, including RMSE, MAE, REL, 61. Formally, they are defined as follows:
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Root mean square enror (RMSE) (RMSE):
CD: D
-D)
(9)
[M
IEM
Mean absolute error (MAE):
1
Σ
D - D.
(10)
M
SE
Mean relative enror (REL):
D - D
(11)
M
D;
iEM
Thresholded accuracy (δ1):
D; D
max
1.25
(12)
M
D D:
EM
For models that produce relative depth maps D,, we first follow Equation 2 to compute (a, 9), and then the metric depth maps are recoverd by:
D D B.
(13)
Point Map Estimation. For evaluating the reconstructed 3D point map, we adopt analogous met- rics based on Euclidean distances between predicted and ground truth points. The metries include RMSE", MAE", REL", and 8", defined as:
Point-wise Root Mean Square Error (RMSEP):
Σ SEM
p. P P.
(14)
M
Point-wise Mean Absolute Error (MAF"):
Σ
P P.
(15)
M
iEM
Point-wise Mean Relative Error (RELP):
P; P.
(16)
M
P
iEM
Point-wise Thresholded Accuracy (8p):
1
Σ iEM
p. -
< 0.25 min (IP.I..IP..l))
(17)
M
Affine-ivvariant Point Map Estimation. To evaluate the intaeeinvarrint point, we first compute the scale ap and shift 3p using the following equation, which recovers the affine transformation applied to the predicted point map. This equation can be solved efficiently using the ROE solver proposed by MoGe Wang et al. (2025e).
(apps 3p) = arg min
Σ (P..-a, P. iEM
. PP-Bpp) 33) -P.
(18)
ago
C MORE QUANTITATIVE RESULTS
From Table 12 to Table 29, we provide detailed quantitative results under different types of sparse observations.
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Table Quantitative comparison of depth completion with diffusion--ased methods on bench- mark datasets. The best results are in bold.
VOID-1500-Points
VOID-500-Points
VOID-150-Points
Method
RMSEJ
MAEJ
REL
61
RMSEL
MAE,
RELL
61
RMSEL
MAE]
REL.
6
DepihLab
0,577
0.162
0.034
0.969
0.572
0,183
0.053
0.941
0,688
0.249
0.083
0.901
Marigold-D& DC
0.553
0.154
0.031
0.975
0.536
0.162
0.043
0.965
0.626
0.199
0.053
0.955
LDCM (Ours)
0.981
0.035
0.972
NYUv2-500-Poinss
NYUV2-100-PPinng
KITTI-64-Lines
Method
RMSE
MAEI
REL
61
RMSEL
MAE,
RELL
61
RMSEL
MAEI
REL
61
DepthLub
0.118
0.041
0.015
0.993
0.213
0.100
0.037
0.976
2.032
0.828
0.061
0.962
Marigold-DC
0.116
0.040
0.014
0.993
0.157
0.061
0.022
0.988
1.931
0.818
0.054
0.971
LDCM (Ours)
0.094
0.996
0.992
1.240
0.292
0.016
Method
KITTI-32-Liies
KITTI--6-LLines
AVERAGE
RMSEJ
MAEI
REL
8,
RMSET
MAE,
REL]
8,
RMSEL
MAEI
RELJ
6,
DepthLab
2.250
0.893
0.064
0.959
2.748
0.932
0.066
0.953
1.150
0.424
0.052
0.957
Marigold-DC
2.155
0.875
0.057
0.968
2.546
0.981
0.062
0.963
1.078
0.411
0.042
0.972
LDCM (Ours)
0.991
0.987
D MORE COMPARISON RESULTS WITH DIFFUSION-BASE METHODS
Here, we present additional comparisons with diffusion brases models Marillsd DDC Viola et al. (2024) and DepthLab Liu et al. (2024). 3. Due to their prohibitively long inference times, we evaluate these methods primarily on three benchmarks with varying levels of sparse input: NYUv2 Silberman et al. (2012) (500 and 100 points), VOID Wong et al. (2020) (150, 500, and 1500 points), and KITTI Geiger et al. (2012) (64, 32, and 16 scan lines). As shown in Table LDCM consistently outperforms both Marigold-DC and DepthLab across all settings.
Table Ablation study on the training data. We report the relative error (REL) for depth completion.
Configuration
Depth Completion (REL L)
KITTI
iBims-1
DIODE
ETHED
Average
Ours
0.019
0.018
0J033
0.019
0.022
Input Image
Sparse Prior
w/ more real data
result
Figure 4: Qualitative comparison between the results from models using different training datasets.
E ABLATION ON THE TRAINING DATA
Training Data. We perform an ablation study on the training data used to train the LDCM. In addition to the original datasets, introduce an extra dataset: StivingStereo Yang et al. (2019). The quantitative results are presented in Table As shown, the inclusion of this additional data does not significantly affect metric performance. However, illustrated in Fig. 4, incorporating more real-worls data leads to visually less sharp predictions, likely due to imperfect supervision signals in the added dataset.
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Table 10: Quantitative comparison of depth completion methods on benchmark datasets. All methods are evaluated under zero-shot settings. Methods marked with i produce relative depth, and metric depth recovered by optimizing global scale and shift via least squares regression using the same sparse depth prior. The best and second- best results are highlighted.
Methed
iBime1
DIOBE Indaer
RMSEJ
MAE]
REL.
4,7
RX_
R34SE
MIAE.
REL]
6g
RL..
RMSEL
MAE]
REL..
4.7
Rl_
22ghtAnyhhing
4,407
1,800
01922
0.915
5.318
0.349
0.17M
0.43
03335
5,614
0.35
0.185
11345
0.995
4.50
DrphAryytingV2 Poisern
7.648
0.951
0,51
(3990
3.955
0.211
0198
0.033
0.376
3,156
0.195
0.091
10036
0.967
3.705
VGGTT
4,219
2.318
0.158
0.793
6.955
0.348
1.19%
0.033
0.577
6,727
0.425
0.244
0,8%
0.50
6.750
VGGT wi Prasson
3,627
1.312
0.88
0.917
4.305
0.341
0.104
0.055
0.355
4.316
0.217
0.111
0.037
0.057
4.341
McGe V1j
3,000
1.821
0.125
0.887
5.341
0.258
0.120
0.085
0.981
4,159
0.272
0.175
0.064
0.950
5.250
Poisson
0.865
0.025
0.965
0.012
Methed
DIODE Chilaeer
ET100
XMSEJ
MAE1
REL.
4,
R3.
R34SE
MA.E.
REL
61
RL..
RMSEL
MAEL
REL.
4.77
R3.
DepilAry V2]
0.943
5.364
DephArgehingyY
VGGT!
2,803
0.237
0.540
0.317
0.000
2.086
1.243
0.121
0.876
6.391
VGGT Prasson
3,910
1.367
0.041
0.91
1350
0.319
140
0.044
0.900
3.262
1.367
0.566
0047
0.991
3479
MoGe VIT
10.576
8,340
0.406
0.599
6.932
1.611
0.550
0.082
0.433
5.455
3.157
2,201
0.142
0.872
5.427
2,500
00910
0.319
3.048
0.967
2.254
F APPLYING POISSON-BASED ALIGNMENT STRATEGY TO MONOCULAR ESTIMATORS
In Table 10, we apply the Poisson alignment strategy to relative geometry estimators to obtain dense depth maps. As shown the table. this strategy effectively improves the metric accuracy of these approaches, demonstrating its effectiveness. Moreover, our LDCM maintains state-of-the per- formance.
G INFERENCE TIME
We report the per-stage inference times of our method, measured a resolution of 480x640 on an NVIDIA 1.20 GPU. Our pipeline comprises four stages: Depth Anything Small (0.006 s), global alignment (0.006 s), Poisson-based alignment (0.020 s), and the subsequent refinement model (0.040 s), resulting in a total runtime of 0.072 s. For comparison, LWLR runs in 0.010 under the same con- ditions. detailed comparison with the inference times several competing methods is provided in Table 11.
Table 11: Inference time (in seconds) of different methods at 480 x 640 resolution on an NVIDIA
L20 GPU, with all inference performed in FP32 precision.
Method
OMNI-DC
PriorDA
DepthPro
VGGT
MoGe V2
DepthAnythingV2
LDCM (Ours)
Inference Time (s)
0.128
0.064
0.554
0.196
0.220
0.019
0.072
H MORE QUALITATIVE RESULTS
Fig. and Fig. 7 presents a qualitative comparison between LDCM and state-of-the-art methods. Notably, LDCM produces sharper geometric structures and more accurate depth distributions, par- ticularly in regions with complex geometry or extreme sparsity. The predictions from LDCM exhibit significantly clearer boundaries and finer details, demonstrating the effectiveness of our coarse-to- fine framework and structural prior integration. In Fig. 8, we provide more visualization results for depth map and point map.
21

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Image
Result
Ground Truth
Figure 5: Example of two failure cases.
RGB
Sparse
OMNI-DC
PriorDA
PromptDA
LDCM
Ground Truth
Figure 6: Visualization comparion with state-of-the-ar methods.
I
NOISE ANALYSIS
Fig. presents an example with noisy input. When the sparse prior contains noise, the Poisson alignment strategy is adversely affected. However, the subsequent network effecctively mitigates this issue and produces a high-quality result.
LIMTATION AND FUTURE WORK
Although LDCM achieves superior performance, accurately reconstructing transparent objects and reflective surfaces remains challenging, illustrated by two failure cases in Fig. 5. This limitation
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Published as a conference paper at ICLR 2026
Sparse
SPNet
G2-MonoDepth
WorldMirror
LDOM
Ground Truth
Figure 7: Visualization comparion with state-of-the-ar methods.
RGB
Sparse
Depth Pred
Ground Truth
Point Pred
Figure 8: More visualization results for depth map and point map.
stems from the lack of large-scale datasets containing such materials, which are difficult to capture and annotate. In the future, plan to investigate synthetic data simulation to augment training and improve robustness on these challenging scenarios. Additionally, while monocular video recon- struction is promising application, achieving temporal consistency poses substantial challenges. Extending LDCM to process video sequences for consistent 3D geometry estimation over time an important direction for future exploration.
23

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Published as a conference paper at ICLR 2026
Input Image
Sparse Prior
Poisson Result
Depth
Figure 9: An example for noise input
K
STATEMENT ON THE USE OF LLMS
Large language models (LLMs) were used only for linguistic refinement, such as improving gram- mar and phrasing. They played role in shaping research concepts, designing experiments, or interpreting data. The authors authored all content, verified its accuracy originality, and assume full responsibility for the manuscript.
24

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Published as a conference paper at ICLR 2026
Quantitative comparison of depth completion with baseline methods on the KITTI dataset Geiger et al. (2012); Uhrig et al. (2017). Methods marked with i produce relative depth maps, where the metric depth is recovered by optimizing global scale and shift via least squares regression using the sparse depth prior. Methods marked with $ use scenario-specific configurations for indoor and outdoor scenes, respectively. The best and the second best results are highlighted.
Table 12:
methed
LiGr 64 Lines
Lidar 52 Lines
Lidar 16 Lines
RMSEL
MAFL
REL.]
6: 1
RR:11
RMSEL
MAR]
RFI.
6.-
Rk.j
RMSFF
MAR]
[FL.]
6.1
Rk-..
DesthPro
4.149
2.763
0.378
0.731
14,000
4.349
2.763
0.175
0.731
14,000
4.149
2.763
0.17%
0.753
13.750
UniDegth V1
3,335
2,010
0.318
0.93%
9.000
3.335
2.030
0.115
0.938
9.500
3.335
2.000
0.138
0.533
9.500
UniDepth V2
3.150
1.595
0,090
0.960
7,500
3.150
1.598
B.090
0.960
7.250
3.150
1.598
0.090
0.560
7.500
DepdhAnyttingVV
3,902
1.826
0.088
0.925
9.250
3.903
1.824
0.085
0.925
8,750
3.902
1.824
0.058
0.923
9,000
VOGTY
4.122
2.417
0.148
0.804
12.000
4.125
2.415
0.147
0.306
13:00
4.134
2.431
0.149
0.302
12,500
MoGe Vi-
3.822
2
0.159
0.869
11,500
3.299
1.90
0.151
0.890
10:250
2.77
1,730
0.113
0.15
8,500
MoGe V22
4.517
1.365
0:113
0.458
15. 1.250
4.517
3366
0.213
0.458
15:250
4.617
3.366
0.213
0.458
5.000
..99
0.378
0.004
0.986
4.000
1.501
0.454
0.23
0.984
4,000
2.187
0.652
0.036
0.981
4,250
OSINI DC
[.188
0.274
0.015
0.943
3.000
1.424
0.354
0.019
0.990
2,000
1.710
0.560
0.024
0.586
2.00
PRiDDA
1.776
0.561
0,629
0.985
3,000
1.912
0.615
0.244
0
3,000
2.124
0.773
0.041
0.979
4,750
1.547
0.066
3:00
0.44
0.050
WirldMiroee
3.540
0.793
4.075
11,250
2.543
0.134
13,000
MapAnsthing
0.322
15.750
12.856
0.331
15.750
Poo3R)
3,027
0.865
3.254
1.900
0.125
3.352
1.944
0.127
10.500
LDCM (Ouns)
1.340
0.816
0.993
1.750
1.416
0.333
0.018
0.991
1.000
1.413
0.393
0.000
0.990
1.000
method
Lalar-S-_ancs
Lidar-4-Lines
RMSEL
MAEE
RBLJ
R3.1
RMSEL
MAEE
REL.
RM-SE1
MAAE
REL
Rl...
DepthPro
1149
2.763
0.178
0,731
13,250
4,149
2,763
0.178
0.731
13,250
4.149
2,763
0.178
0.751
14.000
UniDepth V1
8,355
2,010
0.118
0.918
9.500
3:335
2,010
0.118
0.938
8.500
5.335
2,010
0.118
0.933
9.250
UniDepth
3.150
0.960
3.150
0.090
0.960
1.590
0.050
7,500
3,927
0.912
4.770
1.934
0.917
3.919
1,833
0.088
9.250
VOGTY
4.158
0.809
4.284
0.147
0,811
12:250
4.129
2.421
0.148
13,000
Modic Vi-
828
1 1.587
0.005
0.918
5,000
2.795
1.508
0.98
0.926
6,250
2.952
1,688
0.112
0.944
8. 8,500
MuGic V2
4.617
3.366
0.313
0.45%
15,000
4.617
3366
02133
0.458
150000
4.677
3,566
0.21
0.555
152250
2.658
0.877
0.046
0.972
4.500
3.547
1.572
B.77
0.930
7,500
18849
0.498
0.023
0.985
3.750
OMNI-DC
2.116
0.629
0.001
0.981
2,000
3:305
1.114
0.000
0.958
3,000
1,780
0.443
0.035
0.985
2:350
PAintDA
2.404
0.93
0.040
0.975
4.500
3.500
1.320
0.633
0.954
4,350
1.900
0.640
0.030
0.981
5000
SPNel
2.579
0.978
3.515
1.219
0.055
0,958
0.026
2.750
Prompel1
2,908
0.960
3.685
0.78
2.591
0.964
0.048
WodldMmnott
3.776
0.148
0.794
6.412
0.159
0.765
13.750
3.848
2.192
0.133
11.500
MapAmmthing
13.057
0.598
13.105
0.351
0.579
157750
12.926
6,684
0.344
15.750
Fow3Rt
3,448
0.128
0.854
10,500
3.648
1.961
0.123
0.361
10,000
3,491
2,045
0.132
11.000
LDCM (Ouns)
18778
0.494
0.825
0.987
1.000
2.592
0.767
0.031
0.978
1.000
1.565
0.373
0.019
0.990
1.000
method
5%
RMSEL
MAEL
RBC]
5,1 1
R3.1
RMSEL
MAE1
REL.
6,1
R3.
RMSE1
MAE]
REL.
6.1
Rk..
DepthPro
4.149
2.763
0.378
0,731
14,000
4.349
2.763
0.178
0.731
14,000
6.149
2.765
0.178
0.331
13.750
UniDeysh
3,335
0.118
0.98%
9.250
3.335
2,010
0.115
0.938
9.250
3.355
2,000
0.118
0.936
0.250
UniDeytt
3.150
1.598
0,000
0.960
7.750
3.150
1.598
0.090
0.960
7.500
3.150
3.598
0.050
7.000
A
3.924
1,855
0.088
0.922
9.250
3.940
0.089
0.922
9.500
1.866
0.050
9.500
VOGT!
4.136
2445
0,149
0.902
13,000
4,345
2.435
0.150
0.301
13.000
4.185
2.458
0.151
0.797
13.250
MoGe V1-
2,805
0.104
0.922
3,000
2.5.8
1.517
0.100
0.925
7,750
2.757
1.512
0.099
0.922
7,500
MoGe V2
4.517
5.366
0,213
0.458
15. 15,250
4,517
3.366
02133
0.458
15,250
4.617
3.366
0.213
0.158
15,250
2,035
0.533
0.61
0.985
3,750
2.244
0.085
0.980
4,000
2.900
1,010
0.051
0.966
5.250
OMNI-DC
1.951
0.516
0.088
0.985
3,000
2.124
0.589
0.011
0.980
3,000
2.677
0.640
0.042
0.969
3.500
PHoDA
2,099
0.980
5,000
2.310
0.036
0.975
2.524
0,850
0.042
3,000
SPNeet
1.897
0.513
0:027
0.986
2,000
2.03
0.577
0.29
0.564
2.000
2.554
0.773
0.097
0.77
2250
Prempel1A
3.262
1,070
0.665
0.961
6000
2.574
1111
0086
0,559
6,500
3-265
1.54
0.88
03566
6.750
Winddhirree
3.670
2.123
0.125
0.858
11:250
3.835
2.128
0.125
0.367
11:250
3,001
1.160
0.127
0.80
11.250
MapAnsthing
12,960
6.726
0.345
0.914
15.750
18.106
6.996
0.365
0.573
157700
13.354
7.336
0.388
0.550
15.750
PowdRR
3,472
2,020
0.131
0.850
11.250
3.472
2.016
0.150
0.553
11.000
3.544
2.047
0.132
0.550
11:250
LDCM (Oun)
L691
0.428
11921
0.589
1.000
1,521
0.468
R.022
0.988
1.000
2.168
0.630
0.027
0.983
1.000
method
SIFT
ORR
Averggg
RMSEL
MAEL
REL]
31 7
R3.1
RMSEL
MAEL
RELI
RK.1
RMSEL
MAE]
REL.
6,1
Rk...
DepthPro
4.149
2.763
0.178
0.731
12,000
4.149
2,763
0.178
0.731
11,350
4.169
2.763
0.178
0.231
13.432
UniDepth V1
3,335
2,010
0.118
0.918
5.750
3:335
2.010
0.118
0.38
6.250
3.355
2.010
0.188
0.938
8.636
UniDepth V2
3.150
1.595
0,050
0.960
3,250
3.150
1,598
0.090
0360
3.750
3.150
1.598
0.090
0.560
6,500
DeythAnything221
4.295
20000
0.102
0.585
5,000
4.299
2.116
0.105
0.586
9,000
4.007
1.890
0.092
0.916
9,091
VOGTY
4.516
2,999
0.199
0.591
14,000
4.473
2.933
0.204
0.682
13:500
4.219
2,518
0.158
0.781
12 22.909
MoGo Vi-
3.276
2.14
0.170
0.292
5,500
3:315
2.289
0.184
0.760
9,500
3,050
1.821
0.125
0.587
8. 3,566
MoGie V2
4.617
5 3366
0:215
0.458
15,250
4.617
1356
0.213
0.45%
15220
4617
1.566
0.21
0.555
15 151122
Co-Monolaphh
4.238
2,212
0.135
0.800
10.000
3.617
1,690
0.101
0.369
7,250
2,638
0.964
0.054
0.949
5.005
OMNEDC
3.630
0.099
0.834
3.443
0.097
2.302
0.042
3.045
PrinfAA
2.904
1.124
0.061
0.948
3.250
2.769
11118
006
0.949
2.250
2.364
0.861
0.044
0.71
4.159
SPNet
3.358
1.482
0,085
0
4.500
3.107
1.265
0,075
0.911
3,500
2.365
0.757
0.041
0.566
3,000
PrompeIAA
3,882
1990
0.120
0.587
7.250
3,509
2.027
0.125
0.872
8,500
3,060
1.251
0.067
0.966
6.545
WorldMimett
4.166
2.555
0.165
0.769
10:750
4.239
2.551
0.166
0.720
10.550
4.439
2.432
0.142
0.824
11818
MapAmnthing
13.101
7.111
0.374
0,561
15:750
13.133
7.816
0.372
0.577
15.750
12.974
6.784
0.350
0.588
15.750
Pow3Rt
3.981
2.666
0.197
0.735
11,500
3.973
2.694
0.206
0.719
12.250
3.515
2.096
0.141
0.332
10.750
LDUM (Ous))
2.579
0.910
0.643
8.96
1.000
2.478
0.846
0,042
0,963
1.000
1911
0.537
0.036
0.383
1.068
25

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Published as a conference paper at ICLR 2026
Table 13: Quantitative comparison of depth completion with baseline methods on the indoor scenes of the DIODE dataset Vasiljevic et al. (2019). Methods marked with produce relative depth maps, where the metric depth is recovered by optimizing global scale and shift via least squares regression using the sparse depth prior. Methods marked with & use scenario-specifie con- figurations for indoor and outdoor scenes, respectively. The best and the second best results are highlighted.
Mithool
10% Noise
5%6
3:6
RMSEL
MANEL
KBLJ
0.1 1
K3.1
RMSEL
MADI
REL]
0.77
RE.
RMSSEL
MAEL
REL.
DepthPon
0.657
0.568
14.
0:537
0.700
0.191
14.
0.857
0.700
0.191
5.000
UniDapsh
0.779
0.989
0.158
0.779
0.959
0.640
0.158
UniDagsh
0.611
0.365
0.681
13.500
0.678
0.165
0.681
13.
08811
0.678
0.165
13.500
Anylling
0.583
0.04
0.979
6.500
0.387
0.040
0.979
8.750
0.388
0.181
0.040
8.750
0.392
0.078
0.928
11.750
0.391
0.078
0.929
11,750
0
0.251
0.078
11:750
MoGe V1-
0.245
0.144
0.455
0
5.750
0.239
0.140
0.043
0.958
8,250
0.259
0.140
0.043
8.000
MoGe V2
1.064
0.235
0.435
16,000
1.064
0.235
0.433
16,000
0.938
0.235
16.000
Monooeptth
0.020
0.001
0.001
1,000
1,500
0.021
0.005
0.001
1.000
2.250
0.06
0.005
0.001
1.000
2.250
DC
0.066
0.009
0,002
0.999
4,000
0.022
0.032
00000
1.000
1.500
0.025
0.002
0.000
1,000
1.250
PrinDA
0.077
0.006
0.999
0.050
0094
0.51
0.034
5,000
SPNest
0.019
0.001
0.001
1.000
3.000
0.018
0.033
00001
1.000
1.500
0.21
0.003
0.001
1,000
1.500
DA
0.099
0.049
0.014
0.958
6.000
0.092
0.047
0013
0.997
6.000
0.955
0.947
0.013
0.995
6.000
WuldMMinee
0,523
0.195
0.062
0.958
10.250
0.350
0.231
0.000
0.946
10.500
0.376
0,220
0.066
0.945
10.500
ManAnnthing
0,535
0.129
0.030
0.977
8.500
0.508
0.133
0.027
0.984
8.250
0.552
0.156
0.031
0.992
8.500
Pow3R)
0.260
0.186
0.055
0.976
9.250
0.296
0.207
0.063
0.954
9,500
0.310
0.211
0.063
0.951
9,500
LDCM
0,023
11:001
1.000
0,025
0.003
0
0.004
0.001
2,500
Mathod
100
RMSEL
MAEL
KBL]
9, 1
RMSEL
MAEI
REL
0.77
REL
RMISEL
MAEE
REL
6.11
RK...
DepthPmo
0.832
0.702
0.195
0.668
14,500
0.837
0,700
0.193
0.668
14.250
0.857
0,202
0.193
0.668
13.750
DniDogth V1
0.939
0.340
0.158
0.779
14.000
0.999
0.840
0.158
0370
13.500
0.039
0.640
0.158
0.779
13.250
UniDopth V2
0.811
0.678
0.365
0.581
13.500
0.511
0.678
0:165
11881
13:50
0811
0.678
0.665
0.681
12,500
DeythAnndhhng
0.591
0.882
0.940
0.079
5.000
0.410
0.189
B101
0182
8,500
0.428
0.193
0.94]
0.776
7.500
VOKTT)
0.591
0.261
0.078
0.228
11.500
0.392
0.262
B075
01228
10.750
0.399
0.251
0.07%
0.531
9.750
MoGic V1"
0.239
0.140
0.043
0.85%
3.750
0.240
0.141
B104
0356
7,750
0244
0.341
0.043
0.599
6.250
MoGe V2
1,064
0.935
0.235
0.433
16,000
1.064
0.938
0.335
0.433
157750
1.064
0.938
0.255
0.433
15.750
MonoDe
0.045
0.009
0002
1.000
3,250
0.136
0.045
00011
0.997
0.752
0.550
0.179
0.622
12.750
OMNLDDC
0.036
0.003
0.001
1.000
0.088
0.016
0000
0.998
2.500
0.185
0.159
0.014
0.992
3,000
PrinDAA
0.057
0016
0.044
0.999
5.000
0081
0.022
0005
0.990
2.750
0112
0.099
0.000
0.996
1.500
SPNett
0.034
1.000
0.301
0.020
0.004
0.067
0.016
3.750
PrompeDA
0.105
0.957
0.365
0.02
0.991
0.246
0.115
0.051
5.250
WoldMMnnot
0.345
0.057
0.954
0.325
0.054
0.955
8,750
0.330
0.180
0.052
7,500
Map-Amtthing
0.635
0.045
0.974
1.349
0.135
0.379
13,000
1244
0.771
0.155
0.791
13.000
Pon3R1
0.521
0.21
0.061
0.956
9.750
0.357
0.210
0.000
0.958
9,250
0.349
0.215
0.062
0.953
8.750
LDCM (Ouns)
0,038
0
1.000
1.750
0.679
0.012
0
0.154
0.034
0.006
0.996
1.250
Metuad
Virtull
Lidar Lines
RMSEL
MAEL
REL.
6.
Rk.
RMSEL
MAE]
REL.
4.7
RMSE
MAE]
REL.]
6.1
Rk...
DapthPio
0,83
0.702
0.395
0.568
13.500
0.537
0.702
0.193
0.568
13,000
0.857
0,702
0.193
0.668
14:250
UniDeaah V1
0,939
0.340
0.158
0.779
12.250
0.039
0.840
0.155
13779
12.250
0.559
0.540
0.158
0.770
13.750
UniDepth V2
0.11
0.678
0.165
0.681
12,000
0.911
0.678
0.165
01681
11.750
0311
0.678
0.165
0.681
13.250
Anythinntiingg
0.565
0.022
0.061
0:343
B000
0.961
0.040
8:000
0.571
0.577
0.774
0.577
8.500
0.391
0.251
0.07%
0926
11 10000
MoGic V1
0.411
0.919
0.413
0155
0.913
5.250
0.200
0.140
0.043
0957
7.500
MuGi V2
1.064
0.958
0.235
0.435
1.064
0.235
0.433
15,750
0.938
0.35
16,000
G2 MonoDDpthi
0.894
0.670
0.241
0.554
12,750
0.995
0.755
0.261
0.513
14,50
0.081
0.020
0.008
0.999
4,000
OMNNDDC
0,553
0.223
0.833
0.570
5,000
0,492
0.330
0.119
0.500
5.750
0.056
0.007
0.002
0.999
1.509
PAinf1A
0.145
0.02
0.915
0.197
B199
3/00
SPkat
0.473
0.318
0.125
0.78%
7.750
0.552
0.375
0.140
0.746
7,500
0.071
0.011
0.000
0.590
2.500
PrompeDA
0.565
0.252
0.065
0,677
5,500
0.608
0.419
0.149
0.795
8.500
0.124
0.058
0.018
0.99
6:000
WorldMirrer
0.512
0.11
0.187
0.817
9.000
0.648
0.50
0.114
0.874
10.000
0.30
0.185
0.054
0.544
9.000
MapAngthing
1,105
0.670
0.157
0.812
11,250
1.082
0.578
01400
0.854
9.750
1.577
0.560
0.147
0.870
12.750
PowdRR
0.499
0.388
0.356
0.884
7,250
0.499
0.386
0.154
0.872
7,000
0.351
0.208
0.060
0.956
9.500
LDCM
11,027
0.973
0.208
0.164
10387
[0853
0.007
0.001
1.000
Method
Vinual Lidar
Virtual Lidar Lines
RMSEL
MAEL
REL.
0, 1
RMSEL
MAEI
REL.
RR.1
RMSEL
MAE]
REL.
6,1
RK...
DepthPro
0,837
0.702
0.195
0.568
14.250
0.837
0.702
0.193
0.668
14,250
0.857
0.702
0.193
0.668
14.114
UniDepth V1
0.939
0.360
0.158
0.779
13,750
0.939
0.810
0.158
0.779
13.750
0.939
0.640
0.158
0.775
13.523
UniDepth V2
0.811
0.678
0.165
0.681
13:250
0.311
0.678
0.165
0.681
13:250
0811
0.578
0.165
0.661
19.023
Depth AnythingV2
0.582
0.180
0.640
0.979
5.000
0.982
0.170
D.000
0.879
8.000
0.385
0.180
0.845
0.976
7.305
VOKIT)
0.261
0.07%
0.975
11.000
0.393
0.264
D070
0.928
11 11.000
0.425
0.204
0,066
0.920
10:773
Modic VI
0.540
0.140
0.043
0.957
7.500
0.341
0.142
B044
0.054
7.500
0.272
0.175
01844
0.550
7.385
MuGic V2
1,064
0.938
0.235
0.433
15.750
1.064
0.933
0.335
0.433
157750
1066
0.938
0.255
0.433
15841
G2 MannDai
0.121
0.033
0.008
0.997
5,000
0.211
0.086
B023
0.985
5,250
0.295
0.198
0.867
0.379
6.341
OMNI DC
0.080
0014
0.003
0.998
2,500
0.341
0.100
0.995
4,000
0.141
0.054
0.022
0.68
2.52
PrnDA
0.078
0.005
0.999
2.750
0.304
0.034
0.08
0.598
1.500
0.093
0.037
0.012
0.944
3.023
SPKat
0.084
0.017
0.004
0.908
3,250
0.14%
0.33
0009
0.996
3,000
0157
0.078
0.038
0.54
3.773
PAomjeDA
0.146
0.066
0.018
0.914
6,000
0.190
0.087
0024
0.988
5,500
02003
0.115
0.037
0.955
6.68
WorldMtmeet
0.327
0.182
0.665
0.956
8.750
0.331
0.188
0.056
0.951
9,000
0.385
0.243
0.084
0.4)
9.365
MapAngthing
1,072
0.605
0.149
0.670
13,000
1.099
0.600
0.125
0.890
13:00
0.909
0.658
0.101
0.390
[1 11.000
PowiRk
0.537
0.211
0.061
0.955
9.750
0.341
0.216
0003
0.951
9. 9,500
0.353
0.340
0.078
0.441
9,000
LDCM (Oona
0.067
0.011
0.602
8.999
1.000
0.104
0,023
0.08
0.998
1.000
0.064
6.025
0.068
0.993
1.545
26

---

Published as a conference paper at ICLR 2026
Table 14: Quantitative comparison of depth completion with baseline methods on the outdoor scenes of the DIODE dataset Vasiljevic et al. (2019). Methods marked with t produce relative depth maps, where the metric depth is recovered by optimizing global scale and shift via least squares regression using the sparse depth prior. Methods marked with & use scenario-speciffe con- figurations for indoor and outdoor scenes, respectively. The best and the second best results are highlighted.
Method
Vinual Lidar 64 Lines
Vinual Litar 32 Lines
Virtnal Lidar 16 Lines
RMSFL
MAFL
[FF.]]
6. 1
RE:
RMSFA
MAF]
RFI.]
4.7
Bk.j
RMSFF]
MAF]
RFI
6r1
Rk-..
DepthPm
9:539
7.635
0.40%
0.127
14:250
9.539
7.635
0.403
0.127
15:250
9.539
7.635
0.40
0.177
1. 15,000
UniDepth
5,782
0.189
0.561
12.000
57782
3.841
0.189
0.661
11,350
57783
3.841
0.189
0.661
11
UniDepth V2
11,145
8.936
0.515
0.526
15,750
11:145
8.936
0.515
0.526
15.750
11.145
3,936
0.515
0.526
15.750
DepthAnyhingVV
3.786
2.625
0.118
0.882
5.750
5.829
2.648
0.118
0.580
8,750
5.851
2.870
0.121
0.879
8,750
VOGTT
4.299
2.768
0:227
0.779
10/250
4.343
2.761
0.330
0.779
10.250
4.765
2.758
0.228
0.780
10:250
MoGe Vi-
10.329
8:351
0.396
0.803
14,500
9.455
1.366
0.354
0.688
14:00
9.034
6,809
0.317
0.685
12 12.500
MaGe V22
0.182
0.680
10:750
4.307
3.30
0.183
0.600
10. 10.500
3.352
0.183
0.600
1.938
0.489
0.089
0.975
4.000
2:375
0.670
0,53
0.967
4,500
2.719
0.882
0.009
0.900
5,000
OMNE DC
1,899
0.424
0,035
0.902
3,000
2.196
0.532
0.042
0,970
3,250
2.659
0.738
0.056
0.960
3,750
1.970
0.969
2.097
D.44
2.778
0.050
2.500
SPNet
E.809
0.419
0,002
0.978
3.750
2,100
0.518
0.039
0.972
2.250
2.535
0.715
0.054
0.962
2.500
PrompeiX
3.142
1.239
0.671
0.919
6.000
3.315
1.336
0.071
0.911
6,000
3,579
1,470
0.082
0.925
6,000
WorldMirmee
2. 2.105
0.147
0.836
7.750
4.224
2.268
0.151
0.832
8,250
4.214
2.214
0.147
0.355
8.000
MipAngthing
7.993
3.464
0.00%
0.622
11:750
8:875
4.020
0.799
0.633
12,550
9.705
5.717
0.318
0.560
40000
Pow
3.859
0.179
0.814
3,010
2.191
0.170
0.835
7,750
3,957
2.193
0.178
0.335
7,750
EDCM (Duna)
L.788
0.404
11.044
0.078
1.000)
2,010
0.476
0.029
0.914
1.000
2.280
0.663
[.036
0.967
1.250
Method
Vistual 8 LiLas
VistaalLista-44 Litaa-4-Lincs
10% Nose
RMSEL
MAEI
REL.]]
1
RL.1
RMSEL
MAE]
REL.
6. 1
RL.1
RMSEI
MAE]
REL.
6,1
RL...
DepthPro
9,539
0:403
0.177
9.539
0.403
0.177
15.000
9.539
7.635
0.403
14,500
UniDepth
5.782
5.782
0.189
0
11:000
5.782
3.841
0.189
12,500
UniDepth
11,145
0.515
0.526
11.165
0.155
0.526
15.500
11.145
5.936
0.515
Anpthnnnthnng221
5.911
0.875
5.750
6.421
0.138
57778
2.855
0.119
9.500
2.753
Modic Vi-
0.744
7.833
0.341
0.708
0.512
MaGi V2
4.897
0.182
0.580
4.507
0.182
0.680
9.250
3.552
0.122
11.250
3.611
1.467
0,099
0.969
5,250
5.910
3,230
0.204
0,698
10.750
0.76
0.206
0.016
0.992
2.250
OMINEDC
3,536
1.225
0
0.935
3.750
5.361
2.572
0.155
0.301
6,000
1.055
0.222
0.017
0.591
3.750
PAiDDA
2.744
1.019
0.059
0.949
2,000
4.068
1.731
0.105
0.877
2,000
1.957
0.759
0.041
0.971
5,000
SPNee
3.286
4.652
0.168
0.192
0.014
1000
WirldMirrer
1.525
7.350
MapAngthing
0.520
10:993
0.346
0.343
5.845
0.122
FowGRt
4.045
0.174
0,835
7,500
4.515
0.197
0,521
2.976
1.725
0.144
0.361
8,750
LDCM (Ouns)
2,679
0.823
0,848
0.958
1.000
3418
1.300
0.072
0.913
1.000
1.052
0.222
0.014
0.993
2.350
Method
5%
1%
RMSEL
MAEL
REL]
RMSEL
REL.
1
RM4SEI
MAE1
REL
RL...
DepthPro
9,539
7,635
0.403
0.177
14,500
9.539
7.635
0.403
0.171
14.500
9.599
3.635
0.803
0.171
13.500
UniDepth V1
5,782
3.861
0.189
0.561
12.250
5,782
5.841
0.189
0.661
12.250
5.962
3.641
0.189
0.661
11:250
UniDepth V2
11 1.145
6.936
0.515
0.576
15.000
11:145
8.936
0.515
0.526
15,000
11.145
3.936
0.515
0.526
14.250
Dopth Anythingg
891
2,687
0.119
0.681
9.750
5.396
2.656
0.10
0.381
9.250
5.924
2,680
0.119
0.881
8:350
VOGTT
4.732
2.756
0.228
0,781
11,000
4.732
2.759
0.229
0.781
11.000
4.754
2.758
0.229
0.781
10.000
MoGe Vi-
12,546
10.622
0.502
0.696
15,500
12.151
10.042
0.480
0.519
15.500
11:346
9.525
0.441
0.558
14.250
MaGe V2
53352
0.182
0.680
11,250
4,507
3,350
0.182
0
11,000
3.352
0.182
0.680
10:250
MonoD
E076
0.020
0.989
2.750
1.345
0.23
0
1.600
0.402
0.032
4,000
CMNI-DC
1.100
0:224
0.11
0.989
2.250
1.270
0.266
0000
0.987
2:250
1669
0.356
0.027
0988
2,250
PriDDA
1.785
0.655
0.037
0.974
5,000
1.505
0.645
8055
0.975
5.000
1,895
0.566
0.040
0.570
5,000
SPNed
1.029
0.017
0.950
1.187
0.020
0.988
1.250
0.349
0.027
1.250
PromptDA
2.941
0.070
0.957
2.981
0.070
0.938
1.235
0.073
0.335
6,000
WorldMimott
3,510
1,335
0.127
0,660
$.250
3,346
0.141
0.330
5,250
6.659
2.731
0.159
0.860
9.250
Map-Anything
5.868
2.033
0.116
0.855
$.750
6.099
2.226
0.125
0.358
9,250
3.996
2.152
0.150
0.333
8.000
PowERt
2.365
0.174
0.523
5.750
3.585
0.183
0.522
9,000
1.645
0.560
0.022
2,000
LDCM (Ouns)
1,209
0.255
0,016
0.989
3.750
1.348
0.285
0.015
0.987
2,500
1.645
0.360
0.022
0.981
2,000
Methoal
SIFT
ORB
Average
RMSEI
MALL
RBL]
5.1
K3.1
RMSUL
MAL1]
REL]
6,7
RS.
RMS1L
MAEU]
REL]
Rk...
DepthPon
0.177
9.530
7.635
0.177
14.500
7.635
0.401
14.636
UniDessh
3,782
0.389
0.661
5.782
B.080
0.661
5.782
3.841
0.150
11.795
UniDagst
11.145
0.515
0.526
15.250
11.145
0.515
0.526
15,000
11.145
5.936
0.515
15.250
A1
5.54
0.133
0.836
6.750
6.079
3.170
0.136
0.527
8.500
5.500
2.777
0.124
0.869
8.659
VGGT
5,102
0.256
0.739
11.000
5.296
0.267
0.728
10.750
4.855
2
0.237
MoGe V1-
11,377
0.472
0.535
15.250
11.684
0.485
0.518
15.500
10.576
8.340
0.606
14.250
MoGe V2
4,507
0.182
0.580
10.250
4.307
0.182
0.580
9,500
4.807
3.352
0.182
10.477
MonoDe
2.335
0.500
0.60
0.945
4.750
2.659
0.964
0.633
0.927
5,000
2.393
0.875
0.062
0.33
4.682
OMN-DC
2.201
0.00
0.366
2.605
0.824
B.099
0.951
3.500
2.322
0.726
0.049
3.341
PáinDA
2.220
0.06
0.964
2.587
0.979
0.054
0.952
3,500
2.310
0.85%
0.051
3.922
SPXat
1.083
0.088
0.970
2.323
0.678
0044
0.66
1.750
2.11
0.658
0.04
0.980
2.114
PrompeDA
3,705
0.063
0.915
6.000
4.159
1.911
0.102
0.582
6.000
1,561
0.067
0013
6.182
W
4.426
2.477
0.166
0.802
7.750
5.995
3.501
0209
0.696
11:250
4.464
2,517
0151
5.045
MinAAmthing
7.788
0.208
0.797
11.500
7.540
3,316
81183
0.521
9.750
7.675
3
0.219
0.731
11.318
Pow3R!
4,236
0.314
0.502
4.308
2.634
0213
0.792
8.750
3,682
2.068
0.169
7.568
LDCM rOuns)
1.984
0.491
0
0.974
1.250
2.334
0.683
0.044
0.965
1.350
1.69
0.529
0.031
0.970
1.568
27

---

Published as a conference paper at ICLR 2026
Table 15:
Quantitative comparison of depth completion with baseline methods on the iBims-1 1 dataset Koch et al. (2018), Methods marked with i produce relative depth maps, where the metric depth is recovered by optimizing global scale and shift via least squares regression using the sparse depth prior. Methods marked with & use scenario-speciff configurations for indoor and outdoor scenes, respectively, The best and the second best results are highlighted.
Method
10% Noist
1/6
RMSEL
MAEL
REL]
RMSEL
REL]
RMSEE]
MAE]
REL.
RL...
DepthPro
0.605
0.503
0.155
0.829
13.750
0.605
0.503
0.156
0.829
13.750
0.605
0.503
0.156
0.829
13.750
UniDepth V1
1.166
1082
0.370
0.236
16,000
1.165
1,082
0.370
0.236
16,000
1.155
1,082
0.510
0.236
16.000
UniDepth V2
0.646
0.321
0.100
0.915
12,500
0.445
0.321
0.100
0.935
12.500
0.466
0.521
0.100
0.935
12,500
AnpthAngghnng22
0.332
0.109
0.041
0.980
8.750
0:347
0.172
0.041
0.978
9.250
0.348
0,172
0.041
0.978
9.250
VOITT
0.310
0.128
0.046
0.965
10.750
0:337
0.176
0.047
0.964
10,500
0.387
0.176
0.047
0.964
10.750
Modic Vi-
0.234
0.117
0.004
0.985
7.000
0:331
0.110
D.31
0.985
7,000
0.251
0.110
0.031
0.985
7,000
MuGe V2
0.633
0.540
0.356
0.707
14,500
0.633
0.540
0.156
0.707
14,500
0.633
0,540
0.156
0.707
14.500
Go-Monolaphh
0.06
0.118
0.00
3:250
OMNI-DC
0,142
0.031
0.008
0.914
4.000
0.311
0.020
D005
0.996
1.000
0.124
0.023
0.006
0.995
1000
PrietMM
0,151
0,52
0.014
0,993
5,000
0,142
0.043
0011
0.993
5,000
0.166
0.044
0.012
0.991
5,000
SPNec
0.102
0.996
3.000
0.312
0.005
0.596
L.730
0.126
0.025
0.006
1.750
PromptDA
0.191
0.022
0.988
0.192
0
0.1%
0.079
0.022
6,000
WorldMimmet
0.285
0.062
0.976
0.298
0.077
0.978
0.305
0.160
0.043
3,500
MapAnsthing
0.086
0.917
13.000
0:921
0.080
0.925
13.000
0.25
0.122
0.882
13.000
Pow3Rt
0.272
0.041
0.977
5.500
0.292
0.043
0.972
0.310
0.161
0.043
9.250
LDCM rOun)
0.113
0.022
0,006
0.996
2,000
0.120
0.023
0.006
0.995
3,000
0.127
0.124
0.006
0.995
1.750
Mathol
RMSEL
MAEL
RBL1
6.1 1
R3.1
RMSEL
MAE]
REL
6.7
RE_
RMSEL
MAEI
REL
6.1
RK...
DesthPro
0.605
0.511
0.156
0.829
13.750
0.605
0.156
0.529
13,750
0.605
0.503
0.156
13.750
UniDest
1.166
0.370
0,236
16.000
1,365
1.082
0.370
0.236
16,000
1.166
3.082
0,570
0.236
16.000
UniDepth V2
0.446
0.321
0.100
0.935
12,500
0.445
0.100
0.955
12,000
0.466
0.321
0.100
0.935
Anylling
0.552
0.41
0.97%
9.250
0.381
0.179
0102
0.978
0.250
0.381
0.183
0.042
8.500
0.337
0.07
0.964
0.338
0.045
0.961
10.250
0.341
0.183
0.049
0.500
9.500
MoGe Vi-
0231
0.110
0.011
0.985
7,000
0:332
0.111
0033
0.985
6,500
0.36
0.112
0,00
0984
4.000
MuGk
0.633
0.156
0.707
14,500
0.633
0.540
0.166
0.707
14.000
0.63
0.540
0.16
0.707
5.0
0.160
0.040
0.010
0.902
0:232
0.073
0019
0,986
5,000
0.357
0.17%
0.053
0,50
10,000
OMNN-DC
0.148
00000
0:06
0.993
2,000
0.188
0.050
0013
0.989
2,500
0.265
0.096
0.025
0.979
4,250
PiinDA
0.013
0.992
0015
0.991
0.211
0.030
1.750
SPNeej
0.156
0.034
0.088
0.943
2,500
0:211
0.055
0014
0.968
3,500
0.270
0.082
0.023
0.911
3.750
PrempelA
0.205
0.084
0.025
0.988
6.000
0:237
0.101
0.27
0.985
6,000
0.34
0.153
0.040
0.755
6.250
WirldMirree
0.336
0 1.000
0.042
0.971
8.700
0:342
0.158
0.443
0.921
9.250
0847
0.172
0.044
0.968
8,550
MapAngthing
0:923
0.083
0.024
13,000
0.992
0.40
0123
0.803
19. 13.500
1.077
0.454
0.125
0.900
5.0
PowdRR
0,332
0.042
0.972
9.000
0.343
0.156
0.043
0.972
9.000
0.346
0.167
0.042
0.969
7.500
LDCM (Ouns)
0.142
0.029
0.807
0.994
1.000
0.169
0.841
0.011
0.991
1.000
0.202
0.061
0.015
0.988
1.000
Method
SIFT
ORB
Virtual Lidar 32 Lines
RMSEL
MAEL
REL]
8. 1
RL.
RMSEL
MAE]
REL]
4, 7
RL.
RMSEI
MAE]
REL.
6,1
RK...
DepthPro
0.156
0.505
0.156
0.829
13750
0.608
0.505
0.156
13:750
UniDepth
0.236
1.105
0.370
1.082
16,000
0.100
Anpthingg
0.531
0.912
0.354
0082
0.963
0.973
0.040
0.256
0.075
0.98%
0.399
0.270
0077
0.990
8.750
0.557
0.17%
0.048
10:250
Modic VI
0.257
0.46
0.967
4.750
0.270
0053
0.957
4,500
0.251
0.110
0.051
MuGi V2
0.633
0.156
0.107
0.633
0.540
0.156
0.707
14,500
0.633
0.540
0.156
14.500
MonoDaptii
0.338
0.197
0,065
0.923
9,000
0.375
0.230
0.076
0.903
3,000
0.193
0.057
0.015
0.590
3,000
OMNNLDC
0.250
0.119
0.036
0.960
4.750
0.313
0.156
0.044
0.925
6.250
0.160
0.116
0.010
0.992
1.009
PrioDDA
0.180
0.989
2.000
0:11
0032
0.201
0.021
SPKat
0.224
0.096
0.008
0.081
3,000
0.264
0.120
B041
0.061
3,250
0.13
0.940
0.000
0.993
1.500
PromneDA
0.517
0.183
0,063
0.94%
6.500
0.394
0.244
B1072
0.003
8.250
0.209
0.086
0.024
0.986
5.550
WuldMiinee
0.582
0.236
0.066
0.940
5.750
0.567
0.380
0.103
0.397
12,000
0.356
0.161
0.841
0.972
8,000
MapAmnthing
0.977
0.411
0.115
0
13.500
1.002
0.427
0.122
0.596
13,500
0.973
0.565
0.09%
0.912
13:250
Pow3R:
0.594
0.254
0,069
0.934
10.250
0.411
0.274
0.075
0.935
9,000
0.356
0.170
0.044
0.569
8.750
LDCM (Ous))
0.170
0.853
0.015
0.991
1.000
0.186
0J867
0.020
0.989
1.000
0.196
0.076
0.018
0.983
4,500
Mithoal
Vituul-L.äar-10 Li-
Vasual-Laaae
Avcruge
RMSEL
MAE]
RBL]
5.1
R3.1
RMSC
MAEI
6.77
RK.
RMSE4
MARI
REL4
6,1
Rk...
DepthPm
0.905
0.156
0.679
13,750
0.505
0.501
0.156
0.820
13.550
0.605
0.503
0.156
0.820
13.70
UniDoyth
1.166
1.022
0.370
0.236
16.000
1.166
1.003
0.370
0.236
16.000
1.002
0.370
0.336
16.000
0.46
0.300
0.935
13.000
0.445
0.321
0.100
0.935
12.000
0.445
0.521
0.100
0.955
11 11952
DoydiAnyylhin
0.333
0.040
0.97%
6.250
0.336
0.166
0,040
0.979
7.750
0.349
0.179
0.043
0.755
8.205
VGGTT
0.338
0.045
0.963
10.750
0.339
0.150
0.045
0.962
10.500
0.44
0.194
0.053
10.318
MoGe V1:
0.222
0.022
0.985
0.233
0052
0.981
0.258
0.120
0.035
6,045
MaGe
0.633
0.156
0.707
14,500
0.533
0.540
0.156
0.707
14,500
0.633
0.540
0.156
0.707
14,500
MonolDppti
0.216
0,015
0.987
0.275
0.108
0029
0.978
0.277
0.964
0.028
5,409
OMNN-DDC
0.176
0066
0.012
0.900
2,500
0.225
0.075
0021
0.964
3,000
0.192
0.063
0.018
0.963
2.932
PrlorD
0.169
0,015
0.991
3,000
0.187
0.070
0.019
0.990
2,000
0.178
0.065
0.018
0.590
3.477
SPNest
0.19%
0.013
0.989
0.341
0.082
B021
0.984
3.750
0.189
0.019
0.016
0.997
2,659
PromppDA
0.217
0.025
0.988
57750
0.242
0.109
00000
0.984
0249
0.116
0.033
6,091
WodddMinoot
0.337
0.042
0.971
8.750
0.342
0.168
0.43
0,970
0.352
0.192
0.051
9.205
MinAmmthing
0.972
0.113
0.901
13.500
0.975
0.410
0.117
0,910
13.500
0.668
0.574
0.104
13:295
Fow3Rt
0.542
0.043
0.971
10.000
0.345
0.44
0,971
10,000
0.338
0.183
0.049
LDCM (Oun)
0.165
0,039
0,010
8.992
1.000
0.181
00013
0.901
1.000
0.161
0.044
0.012
0.991
1,689
28

---

Published as a conference paper at ICLR 2026
Table 16: Quantitative comparison of depth completion with baseline methods on the indoor scenes of the ETH3D dataset Schops et al. (2017). Methods marked with i produce relative depth maps, where the metric depth is recovered by optimizing global scale and shift via least squares regression using the sparse depth prior. Methods marked with $ use scenario-specific configurations for indoor and outdoor scenes, respectively. The best and the second best results are highlighted.
Mithool
10% Noise
5%
3/b
RMSEL
MANE
RBLI
9:1
RX.1
RMSEI
MAEE
KEL4
0177
RE-1
RMSEL
MAEE
REL.
611
Rk...
DepthPPm
0.811
0.670
0.192
0.205
14.250
0.331
0.670
0.193
0.705
14.500
0.831
0.670
0.192
0.705
UniDogth
3.549
0.695
0.071
16.000
2.549
2.390
0.695
0.071
16.000
2.549
2.530
0.605
0.071
16:000
UniDopsh
0.660
0.369
0.799
13.250
0.660
0.563
0.169
0.799
18:150
0.660
0.562
0.160
0.790
13
0.588
0.203
0.087
0.989
10.000
1.017
0.230
0.388
0.988
10. 500
0.220
0.058
0.985
750
VGKIT)
0.391
0.099
0.966
10.250
0.385
0.201
00000
0.959
11.000
0.386
0.202
0.050
MoGa Vi:
0214
0.955
0.211
0.114
0025
0.995
0.204
0.114
0.028
7,000
MaGe
0.542
0.17
0.784
12.750
0.542
0.419
0.117
0.784
12:750
0.542
0.419
0.117
12.750
0.055
0.011
0.002
1.000
2,000
0.062
0.012
B002
1.000
2.750
0.076
0.014
0.003
1.000
3,000
OMNEDC
0.116
0.003
0.999
0.052
01001
1.200
0.03
0.008
0.002
1.250
PAiDDA
0.110
0.008
0.959
4,500
0.090
0.036
0.066
0.999
5.000
0.093
0.027
0.006
0.990
4.750
SPNee]
0:076
0.010
0100
1,000
1.750
0.080
0.010
0100
1,000
2.250
0.101
0.013
0.000
1.000
2.500
Prompel1A
0.173
0.076
0.015
0.94h
6.0.0
0.148
0.057
0017
0.596
6.000
0.159
0.071
0.019
0.995
61000
WodldMiroet
0.328
0.197
0.050
0.984
9.500
0,280
0.154
0.038
0.992
8,000
0.261
0.141
0.035
0.991
3,000
MapAnythin
1,076
0.272
0.055
0.951
12:750
1.039
0.218
0.045
0.962
12,250
1.083
0.271
0.051
0.958
12.750
PonCRt
0,325
0.192
0,044
0,980
8,750
0.309
0.180
0.043
0,975
9,500
0.285
0.159
0.038
0.980
9.250
LDCM (Ouns)
0,066
0,009
0
1,000
1.250
0.851
0.009
0
1,800
1,750
0.058
0.009
0.002
1,000
1.350
Method
115
500
100
RMSEL
MAEL
6: 1
RE:1
RMSEL
MAR]
KEL.
6. 7
Hk.1
RMSFF
MAR]
REI.
6.1
Rk...
DepthPro
0.31
0.670
0.192
0.205
14,500
0.851
0.670
0.192
0,705
14,500
0.331
0.870
0.192
0.705
(62250
UniDepth V1
2,549
2.390
0.695
0.071
16,000
2,589
2.330
0.695
0.021
16:00
2.569
2.530
0.695
0.021
16.000
UniDepth V2
0.560
0.562
0.369
0.39
13.250
0.660
0.582
1
0759
13:50
0.600
0.582
0.109
0.799
13.000
DepdiAnnytinnV2
1.06
0.221
0,038
0.887
11.000
1.015
0.220
0
0387
11:000
10005
0.222
0.088
0597
10:250
VOGTT
0.387
0.203
0.000
0.959
10,500
0.389
0.200
01200
0302
10.500
0.398
0.198
0.048
0965
10:250
MoGa V1
0,205
0.114
0.028
0.994
7,000
0.206
0.114
0,225
03955
6,000
0.114
0.118
0.029
0.595
3.750
MoGic V2
0,542
0.419
0.117
0.784
12.750
0.542
0.419
0.117
0.784
12,250
0.542
0.419
0.137
0.794
12.250
GD-MannDapht
0.005
0.909
0:315
0012
0.443
OMNN-DC
0.699
0012
0.002
0.999
3,250
0.153
00031
B008
0.998
2.500
0.309
0.089
0.017
0.592
4.750
PA
0,103
0.029
0.065
0.999
3,500
0.133
0.033
0.007
0.999
2,500
0.222
0.054
0.012
0.997
2:350
SPNel
0.145
0.999
3,000
0:223
0005
0.323
0.085
0.015
4.250
PrempeDA
1888
0.078
0.019
0.908
6,000
0.768
0.106
0025
0,990
7.000
0.396
0.154
0.033
0.987
7.500
Winddhirree
0.341
0.225
0.01
0.992
5.000
0.334
0.121
B029
0.992
7,500
0.46
0.122
0.831
0.991
5,350
MapAnsthing
1,162
0.333
0.068
0.945
12.750
1.390
0.549
0144
0.873
13:250
1676
0,702
0.149
0.376
13,750
BoodRR
0.279
0.146
0.034
0.984
9.250
0.287
0.145
0034
0.985
9.250
0.298
0.150
0.035
0.988
7.250
LDCM (Ous)
0.074
0.012
01,002
0.999
1.000
0.306
0.021
0.004
0.999
1.000
0.157
0.041
0.008
0.94S
1.000
Method
SIFT
ORR
Vintesi-
adar- 50.1 Inees
RMSEL
MAEL
REL]
0,
RE.1
RMSEL
MAEL
RELI
7
RR.1
RMSEL
MAE]
REL.
0.1
Rk...
DepthPro
0.831
0.620
0.192
0.205
14,250
0,331
0.670
0.192
0,705
14,500
0.311
0.870
0.
0.705
14.500
UniDepth
2,549
2.390
0.695
0.071
16:000
2.549
2.330
0.695
0.21
16000
2.569
2.530
0,605
0.071
16.000
UniDepth
0.660
0.563
0.169
0.799
13.500
0.560
0.560
0.100
0.79
13.500
0.600
0.562
0.160
0.790
132200
An
0.436
0.179
0,040
0.990
6,500
0.395
0.183
0702
03990
5,000
0.915
0.206
0.037
0.989
10.750
VGGTT
0.466
0.242
0,065
0.937
10,000
0.481
0.277
0084
0S11
10.500
0.387
0.209
0.053
0.553
10.750
MoGo Vi-
0.228
0.141
0,640
0.975
4,500
0.250
0.164
0005
0.565
4.350
0.205
0.115
0.029
0.991
7,000
MoCie V2
0.542
0.49
0.117
0.784
12,500
0.542
0.419
0.117
0.784
12,550
0.542
0.419
0.117
0.764
12,500
0.435
0.076
0.910
0.440
B.07
0.914
9,250
0.153
0.009
4,750
OMNI-DC
0.248
0.114
0.005
0
5,000
0.284
0.144
0.45
0.954
5.00 5,000
0.107
0.017
0.00
0.990
1.500
PHoDA
0:189
0.084
0.028
0.984
2.250
0.227
0.158
00033
0,980
2.250
0.111
0,030
0.006
0.999
3,000
SPNee
0:308
0.055
0.974
0.00M
0.018
WirldMirrrr
0.239
10:250
0.53
0.122
8/00
MapAngthing
1.427
0.689
1.353
0114
1.350
0.106
100
PooRR
0.045
0.975
B0S5
0.961
0.283
0.036
9.250
LDCM (Ouns)
6.127
0,012
0.995
1,000
0.139
RL156
00016
0.996
1.000
0.087
0.014
0.003
0.999
1.000
Method
Vinusl-I
Virtesl-1
alor-8-1
Average
RMSEL
REL.
RMSEL
REL.
RMSEL
MAE]
REL
RL...
DepthPro
0,831
0.620
0.192
0.705
14.500
0.331
0.670
0.192
0.705
14.500
0.351
0.670
0.192
0.705
14.565
UniDepth V1
2,549
2.330
0.685
0.071
16,000
2.549
2.330
0.695
0.021
16.000
2.569
2.530
0.695
0.071
16.000
UniDegth V2
0.660
0.562
0.169
0.799
13.250
0.660
0.562
0.109
0.799
132250
0.690
0.562
0.169
0.799
13:273
Depth Amythingg
0.934
0.306
0.077
0.989
10.750
0.589
0.201
0.077
0.985
10.500
0857
0.208
0.0%
0.988
9.727
VOKTT
0.587
0.207
0.053
0.954
10.750
0.393
0.118
0.066
0.949
16.550
0.396
0.214
0.056
0.91
10.523
Modic VI
0.206
0.117
0.009
0.913
6.700
0.209
0.121
0.030
0.990
5.250
0211
0.123
0.032
0.900
6,000
MuGi V2
0.542
0.419
0.117
0.734
12.250
0.542
0.419
0.17
0.784
12.500
0.542
0.419
0.177
0.784
12.545
Dapui
0,302
0.060
0,012
0.997
4.750
0.315
0.126
0.077
0.590
6,500
0.229
0,992
0.025
0.900
5.501
OMNI
0.139
0.065
0.948
2,500
0.225
0013
0.995
0.162
0.948
0.012
2.932
PrinDA
0.129
0.039
0.008
0.959
2,500
0.165
0013
0.597
2.000
0.143
0.549
0.012
8.996
3.336
SPKie
0.202
0.44
0.007
0.958
1,500
0:785
0.044
0019
0.994
4.900
0.19
0.052
0011
0.994
3:395
PA
0.233
0.090
0.001
0.994
6.350
0:389
0.130
0.28
0.990
5750
0.20
0.115
0.03
0.984
6.636
WoldMMnne!
0.234
0.122
0.600
0.912
8.000
0.342
0.131
0.02
0.988
7.250
0.279
0.157
0.830
0.984
7.705
MansAnttinn
1.523
0.520
0.333
0.800
13.250
1.325
0.487
0115
0.908
13,000
1.289
0.443
0.101
0.915
13.000
Pow3R)
0.291
0.148
0.034
0.982
9.250
0.299
0.156
0.036
0.978
8.750
0.300
0.165
0.039
0.978
8.545
LDCM (Ouns)
0.136
1,000
1.114
29

---

Published as a conference paper at ICLR 2026
Table 17: Quantitative comparison of depth completion with baseline methods on the outdoor scenes of the ETH3D dataset Schops et al. (2017). Methods marked with i produce relative depth maps, where the metric depth is recovered by optimizing global scale and shift via least squares regression using the sparse depth prior. Methods marked with & use scenario-specific configurations for indoor and outdoor scenes, respectively. The best and the second best results are highlighted.
Method
Vintul---ata-- 32-Lines
Virteai-Lidar- 16-1Lines
RMSEL
MAEL
REL.
RMSEL
MAE]
REL
RMSEI
MAE]
REL.]
RL..
DepthPro
5.567
4.451
0,411
0,248
15,500
5.567
4.454
0.411
0.248
15.500
5.567
4.554
0.411
0.248
15,500
UniDepth V1
4.414
4.009
0:463
0.160
15.500
4,414
4.008
0.463
0.160
15.500
4.414
4,009
0.463
0.160
15,500
UniDepth V2
2,500
1.775
0.230
0.652
13.500
2,600
1,775
0.250
0.652
13.500
2.600
1,775
0.230
0.653
13:250
Depeh Angthinngiingg2
3.445
0.688
0.668
0.972
10,000
3:361
0.641
0.057
0.923
10.000
57577
0.673
0.059
0.973
10,000
VOKTT
0.650
0.381
0.068
0.957
9.000
0.651
0.363
0.066
0.957
9.000
0.61
0.384
0.056
0.957
8.500
Modic Vi-
2.554
1.100
0.343
0.876
12.500
2.561
0.801
0.124
0.890
11:250
5.301
0,752
0.111
0.921
11.500
MaGe V2
1,152
0.319
0.311
0.833
11:250
1.152
0.819
0.111
0.893
11:250
11552
0.819
0.111
0.893
11 112250
0.508
0.068
0.011
0.996
4.500
0.442
0.111
0.017
0.992
4.750
0.610
0.175
0.027
0.985
4.750
OMNIDCC
2.250
PrinDAA
0.267
0.083
0.010
0.916
4,000
0.291
0.830
B001
0.995
3,500
0.354
0.111
0.014
0.995
2.500
SPRiet
0,289
0.42
0.05
0.998
2,750
0.474
0.070
0.008
0.996
3,500
0.671
0.125
0.015
0.991
5100
PrompelA
0.915
0.355
0.044
0.964
7.250
0.972
0.365
0.045
0.961
7,750
1000
0.371
0.041
0.970
7,500
WorldMiree
0,584
0.327
0.048
0.963
6.700
0.631
0.353
0.000
0.958
7,250
0.647
0,362
0.053
0.955
7,750
ManAnsthing
27781
0.994
0.309
0.877
11.750
2.304
1.092
0.126
0.885
13,000
2,814
1.191
0.148
0.865
12.750
PondR)
0.637
0.361
0.053
0.958
8,000
0.636
0.344
0,050
0.964
6,750
0.634
0,337
0.050
0.962
6.500
LDCM (Ouna)
0.304
0.033
0.804
0.998
1.000
0.346
0.042
0,005
0.998
0.294
0.059
0.007
1,000
Method
Virtual LiGar Lites
Vistaal Lidar Lines
Nosse
RMSEL
MAEL
RELJ
0, 1
RE.
RMSEL
MAEI
REL]
7
RL.1
RMSSEL
MAE]
REL.
6,
RK...
DepthPmo
3.567
4.454
0:411
0.248
15,500
3.567
4.554
0.411
0.248
15,500
5.367
4.454
0.411
0.248
15,500
UniDegth V
4.464
40009
0:465
0.160
15,500
4.414
4.009
0:463
0.160
15:250
4.414
4.039
0.453
0.160
15. 15,500
UniDogeh V2
3,400
1.775
0:330
0.652
19:250
2.400
1.775
0.250
0.652
13:350
2,000
1.778
0.230
0.652
13.750
Anything
3.592
0.069
0.971
9.250
3.319
B1063
0.963
3,052
0.620
0.057
10.000
VGGTT
0.670
0.058
0.953
7,000
0.742
0.074
4,500
0.661
0.387
0.054
0.965
7,000
MoGe V1-
3.995
0.941
11.500
4.979
0.097
0.948
5,000
2.184
1.702
0.153
12.500
MoGe V2
1.152
0,111
0,593
11.250
1.152
0.819
0.111
0.393
8.750
1.152
0.819
0.111
0.822
11.500
1.005
0.419
0.057
0.954
7,000
1.362
1.177
0.171
0.774
11.750
0.128
0.031
0.004
0.990
3,250
OMNN-DC
0.594
0.221
0,005
0.983
3,000
1.282
0.751
0.995
0.888
8,500
0.177
0.028
0.00
0.990
3,000
PAinDA
0.023
0.989
1.071
0,900
0.280
0.012
5,000
SPNeet
.1004
0.327
0.045
0.970
5.250
2.088
0.888
0.134
0.856
11.000
0110
0.019
0.00
1.000
10000
PrompelA
1.511
0.49
0.067
0.952
5,500
1.372
4.799
0.92
0.902
7,500
0912
0.423
0.09
0.61
$.250
WirldMirree
0.715
0.405
0.067
0.941
0.732
0.436
0.7)
0.919
4,000
0.694
0.404
0.866
0.771
7,250
MipAnything
2.917
0.345
0.867
12.750
3.086
1.401
0.153
0.365
12.000
2.189
0.653
0.079
0.921
11
Pow3R)
0.645
0.353
0,050
0.962
5.000
0.712
0.390
0.063
0.933
2.500
0.658
0.415
0.056
0.960
8,000
LDCM
11.012
0.994
0.551
0.129
0.002
1,750
Mathod
5%
3%5
15
RMSEL
MAEL
RBL
0, 1
RE.
RMSEL
MAE1
REL
4.77
RE.
RMISEL
MAEL
REL.
6.11
RK...
DesthPro
5.567
4.454
0.411
0.24%
15.500
5.567
4.454
0.411
0.248
15. 15,500
5.567
4.454
0.411
0.245
15,500
UniDeath V1
4.414
4.009
0.465
0.160
15.500
4.414
4.009
B.463
0310
15.500
4.414
4,009
0.863
0.160
15.500
UniDggth V3
3,600
1.725
0:330
0.652
13:350
2.600
1.725
0-330
1852
131500
2,600
1.775
0220
0.633
137700
Anylhinng
3.702
0.62
0.069
0.071
10:250
3.745
0,673
1000
n8211
10:250
3.760
0.668
0.900
057)
10,000
VOKIT)
0.649
0.383
0.056
0:959
7.750
0.649
0.382
11056
0.509
7,250
0.650
0.582
0.05
0559
8.250
MoGic VL:
2,057
1.574
0.177
0.538
12.500
2.151
1.342
0:157
0.556
12,500
2.199
0.540
0.134
09884
12,500
MoGi V2
L.152
0.119
0.111
0.893
11.50U
1.152
0.819
0.111
0.593
11.500
1.152
0.839
0.111
0.93
11,500
0,156
0.036
0.005
0.999
3,250
0:302
0.044
0.005
0.998
3.750
0.367
0.079
0.012
0.995
4,500
OMNE-DC
0.909
0.190
0003
0.998
0.041
0.06
21000
PrinfAA
0.342
0.079
0.009
0.907
5,000
0.353
0.800
0009
0.997
5,000
0.251
0.086
0.010
0.996
4000
SPNed
0.145
0.022
0:003
0.959
1,500
0.182
0.028
0000
0.999
L7550
0.383
0.052
0.006
0.997
3:250
PromptDA
0.817
0.307
0/08
0.966
0.337
0.039
0.966
7,000
0.956
0.544
0.045
0.966
7:000
WorldMireet
0,773
0.415
0.665
0.962
5.000
0.761
0.418
0.056
0.951
8,750
0.699
0.386
0.053
0.951
8,750
MapAngthing
2.116
0.591
0.667
0.914
11.000
2.34%
0.661
0.074
0.927
11.000
2.451
0.817
0.094
0.911
11:250
PondRR)
0.644
0.391
0.056
0.955
5.000
0.650
0.383
0.054
0.958
7.750
0.634
0,546
0.051
0.963
7,000
LDUM (Ouns)
0.146
0,003
0.999
3,000
0.168
0,003
0.999
1.250
6.037
0.004
1.000
Method
Averuse
RMSEL
MAEL
REL.
4, 1
R1.1
RMSEL
MAEI
REL.
7
RR.1
RMSEL
MAE]
REL.
6,1
RL...
DepthPro
3.567
4.454
0.411
0.248
15,500
3.567
4.454
0.411
0.248
15,500
5.567
4.554
0.411
0.258
15,500
UniDepth V1
4.414
4.009
0.465
0.160
15.250
4.414
4.009
0.463
0.160
15,500
4.414
4.009
0.963
0.160
15.455
UniDepth V2
2,500
1.755
0:230
0.552
13:250
2,600
17755
0.250
0.652
13.500
2.600
1.775
0.210
0.652
13,500
Depth AnythingV2
2.613
0.597
0.060
0.964
$.250
2.415
0.600
D002
0.954
7,500
3.325
0.640
0.059
0.989
9.227
VOIITy
0.722
0486
0.060
0.913
8,500
0.525
0.583
0.101
0.897
7.750
0.684
0.419
0.864
0.96
7
Modic Vi-
4.449
0.572
0.106
0.946
10.500
3.109
0.622
0.116
0.394
11:250
3
0.976
0.131
0.895
11 11.500
MuGic V2
1,152
0.119
6.111
0.893
11,500
1.152
0.810
0.113
0.303
10,000
1.152
0.619
0.111
0.393
11 110023
G2 Mepph
0.924
0.396
0,677
0.918
5,000
0.585
0.401
0.777
0.920
6,250
0.625
0.251
0.042
0.957
5.614
OMNI DC
0.487
0.026
0.978
0.594
0.033
0.968
3,250
0.415
0.151
0.019
2.932
PrinDA
0.421
0.006
0.980
1.000
0.510
0.218
0033
0.975
2.000
0.404
0.161
0.022
0.833
3.750
SPNest
0.826
0.034
0.974
4.70
0.761
B05T
0.971
1.750
0.639
0.185
0.027
0.976
3.955
PromppDA
1,076
0.062
0.938
7.750
1.184
0.583
0.76
0.05
7.750
0.466
0.053
7.568
WodddMinott
0.730
0.072
0.920
1.491
0.123
0.536
11.750
0.769
0.446
0.063
7.527
MinAmmthing
2.796
0.143
0.969
2.617
0.124
0.381
12.750
2.615
0.979
0.115
12005
Fow3Rt
0.685
0.065
0.933
0.727
0.80
0.906
0.660
0.381
0.057
0,545
LDCM (Oun)
0,316
0.089
0.012
8.995
1.000
0.333
01101
00013
0.902
1.000
0.274
0.07
0.009
0.996
L.88
30

---

Published as a conference paper at ICLR 2026
Table 18: Quantitative comparison of point map estimation with baseline methods on the KITTI dataset Geiger et al. (2012); Uhrig et al. (2017). Methods marked with use scenario- specific configurations for indoor and outdoor scenes, respectively. The best and the second-best results are highlighted.
Method
Lihrest-Lins
Libe-tS-Lims
Lite--6oLins
1
411
RE.1
RMSE
6
RL_
RMSE"
66
BL.
UniDsppi
2,207
3,540
0.120
0.954
1.750
2.207
3.540
0.120
0.954
7.250
2.207
3.540
0.120
0.054
7.250
Unileym V22
1.815
3,540
0.550
01981
7.000
1.313
3.500
0.095
0961
7000
1813
3.540
0.156
0.961
7000
MoCe V2
3.556
4,899
0.208
0.454
9,000
3.536
4.859
0.208
0.444
9.000
3.536
4,899
0.208
0.484
9.000
GB Depo
1:234
2.185
0.779
0998
4.00
1.369
2.366
0.990
0983
5750
1.385
3,665
0.885
0.99
4000
OMNI DC
1.154
0192
2.000
1963
0998
2000
1.278
2.331
0.078
0.94
2000
PrieDA
1.333
2.255
0.000
0.92
5,000
1.394
2,409
0333
0.979
3,000
1.455
2.607
0.055
0.974
4.250
SPNet:
1.210
2.133
0177
01966
3,000
1.253
23332
0.00
0994
3.00
1.34]
2,597
0.033
0.900
3120)
PrompiDA
1.537
2.26
0.833
0.72
5,000
1.599
2.000
0.065
0.970
6.000
1.639
3.148
0.096
0.963
6.000
LDCM (ouns)
0855
1.686
0.88
0.993
1.008
0.881
1.812
0.051
8.991
1.000
0.934
2,817
0J053
0.989
1.000
1.
REL"
REL.
RMSE
RELF
Unileept
2.207
7.750
Unilkpph
0.61
6.900
3.440
08961
2.000
0.166
0.961
6.250
0.84
0.206
0.484
9.000
MonoDeppit
0.999
4.250
7.000
0.983
3.500
OMNI-DC
1.400
2.903
0083
01988
3.000
1.521
3.50
0009
0993
3.500
1.274
0.79
0.883
2750
0.966
4.750
0.945
0.978
3,000
SPNett
1.455
2.877
0.086
0.77
3,000
1.575
3.946
0.097
0.55
4.250
1.256
2.309
0.078
0.985
2.250
PrompiDA
1.744
3.413
0.096
0.956
0.250
2.187
4.467
0.118
0.922
7.000
1,600
3.133
0.094
0.960
0.250
LDCM (ours)
1.022
2.301
0.854
0.987
1.000
1.309
3.212
0.002
0.974
1.000
0.917
1.553
0.083
0.590
1.000
RMSB
RELF
REL7
RMISE"
REL/
Unilppt V1
2.707
3.540
0.1.0
01956
1.750
3.700)
3.540
0.130
0.954
7.500
2.207
1.540
0.170
0.044
7.54)
Unilaet V2
1.813
3.540
0.966
11961
6.500
1.411
3.540
0.096
0.961
3.500
1.81%
1.540
0.086
0.961
6.000
Moce V2
3.536
4.899
0.208
0.684
9,000
3.536
4.250
0.208
0.454
9.000
3.535
4,899
0.208
0.484
9.000
Ce-Min-Depphk
0982
3.500
0.078
3.750
0.811
4.500
OMNI
0.965
PricDtA
1.416
2.575
0064
0976
3,000
1,426
2.077
0.09
0374
4.700
0.080
0.90
2750
SPNert
1.304
2.55
0.079
0.66
2.000
1.353
2.596
0.6
0.92
2.000
1.513
3,015
0.866
0.975
2.250
PrompiD
1.770
3.512
0.10
01956
6.500
1.505
3,414
0.102
0.333
4.5
2.082
4.041
0.117
0.054
7.300
LDCM (euzs)
0.958
2,088
0.853
0.988
1.000
9.581
2.139
8.647
0.987
1.000
1.007
2.483
0J03
8:983
1.000
SUPU
ORB
Average
RMSE"
REL.
UnilXppt V1
2.307
5,540
0.130
0.954
3,750
3.207
3.540
0.120
0.254
4.000
3.301
3.540
01170
0.054
6.773
UniDapi V2
1313
2.0000
1.513
0.098
22250
5.409
MoGe V2
3.536
4.559
0.234
9.000
3.530
4.350
0.444
91000
4,895
0.484
91000
G2 Mun:Dpphi
2.756
4.677
0.158
0.785
5,000
2.261
4.65
0.133
0.354
6.70
1.660
3.118
0.055
0.945
4.541
OMNLDD
0.38
3.000
0.935
2750
0:08
SPNett
2,096
3.845
0.120
0.888
4.500
1.921
3,631
0.113
0.968
4,500
1.507
2.881
0.089
0.944
3068
PremgitA
2,000
4.581
0.154
0.888
1,000
2.543
4.475
0.189
0.333
8.000
1.933
3,612
0.110
0.935
0.500
LDCM (ouns)
1.290
2.188
0,864
0.999
1.250
1.247
2380
0.065
0.956
1.350
1,027
2,308
0.055
8.982
1,045
31

---

Published as a conference paper at ICLR 2026
Table 19: Quantitative comparison of point map estimation with baseline methods on the in- door scenes of the DIODE dataset Vasiljevic et al. (2019). Methods marked with & use scenario- specific configurations for indoor and outdoor scenes, respectively. The best and the second-best results are highlighted.
Mataod
1055 Noio:
S/
MAE"
RMSE
REL." 1
4-
RG_1
MАEP
RMSE
REL." 1
4''
RE_1
MAE
RMSE"
REL. 1
RL_
Unilapp
0.911
0.799
0.779
7.500
7.500
UniDeom
9.000
0.342
ManxDepdhi
0.118
0.136
1.000
1.500
0.118
1.000
2250
3.118
0.025
1.000
2.350
GMNI-DC
0.122
0.165
0.055
8999
4.000
0.118
0.132
0.09
100
2250
0.118
0.1.9
0007
1000
2.000
PrieDDA
0.132
0.179
0.029
0.999
4.750
0.123
0.155
0.025
0.909
5.000
0.123
0.156
0.025
0.999
4.250
SPNet:
00:18
2,000
0.115
1.000
1.750
1.250
PrompiDA
0.1.8
1977
6.000
0.900
LDCM
1,000
1.000
8.107
1.000
1.000
1.000
1,000
Mathod
156
530
100
RMNNP
KMNH
RR07 5
RMSFF
UniDepi V2
0.730
0.994
7,500
7,500
0.694
7,000
MoCie V2
1.185
0.343
0.10
9/000
1.085
1.188
0.343
0.410
9.000
1.946
1.185
0.242
0.410
9.000
MonzDeptht
0999
1,250
0.996
0.191
0.596
7.000
OMNI-DC
10.11
0.145
0.04
0199
2,500
0.127
0.183
0.08
099
2.500
0.361
0.275
0.136
0.991
41000
PrivDDA
0.:33
0.159
0.055
0.999
3,750
0.124
01139
0.008
0.99
2.250
0.148
03317
01011
0.995
SPNet:
0.:38
0.140
2.000
0.127
2.70
30000
PrompiDA
0.156
0.191
0.032
0.935
5.000
0.107
0.263
0.042
0.969
6.000
0.226
0.425
0.061
0.075
5.000
LDCM (ouns)
1,000
3,000
0.999
1.000
1.250
Mataod
SIFT
ORB
Vitta--Liaa 32-Lines
REL" 1
RG.1
RMSE*
6
R1.1
RMSE"
UniDept
0.911
0.779
7,000
0.911
0.779
7.500
UniDept V2
0.730
0.994
6,500
0.694
6.500
0.544
7.500
MoGe V2
1.48
1.185
0.342
0.40
8,750
1.455
L.65
03342
0.400
8.700
1.465
1.165
0.242
0.40
9.000
MinsDepdi
0.777
0.545
7,750
0.566
0.270
0.504
0.025
03966
DM
0.313
0.867
0.700
3.121
0.025
0.999
1.250
PrieDA
0.174
00232
0.000
8.975
1.500
0.305
0.260
D066
8.987
1.500
0.124
0.164
0.000
0.090
3:250
SPNett
0.350
0.478
0.117
0.118
5,000
0.398
0.554
0.132
0.794
3,000
0.121
0.163
0.025
0.999
2,000
PropiDA
0.356
01.41
0.100
0.814
3,750
0.511
0.700
0.160
0.791
5..00
0.184
0.311
0.06
0.991
6.000
LDCM (euza)
0.872
0.343
0.945
0.964
1.508
8.208
0.360
0.663
0.952
1.500
0.168
0.144
0.021
8.999
1.000
Viruaal Lidar 16 Lines
Viguaal Lidur 8 Lines
Average
UnilDept V1
0.911
1.017
0.159
0.719
7.500
0.911
0.779
7.500
0.911
0.159
0.779
7:318
UniDepd V2
0.730
0.872
0.164
0.694
7,500
0.730
0.572
0.164
0.694
7.500
0.730
0,872
0.164
0.594
7.273
MoCe V2
1948
1.185
03367
0.810
91000
1.045
1.185
0.541
0.410
91000
1.448
1.185
0.342
0.410
8955
MonsDeptht
0.136
0,211
0,031
0.97
5,000
0.175
0.255
0.042
0.57
5.750
0.305
0.401
0.067
0.575
4.841
OMNI-DC
0.126
0.176
0.000
0.98
2.350
0.145
0.239
0.034
0.944
4.000
0.174
0.341
0.045
0.967
2.77
PrieeDA
2.500
0.988
1.250
0.034
0.984
SPNet
0.126
2.750
0.9%6
0.946
2.36
LDCM (ours)
0.988
1.580
8.993
L150
32

---

Published as a conference paper at ICLR 2026
Table 20: Quantitative comparison of point map estimation with baseline methods on the out- door scenes of the DIODE dataset Vasiljevic et al. (2019). Methods marked with & use scenario- specific configurations for indoor and outdoor scenes, respectively. The best and the second-best results are highlighted.
Method
Vistal-Lide
Vinual- Lalua-LLila-- Laccs
Vasua-LLlas- 1lar-s6--issss
MAP
RMSE"
REL
4'1
RX.]
MAP
RMISE"
RELP
RX_1
M.EE
RMSEE
REL"
6'
RL_
UniDepth
4.380
6.372
0.195
0.644
7,500
4.280
6,371
0.156
0.644
7,500
4.250
5,372
0.196
0.644
7,500
Uniflepth
12.049
0.531
9.000
0.665
9.655
13.009
0.501
9.000
5.505
0.235
4.041
0.636
7.500
OMNI-DC
2,000
0.101
19927
3.300
2,000
3.750
PrloeDA
3.071
2.015
2.057
0.944
2750
SPNa
1898
93889
01099
8896
2.000
1885
11119
00105
[198
2.000
2.053
3.547
0.317
09900
PromptIA
2.392
4.186
0.125
0.925
6,000
2.484
4.427
0.129
0.916
6.000
2.607
4.035
0.133
0.907
6.000
LJX (mum)
1.908
2.381
IUI84
1.010
1.563
2.785
IL073
1.006
1.687
X002
0.893
0.965
1.000
Vinua-Libae 4-Lincs
10%
RMSE
MAP
REL.
REL.7
UniDepth V1
4,290
6,372
0.196
0.644
7,500
4,250
5,372
0.196
0.644
6,250
4.250
5,372
0.196
0.644
7,500
UniDepth V2
9,685
12.049
11521
0.505
9,000
9.685
12.009
0.521
0.505
9.000
9.656
12.389
0.521
0.505
9.000
Madic
4.041
3.503
0.205
0.26
7.500
4.541
5.505
0.205
0.636
5.750
4.941
5.505
0.205
0.636
7.500
G2 MonnDppp
2.699
4.563
0.152
0.887
5.250
4.255
6515
0.257
0.658
7.000
1.645
2.195
0/067
0:900
2.750
OM
2.503
4.513
0.133
0.916
3,750
3.689
6325
0.200
0.769
4,500
1.651
2.501
0.087
0.980
3.500
PriorDM
3.788
2.035
5.000
SPNett
PrompiDA
5,316
4.066
2.355
(run)
3.486
0.099
3.313
4.361
1.345
0.876
Method
MAB
KMNEP
RULF
8'1
R5.1
MAD
RMS37
RE37
27.
R3.1
MAEP
RMSSP
K03.7
8.11
11.1
UnitPepth
4,280
6.372
0.196
0.642
7,500
4.280
0.372
0.196
0.644
7.500
4.240
5,373
0.196
0.644
7.500
UniDepth
12.040
0.521
9.685
0.305
0.656
12.049
0.303
9.000
3.505
0.255
4.941
7.500
4.041
0.205
0.636
7.500
OMNE
2.355
0.058
1.995
1.777
0.066
3.000
PriseiiA
1,990
3.991
0.00
0.55
1951
3.947
0.100
0.55
5.000
1.9%
3,000
0.101
0.851
5.000
SPNet
1.679
PrompiIA
3,500
(wans)
1.366
2,048
IU777
1.463
0.083
0.980
1.000
Metual
SIPT
DRR
MAP"
KMNEF
RUL.
4'1
RE.1
MAD.
RMSS
REL
27
R3.1
MAE."
RMSSI
R03.7
8.11
14.1
UniiDppth
6.373
0.106
4.250
0.544
7.356
UniDepth
12.040
0.521
0.605
0.656
0.305
9.000
7.341
3.390
0.121
2.291
2.194
0.920
UMNI-ICC
3.284
0.106
0.92
3,000
2.146
3.802
0.310
08335
3.00
2.089
3.413
0.113
0.940
3-40M
PriseitA
2,004
3.356
0.10
0.944
4.000
2.232
1.604
0.113
0922
4.000
2.136
3.15
0.111
0.938
3.955
SPNa
23993
10102
23333
PrompIDA
2.734
33

---

Published as a conference paper at ICLR 2026
Table 21: Quantitative comparison of point map estimation with baseline methods on the iBims-1 dataset Koch et al. (2018). Methods marked with use scenario-specific configurations for indoor and outdoor scenes, respectively. The best and the second-best results are highlighted.
Mathod
105 Nrio::
3/
MAR
RMSSP
R11""
[
HE.1
RMSF
RM11" 2
8-
RC.
RMSE"
R1177
1.239
0.370
0.239
1.239
0,370
0.250
1:330
0,590
0.250
UnilDept V2
0.355
01:339
01912
7,000
0.365
0.480
0.307
0532
7.000
0.365
0.488
0.107
0.982
7.000
MoCie V2
0.574
11067
0.400
5,000
0.594
0.662
0.156
0.300
5000
0.574
0.667
0.156
0.300
8000
0.995
2250
0.094
SPNett
0.128
0.155
0,085
0.96
1,750
00130
0.181
0.088
0.988
1.750
011E
0.189
0.086
0.995
1.250
PrompifAA
0.167
09988
5,100
161
0.945
0.987
6.000
0.
0.945
0.083
0.000
LDCM (ouzs)
0.075
0.151
0.821
0.996
1.000
0.076
0.158
0.023
0.995
1.000
0.078
0.164
0.023
0.995
1.000
MAR?
RMSSP
RF1.
[3.
MAPP
RMSFF
RE17
RL.]
MAP
RMSFF
RB17
R1
UnilXprt V1
1.154
1.239
0.370
021
9100
1:154
1.21
0.370
0.3M
9100
1.154
1.399
0.37
0.339
UniDepd V2
0.365
0.489
0.107
0932
7,000
0.365
0.489
0.107
0932
7.000
0.565
0.489
0.107
0.952
7.000
Mucie
0.574
0240
K,000
0.574
0.62
0.156
0:300
8000
4.94
0.156
0.140
8000
Mon:Depdit
0.143
0.91
4.500
0.165
0044
0985
5.000
0.249
0.392
0.069
0.958
6.000
OMNI DC
00.13
0.214
0.34
0992
2,500
0.151
0.477
0.42
0993
3.000
0.187
0:310
0.01
0.977
4.000
PrieDDA
0.146
0.221
0.040
0.911
4,500
0.153
0.236
0.042
0.900
275
0.168
0.281
0.045
0:987
2,000
SPNet:
0.138
2,000
0.151
PrompiD
0.172
0.275
0.466
0.97
6.000
186
0.300
0.045
0.955
7.00
0.216
0.353
0.057
0.990
4.750
LDCM
1.000
1.000
8.985
1,000
Mataod
SIFT
ORB
Vimua-1 Late 32-
REL" 1
RMSE
REL. 0
8
RL.]
RMSE
REL.
UniDepti V2
0.365
6.250
0.952
7.000
MoCic V2
0.574
0.667
0.156
0.740
5,000
0.574
0.682
0.156
0.340
8.00
0.594
0.663
0.156
0.745
8.00
G2 MonoDeptht
0.273
0.396
0.082
0.20
0,250
0,303
0.432
0.042
0.900
57700
0.154
0.255
0.41
0399
4.000
OMNLDDC
4,000
0.251
0.074
0.925
4.50
0.039
0.98
2,000
0.184
2.00
02270
0.045
0.982
57700
SPNett
0.170
0.043
0985
3,000
0.192
0.200
0.000
0971
3000
0.14E
0.236
0008
0.901
20000
PrompID
0.947
5,000
0.315
0.903
57700
3.174
0.277
0.947
0.986
LDCM (ours)
0.103
0.308
0.829
0.990
1.000
0.119
0.230
0.034
0.986
1.000
0.8E
0.885
0.034
0.993
1.000
Malara
Average
MARF
RMSU
R13."
MAIE
RMS6
Mii."
6.
RMS65
RBL7
Unilpp Vi
1.154
1.239
0.370
0.239
0.000
1.154
1.239
0.390
0.250
9.000
1.154
1.2.0
0.590
0.350
9.000
Unileept V2
0.305
0.889
0.107
01922
7,000
0.363
0.489
0.107
0.92
7.000
0.365
0.485
0.307
0.932
6.909
Mocie V2
0.574
0.067
0.156
0.200
5,000
0.574
0.607
0.156
0.740
K000
01574
0.60
01156
0.700
5000
Man:Depphi
0.165
0.966
5,250
0.197
0.338
0.002
0.977
5.700
3.186
0.267
0.032
0.972
4.750
OMNI DC
0.150
0.240
0.041
0.99
2.750
0.173
0.264
0.045
0.52
4.200
0.164
0.256
0.046
0.980
3.341
PrieDDA
0.152
0.235
0.011
0.900
2.750
0.102
0.218
0.44
0.98
2.000
0.159
0.240
0.043
0.989
3.500
SPNat:
0.1.0
0.04
2.455
PromiiAA
01987
5.750
0983
5.545
LDCM
0.992
1.000
8.101
8.025
0.990
1,000
0.026
8.992
1.000
34

---

Published as a conference paper at ICLR 2026
Table 22: Quantitative comparison of point map estimation with baseline methods on the in- door scenes of the ETH3D dataset Schops et al. (2017). Methods marked with scenario- specific configurations for indoor and outdoor scenes, respectively. The best and the second-best results are highlighted.
Mathrll
1091 Noise
MAE?
RMSE*
REL."
[S..]
MAEP
RMSE
REL." 1
6'
RL.]
MAE!
RMSE"
REL.
RL_
UniDept Vi
2.429
2.649
0,641
0.056
9,000
2.429
2,640
0.641
0.066
9.000
2.429
2.649
0.64]
0.066
9.000
Unillept V/2
0.624
0.726
0.166
0.825
5,000
0.524
0.726
0.165
0.325
8,000
0.524
0.726
0.166
0.535
$.000
0.89
1,000
0.338
7.000
0.839
7.000
0.361
0.412
0.083
0936
2.750
0.362
0.466
0.085
0.56
4.000
0.563
0.421
0.089
0.986
4.300
OMNIDD
0.307
0.636
0089
0996
1.30
0.361
0.43
0.08
0956
3000
0.361
0.417
01886
0.96
3000)
0.36
0.956
5250
0.089
0.956
3.000
SPNet
0.361
0.566
3,000
0.361
0.966
3.000
0.419
0.035
0.956
3.50
ProopiA
0.331
0.417
0,078
0.908
2,500
0.319
0.397
0.078
0.967
2.000
0.323
0.404
0.078
0.967
2.000
IDM (muns)
0.256
0.308
0,870
0.999
1.010
0.255
013001
0.678
8.999
1.000
0.258
0.302
0J178
8.999
1.004
RELF
RMS8
REL7
RMSS2
RELF
UniDept Vi
2,429
2.649
0.641
0.056
9,000
2.429
2.649
0.541
0.066
9.000
2.429
2,649
0.64]
0.265
9,200
Unilapth V22
0.644
0.776
0.166
0.835
5,000
0.534
0.736
1.165
0.335
8.000
0.534
0.336
0.166
0.535
8000
Mocee V2
0.500
0.020
0.123
0.39
7,000
0.500
0.020
0.123
0.339
7.000
0,500
0.620
0.123
0.339
6.750
G2-MinnDDphh
0.367
0955
5.000
0.383
0.932
6.000
0.015
6.550
0956
0.947
PrieDDA
0.367
0.435
0.089
0956
3,750
0.371
0.440
0.000
0955
2.50
0.585
0.486
0.092
0.882
2.500
SPNeet
0956
0.175
0.484
0.944
4.000
0.89
0.99
3.50
PrompiD
0.322
01423
0.070
0998
3.000
08
0.485
0068
0.983
0.5
0.608
0.088
0.985
3.000
LDCM (euzs)
0.257
0
0.070
0.988
1.000
9.263
0.334
8:071
0.998
1.000
0.279
0.382
0.073
8.996
1.000
SIPT
OKB
Vimua-1 Nimai-Liktr-000 Atae-12-Linee
Unilapph V1
2.429
2.649
0.641
0.966
0.000
2.429
2.649
0.541
0.066
9.000
2.430
0.640
0.641
0.096
9.000
UniDept V2
0.624
0.726
0.106
0.325
5,000
0.524
0.736
0.165
0.825
5,000
0.524
0.736
0.166
0.525
9.000
MoCi V2
0.300
0.820
0.123
0.30
6,350
0.500
0.600
0.123
0.350
62500
0.500
0.630
0.125
0.530
7000
MonoDepott
0.517
0,675
0.136
0844
5.750
0.50
0,655
0.138
0.353
6.750
0.376
0:400
01991
0.93
6.000
OMNI-DC
0.424
0,532
0.100
0.90
4,500
0.447
0.567
0.112
0.890
4.20
0.305
0.437
01089
0.95
32250
PRivDA
10.
01919
3.000
0.511
0.10
8.00
0.367
0.438
01089
0.96
3,500
SPNett
0.408
0.515
0.100
0.924
3,000
0.19
0.539
0.102
0.922
3.000
0.367
0.450
0.089
0.955
4.000
PiomyiDA
0.436
0.557
0.106
0.909
4,500
0.455
0.582
0.112
0.993
4.500
0.325
00430
0.078
0.968
2.000
1 IDCM (ouns)
0.279
0.380
0,815
0.911
1,008
8,288
0.361
8,877
0.986
1.080
0.258
00319
0.088
8.998
1.000
Mebhd
Vittal-Eidar: 16-Lanz
Varual Lada: Linies
Average
RMSE
REL. [
4'
RG.1
RMSE
6
RL.1
RMSE7
RELF
61
R1_
UniDeptt Vi
2.499
2.649
0.966
9.000
2.429
2.640
0.066
9.000
2.429
0.64]
0.066
91000
Unilagnt V22
0.634
0.716
0.166
0.835
5.000
0.534
0.736
0.165
0.335
8.000
0.634
0.336
0.165
0.825
5.000
MoCe V2
0.500
0.620
0.123
0.339
7,000
0.500
0.620
0.123
0.839
7.000
0.500
0.630
0.123
0.839
6.841
MonsDeptit
0.383
0.417
0,93
0952
5,000
0421
0.563
0.98
0943
6.000
0.411
0.523
0.101
0.990
5.455
OMNI-DC
0.370
0.451
0.000
0955
3,000
0.389
0.306
0.093
0.500
4.000
0.585
0.474
0.054
0.943
3.773
PricDA
0.371
0.647
0.000
0.855
3,000
0.379
0.465
0.091
0.92
2750
0.377
0.455
0.092
0.952
3.409
SPNett
0.373
(1.69
0190)
01954
4.900
0.190
0.313
0.084
0.988
5.000
0.581
0.475
0.02
0.943
3,50
PromptAA
0.338
3,500
0399
2250
0.953
2727
LDCM
0.988
1.000
8.996
1.000
8.9%6
1,000
35

---

Published as a conference paper at ICLR 2026
Table 23: Quantitative comparison of point map estimation with baseline methods on the out- door scenes of the ETH3D dataset Schops et al. (2017). Methods marked with use scenario- specific configurations for indoor and outdoor scenes, respectively. The best and the second-best results are highlighted.
Virtall Linss
Vittel Lidar Lites
Vinual Lines
MAR?
RMSS
R13.P
MAD!
RMSB
Mi117
6
MARF
RMSLF
RB17
UniDept: V]
4.653
5.100
0.461
0.145
9.000
4.053
5.100
0.461
0.145
9.000
4.553
5.100
0.45]
0.345
9/220
UnDDkpd V2
1.579
2.844
0.216
0.712
5,000
1.379
2.844
0.216
0.712
8.000
1.870
3.844
0.216
0.712
5.000
Mocie V2
0951
1,316
0.115
0.90
5.70
0.51
1.206
0.115
0.890
6770
0.91
1.200
0.115
0.880
0.500
ManxDepdhi
0.688
0.999
4.50
0.924
4770
0.916
3250
GMNI-DC
[0636
0.288
07966
0991
2.000
0.64
0833
0067
889
2250
0888
0.871
0.099
00027
2.59)
PrieDDA
0.641
0.804
0.067
0.929
3,750
0.646
08888
0.063
0.929
2.50
0.657
0.346
0.089
0.927
2,250
SPNet:
PrompiDA
0.756
0.044
0.33
6.50
0.763
1.665
0.094
0.911
6.250
1.411
0.092
0.093
4.750
DDM (oand)
0.303
0.510
0.841
0.998
1.000
0.402
0.310
0.999
1.080
00872
8:993
1.000
Mebhdd
Vintui-Lidee
Verual-Caaa-
MAE"
RMSE'
RF1." [
R3.
MAE
RMSF
REL.
RL.]
MAE
RMSE7
REL7
RL_]
UniDep Vi
4.633
5,100
0,461
0.145
9,000
4.553
5.100
0.461
0.145
0.000
4.653
5.100
0.461
0.145
9.000
Unilkppt V.t.
1.819
0.316
0.112
5,000
1.479
2.84
0.116
0.912
8.000
1.879
0.216
0.913
50000
MoCe V2
0.31
1.306
0.315
0.890
5,500
0.51
1,206
0.115
0.890
2.000
0.951
1.206
0.115
0,50
71200
G2 Mun--ppphh
0.007
1.448
0.118
0.76
6,250
1.54]
2.263
0.20
0.724
6.750
0.618
0.333
0.085
0.982
3000
OMNI-DC
0.757
1.007
0/568
0903
3,000
1,209
1.726
0.141
0.330
5,00
01825
0.758
018S
0.931
3750
PrieDDA
107009
01947
01991
01911
2.000
1,000
1.499
0.140
0.350
3.500
0.697
03117
0.885
0.990
5000
SPNet:
5.500
366
0.325
01054
0.983
2000
PrompiDA
0.872
1.711
0.100
0.913
4.500
1.187
1.783
0.131
0.363
3.750
0.715
1.100
0.055
0.025
37550
LDCM (ouns)
0.994
1.000
1.000
8.999
1,000
Mataod
51%
V/
15.
42
[G.1
RMSE
R0.1
RMSE
RELF
49-
R1_1
4.653
4.681
0.145
UniDept V2
1,579
0.216
0.712
5,000
0.216
0.712
8.000
0.216
0.712
8.000
MoGe V2
0.951
1,306
0.115
0.900
7,000
0.981
1.206
0.115
0.500
6.70
0.951
1.206
0.115
0.500
6.750
MinoDephht
0.621
0.33
2.750
0.086
3.750
0.927
4.500
OMNI-IC
0.617
0.746
0.92
3.000
0.9
2000
00308
0006
0.090
20000
0.900
4.70
0.085
0.929
3.50
SPNet
0617
0.760
0085
013
2,500
0.21
02001
0005
0993
3000
0.64
0.991
01089
0.900
3,500
PrompiDA
5.000
0.026
6.250
0.771
LOCM louno
0.39
0.886
0.880
0.999
1.000
0.9%
0.498
0.000
0.999
1.000
0.405
0.590
0144
0.994
1.000
Mataod
MAEP
RMSE
REL" 4
R3.1
MAP
RMSE
REL7 3
R0.1
MAP
RMSE:
REL7
67-
R1.
UniDpp Vi
4.653
5,100
0.461
0.145
0,000
4.553
5.100
0.463
0.145
0.000
4.653
3.100
0.461
0.145
9.000
Unillemm V22
1.5Ty
2.546
0.216
0.122
5,000
1.579
2
0.316
0.322
8.00
1.379
2,844
0.216
0.712
51000
0.951
0.800
5,750
0:911
1.206
0.500
5,000
0.500
3,077
Monst
0346
0.357
0.801
OMNI DC
0.717
0.973
0.097
0914
3,000
0.763
1.800
0.10
0.902
3,500
0.714
0.946
0.055
0.915
2.795
PricDAA
01688
0.800
0920
2,000
0.330
00379
0099
0912
2.000
0.998
0.500
0,955
0991
3.295
SPNett
0.754
1.453
0.106
0.00
4.500
0.443
1.311
0.102
0.90
4.000
0.733
1.243
0.100
0.914
3932
PremgitAA
0:871
1.456
0.100
0.899
5.500
0.903
1.576
0.119
0.355
0.500
0.824
1,422
0.100
0.91
3.591
LDCM (ouz))
0.444
8.15
0.866
0.993
1.000
8.454
0.644
0.647
0.990
1.000
0.427
0.390
0.044
8.995
1.000
36

---

Published as a conference paper at ICLR 2026
Table 24: Quantitative comparison of affine-invariant point map estimation with baseline
methods on the KITTI dataset Geiger et al. (2012); Uhrig et al. (2017). The best and the second-best results highlighted.
Lidar-64-Lines
Lidar-32-Lines
Lidar- 16-Lines
Lidar-8-Lines
Method
REL*
Rs..
REL' 4
6'
Rk.
REL" L
8' f
RE.
REL* 4
RK.,
VGGT
0.147
0.823
5.000
0.147
0.823
4.500
0.147
0.823
4.500
0.147
0.823
4,500
MoGe V2
0.056
0.968
2.000
0.056
0.968
2,000
0.056
0.968
2,000
0.056
0.968
2,000
WorkdMirrer
0.095
0,920
3.000
0.103
0.900
3.000
0.118
0.865
3,000
0.129
0.838
3.500
MapAnything
0.362
0.347
6.000
0. 0.364
0.345
6,000
0.364
0.346
6.000
0.366
0.345
6,000
Pow3R
0.140
0,886
4.000
0.147
0.870
4,000
0.151
0.858
4.500
0.153
0.848
4,000
LDCM
0.993
1,000
0.033
0.991
0.034
1.000
0.036
1,000
Lidar-4-Lines
10%
5%
3%
Method
4
6f
Rk...
4
Rk.
4
1
Rk..
RET 4
Rk.]
VGGT
0,147
0.823
4.000
0.147
0.823
4.500
0.147
0.823
4.500
0.147
0.823
4,500
MoGe V2
0,056
0.968
2,000
0.056
0.968
2,000
0.056
0,968
2,000
0.056
0.968
2,000
WorldMirrrr
0.136
0.821
4,000
0.102
0.900
3,000
0.100
0,901
3,000
0.100
0.902
3,000
MapAnything
0.367
0,344
6.000
0.365
0.345
6,000
0.365
0,345
6,000
0.367
0.341
6,000
Pow3R
0.154
0.844
4.000
0.154
0.847
4,500
0.155
0.843
4.500
0.155
0.841
4,500
LDCM
0.043
0,976
1.000
0.034
0.990
1.000
0.035
0.989
1,000
0.036
0.987
1,000
Method
1%
SIPT
ORB
Averige
REL
6f1
Rk...
REL"
Rk.
REL" 4
6. 1
Rk.
REL" 4
Rk.1
VGGT
0.147
0,823
0.147
0.823
4,500
0.147
4.500
0.147
4,500
MoGe
0,968
2,000
0.056
0.968
1.500
0.056
0,968
1.500
0.056
0.968
1,909
WorldMirrer
0.100
0.102
3.136
MapAnything
0,370
0.338
6.000
0.367
0.344
6.000
0,367
0.343
6.000
0.366
0.344
6,000
POw3R
0.155
0.155
4.500
0.152
4.318
LDCM
0.983
0.054
0.961
0.051
1.500
0.039
1.091
Table 25: Quantitative comparison of afline-invariant point map estimation with baseline methods on the indoor scenes of the DIODE dataset Vasiljevic et al. (2019). The best and the second-best results are highlighted.
10% Naise
5%
3%
Method
REL
8
Rk...
REL" 4
8''1
Rk.]
REL" 1
8' f
Rk.j
REL' 4
8
Rk.]
VGGT
0.107
0,926
5.000
0.107
0.926
5,000
0.107
0.926
5,000
0.107
0.926
4,500
MoGe V2
0.052
0.972
2.000
0.052
0.972
2,000
0.052
0.972
2,000
0.052
0.972
2,000
WorldMirrer
0.079
0.951
3.500
0.072
0.952
3,500
0.070
0,953
3,000
0.071
0.953
3,000
MapAnyghing
0.169
0.762
6.000
0.168
0.762
6.000
0.168
0.762
6.000
0.169
0.763
6,000
Pow3R
0.099
0.960
3.500
0.104
0.954
3,500
0.106
0.951
4,000
0.108
0.946
4.500
LDCM
0.009
1.000
1.000
0.009
1.000
1.000
0.009
1.000
1.000
0.009
1.000
500
100
SIFT
ORB
Method
REL/
RK.C
REL' 4
Rk.
REL." 1
1
RE.
REL 4
6 ^
Rk.
VGGT
0.107
0.926
4.500
0.1
0.926
4,500
0.107
0.926
4.500
0.107
0.926
4,500
MoGe V2
0.052
0.972
2.000
0.052
0.972
2.000
0.052
0.972
2.000
0.052
0.972
1,500
WorldMirree
0.072
0.955
3.000
0.073
0.955
3,000
0.073
0.956
3,000
0.078
0.945
3,000
MapAnything
0.175
0.753
6.000
0.173
0.758
6.000
0.176
0.753
6.000
0.175
0.758
6,000
Pow3R
0.110
0.944
4,500
0.110
0.943
4,500
0.109
0.944
4,500
0.109
0.944
4,500
LDCM
0.010
0.999
1.000
0.012
0.996
1.000
0.029
0.979
1.000
0.036
0.972
1.000
Method
Virtual Lidar 32-1-222111
32-Lisss
Virtual- Lidar 16-Lines
Viriual-Lidar 8- Lines
Average
REL
6f
Rk.,
REI 4
Rk.]
REI. L
1
Rk.j
RET. 4
6
Rk.
VGGT
0.107
0.926
4.500
0.107
0.926
4.500
0.107
0.926
4,500
0.107
0.926
4.636
Molie V2
0.052
0.972
2,000
0.052
0.972
2.000
0.052
0.972
2.000
0.052
0.972
1.955
WorldMinrr
0.072
0.955
0.072
0.955
0.073
0.955
3.000
0.073
0.953
3.091
MapAnything
0.175
0.753
6.000
0.173
0.758
6.000
0.173
0.759
6.000
0.172
0.758
6.000
PoW3R
0.109
0.945
4.500
0.109
0.943
4,500
0.110
4.500
0.108
4.273
LDCM
0.010
0.999
1.000
0.010
0.999
0.011
0.997
1.000)
0.014
1.000
37

---

Published as a conference paper at ICLR 2026
Table 26: Quantitative comparison of affine-invarinnt point map estimation with baseline methods on the outdoor scenes of the DIODE dataset Vasiljevic et al. (2019). The best and the second-best results are highlighted.
Virtual- 64-Lines
Virtual- L2-Lines
Virtual-Lidar 16 16-Lines
Virtual-Lide 8- 8-Lines
Method
REL/
6'
Rk..
REI 4
Rk.
REL L
1
Rk.
REL 4
69"
Rk.
VGGT
0.215
0.700
5.000
0.215
0.700
5.000
0,215
0,700
5,000
0.215
0,700
5,000
Molie v2
0.124
0.841
2,000
0.124
0.841
2.000
0.124
0.841
2,000
0.124
0.841
2,000
WorldMirrer
0.156
0.786
3.000
0.156
0.788
3,000
0.154
0.792
3.000
0.155
0.792
3,000
MapAnything
0.299
0.506
6.000
0.310
0.481
6,000
0,309
0.489
6,000
0.317
0.487
6,000
Pow3R
0.200
0.745
4,000
0.200
0.747
4.000
0.201
0.743
4.000
0.201
0.745
4,000
LDCM
0.965
L,000
0.079
0.950
0.090
1.000
0.097
1.000
Method
Virtual-1 idar-441 Lines
10% Noise
5%
3%
REL*
8'
Rk...
REL 4
Rk.
REL* 4
8'' f
Ri.
REL 4
Rk.1
VGGT
0,215
0,700
5.000
0.215
0.700
5,000
0.215
0,700
5,000
0.215
0.700
5,000
MoGe V2
0.124
0.841
2.000
0.124
0.841
2,000
0.124
0.841
2,000
0.124
0.841
2,000
WorldMirror
0.154
0.793
3.000
0.140
0.822
3,000
0.151
0.799
3,000
0.157
0.782
3,000
MapAnything
0.311
0.500
6.000
0.296
0.500
6.000
0.291
0,510
6.000
0.291
0.517
6,000
Pow3R
0.201
0.745
4.000
0.181
0.770
4,000
0.190
0.756
4.000
0.194
0.752
4,000
LDCM
0.117
0.856
1.000
0.059
0.988
1.000
0.061
0.985
1.000
0.063
0.981
1,000
Method
1%
SIFT
Average
RELF
o'
Rk.
REL/ 4
Rk.]
REL." 4
8' 1
Rk.j
REL. 4
6
Rk.
VGGT
0,215
0,700
5.000
0.215
0.700
5.000
0,215
0.700
5,000
0.215
0,700
5,000
MoGe V2
0.124
0.841
2,000
0.124
0.841
2,000
0.124
0.841
2.000
0.124
0.841
2,000
WorldMirror
0.158
0.781
3,000
0.1
0.784
3,000
0,170
0.750
3.500
0.155
0.788
3.045
MapAnything
0.293
0.515
6.000
0.301
0.505
6,000
0,302
0.505
6.000
0.302
6,000
Pow3R
0.199
0.747
4.000
0.200
0.748
4.000
0.197
0,751
3,500
0.197
0.750
3.955
LDCM
0.068
0.971
1.000
0.071
0.966
1.000
0.073
0.957
1,000
0.077
0.949
1,000
Table 27: Quantitative comparison of affine-invariant point map estimation with baseline methods on the iBims dataset Koch et al. (2018). The best and the second- best results are highlighted.
10% Noise
5%
3
Method
REL
8
Rk.
REL" J.
Rk.]
REL. 1
6" +
Rk.
REL' 1
6"
Rk.]
VGGT
0.048
0.967
4.000
0.048
0.967
4.000
0.048
U.967
3.500
0.048
0.967
3.500
MoGe V2
0.046
0.972
2.500
0.046
0.972
2.500
0.046
0.972
2.500
0.046
0.972
2.500
WorldMirror
0.044
0.972
2,000
0.043
0.968
2,500
0.043
0.965
3,000
0.042
0.963
3,500
MapAnything
0.233
0.612
6.000
0.231
0.616
6.000
0.231
0.614
6.000
0.230
0.618
6,000
Pow3R
0.077
0.952
5.000
0.068
0.962
5.000
0.064
0.965
4.500
0.061
0.967
4,000
LDCM
0.013
0.996
1.000
0.013
0.995
1.000
0.014
0.995
1.000
0.015
0.994
1.000
500
100
SIFT
ORB
Method
REL
8
Rk..
REL" 1
87
Rk.]
REL"
8 1
Rk.
REL
8
Rk.1
VGGT
0.048
0,967
4.500
0.048
0.967
4,500
0.048
0.967
4.000
0.048
3,000
MoGe
0.046
0.972
0.046
0.972
0.046
0.972
2.500
0.046
0.972
2,000
WorldMirrer
0.968
2.500
0.042
0.042
0.967
3,000
0.060
0.946
4.500
MapAnything
0.235
0.597
6.000
0.234
0.602
6.000
0.232
0.614
6.000
0.234
0.613
6.000
Pow3R
0.061
0.062
4.000
0.062
0.967
4,000
LDCM
0.018
0.991
L.000
0.022
0.987
1.000
0.020
1.000)
0.024
1.000
Method
Virtual- Lidar-32-Linn
Virtual- Vertual-LidarIIeee Lidar-16--inneee Li-Lines
Virtual- Lidnns
Average
REL/ 1
df
RK.
REL/ 4
87
Rk.]
REL/ 1
1
Rk.J
REL" 1
89
Rk.1
VGGT
0.048
0.967
3.500
0.048
0.967
4.000
0.048
0.967
4.500
0.048
0.967
3,909
MoGe
0,046
0.972
2,500
0.046
0.972
2,500
0.046
0.972
2.500
0.046
0.972
2.455
WorldMirrer
0.042
0.967
2.500
0.042
0.967
3.000
0.042
0.968
2.500
0.044
0.965
2.864
MapAngthing
0.233
0.610
6.000
0.233
0.609
6.000
0.232
0.613
6.000
0.233
0.611
6.000
PRw3R
0.062
0.966
5.000
0.061
0.968
4.000
0.061
0.968
4,000
0.064
0.965
4,318
LDCM
0.015
0.993
L.000
0.017
0.991
1.000
0.020
0.990
1.000
0.017
0.992
1.000
38

---

Published as a conference paper at ICLR 2026
Table 28: Quantitative comparison of affine-invariant point map estimation with baseline methods on the indoor scenes of the ETH3D dataset Schops et al. (2017). The best and the second best results are highlighted.
Method
10% Naise
5%
3
1%
REL 4
6.1
Rk..
REL" 4
8f f
Rk.j
REL" L
6" 1
Rk.
REL" J
of
Rk.j
VGGT
0.045
0.988
2,000
0.045
0,988
3,500
0.045
0,988
3,500
0.045
0.988
3,500
MoGe V2
0.041
0.986
2.500
0.041
0.986
3.000
0.041
0
3,500
0.041
0.986
3,000
WorldMirror
0.048
0.986
4,000
0.042
0.989
2,500
0.040
0.990
1,500
0.041
0.992
1,500
MapAnything
0.255
0.559
6.000
0.252
0.562
6.000
0.252
0.562
6,000
0.254
0.560
6,000
Pow3R
0.070
0,987
4.000
0.068
0.990
3,500
0.069
0.990
3,500
0.070
0.990
4,000
LDCM
0.047
0.994
2.000
0.047
0.994
2.500
0.047
0.994
2.500
0.047
0.994
2.500
Method
500
100
SIFT
ORB
REL
67
Rk.
REL" 4
87 f
Rk.4
REL. L
6" f
Rk.
REL 4
Rk.1
VGGT
0.045
0,988
3.000
0.045
0.988
3,000
0.045
0.988
3,000
0.045
0.988
2.500
MoGi V2
0.041
0,986
3.000
0.041
0.986
3.000
0.041
U.986
3,000
0.041
0.986
2.500
WorldMirror
0.043
0.991
2,000
0.043
0.990
2,000
0.043
0.990
2.000
0.048
0.980
4,000
MapAnything
0,261
0.545
6.000
0.259
0.549
6,000
0.258
0,344
6,000
0.257
0.554
6,000
Pow3R
0.073
0.988
4.000
0.073
0.988
4.000
0.073
0.988
4.000
0.073
0.989
3,500
LDCM
0.047
0.993
2.500
0.048
0.992
2.500
0.049
0.993
2.500
0,050
0.992
2.500
Method
Virtual Lidar 32 Lines
Virtual Lidar 16 Lines
Virtual Lidar Lines
Average
REL
1f
Rk.
REL/ 4
of
Rk.]
REL.
L
1
Rk.
REL' 4
Rk.
VGGT
0.045
0.988
3.500
0.045
0.988
3,000
0,045
0.988
3.500
0.045
0.988
3.091
MoGi
0,986
3,000
0.041
0.986
0,041
3.000
0.041
2,955
WorldMirror
0.041
0.991
1,500
0.042
0.991
2.000
0.043
0.990
2.000
0.043
0.989
2.273
MapAnything
0.259
0.551
6.000
0.257
0.554
6.000
0,256
0.552
6.000
0.256
0.554
6,000
Pow3R
0.071
0.989
4.000
0.072
0.988
4.000
0.073
0.989
4.000
0.071
0.989
3.864
LDCM
0.047
0.994
2.500
0.047
0.993
2,500
0.048
0.992
2.500
0.048
0.993
2.455
Table 29: Quantitative comparison of affine-invariant point map estimation with baseline methods on the outdoor scenes of the ETH3D dataset Schops et al. (2017). The best and the second-ber results are highlighted.
Hirual-Liddr-64-ai
Virtual-Lidar-3 Lidar-32-Line Li-Lines
Virual-Lidar- 1iie-16-Jl 16-Lines
Virtual-Lidar--a Lines
Method
RELÍ 4
8
Rk..
RELL1 1
87
Rk.
REL" 1
8' 1
RE.
REL" 4
8
Rk.l
VGGT
0,061
0.967
4.500
0.061
0.967
4.500
0
0.967
4,500
0.061
0.967
4,500
MoGe V2
0.046
0.974
2.500
0.046
0.974
2.500
0.046
0.974
2.500
0.046
0.974
2.500
WorldMirrer
0.048
0.970
3.500
0.048
0.969
3,500
0.048
0.971
3.500
0.050
0.969
3,500
MapAnything
0.273
0.542
6.000
0.277
0.535
6.000
0.276
0.540
6.000
0.275
0.543
6,000
Pow3R
0,076
0.977
3.500
0.075
0.978
3,500
0.075
0.978
3.500
0.075
0.980
3.500
LDCM
0.027
0.997
1.000
0.028
0.997
1.000
0.029
0.996
1.000
0.032
0.993
1,000
Method
Virtual Lidar Lines
10% Noise
5%
3%
REL
Rk.,
REL"
4
Rk.]
REL 1
1
Rk.
REL 4
8 ~
Rk.1
VGGT
0.061
0.967
4.500
0.061
0.967
3,500
0.061
0.967
3.500
0.061
0.967
3,500
MoGe V2
0.046
0.974
2.500
0.046
0.974
2.000
0.046
0.974
2.000
0.046
0.974
2.000
WorldMirrer
0.048
0.973
3,500
0.058
0.961
0.061
0.953
4,000
0.063
0.951
4,500
MapAnything
0.276
0.546
6.000
0.271
0.545
6.000
0.268
0.549
6.000
0.268
0.553
6.000
POW3R
0.075
0.980
3,500
0.082
0.960
5.000
0.079
0.969
4.000
0.078
0.972
4,000
LDCM
0.040
0.985
1.000
0.026
0.998
1.000
0.027
0.998
1.000
0.027
0.998
1.000
Method
1%
SIFT
ORB
Average
REL
8
Rk..
REL"
8
Rk.]
REL"
8" f
Rk.
REL"
Rk.1
VGGT
0.061
0.967
4.000
0.061
0.967
4.500
0.061
0.967
3.500
0.061
0.967
4.091
MoGe V2
0.046
0.974
2,500
0.046
0.974
2,500
0.046
0.974
2,000
0.046
0.974
2.318
WorldMirror
0.054
0.961
4.000
0.050
0.969
3,500
0.077
0.931
4.500
0.055
0.962
3.773
MapAnything
0.271
0.548
6.000
0.276
0.543
6.000
0,275
0.545
6.000
0.273
0.544
6.000
Pow3R
0.076
0.975
3.500
0.076
0.977
3,500
0,077
0.973
3.500
0.077
0.974
3.727
LDCM
0.027
0.997
L.000
0.031
0.994
1.000
0.030
0.994
1.000)
0.029
1.000
39


Reviews:
[{"review_id": "BlOBIq5aOL", "reviewer": "Reviewer_69y4", "summary": "The paper proposes a two-stage pipeline that produces a metric-consistent dense depth map from a sparse prior + a monocular relative-depth foundation model via a Poisson-style reconstruction, and then feeds that coarse depth and RGB image into the network that regresses a metric 3D point map. The method is evaluated zero-shot across many benchmarks and reports better performance than previous works in both depth completion and point-map estimation tasks.", "questions": "\tCould the authors clarify the computational cost of the Poisson reconstruction stage (Eq. 1–4) compared to simpler alignment methods like LWLR?\n\n\tThe related work ‘monocular geometry estimation’ section could be improved by adding recent and concurrent 3D foundation models such as\n- [1] Jiang, Lihan, et al. \"AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views.\" SIGGRAPH Asia 2025\n- [2] Keetha, Nikhil, et al. \"MapAnything: Universal feed-forward metric 3D reconstruction.\" arXiv preprint arXiv:2509.13414 (2025).\n\nIncluding and discussing these would better situate the proposed approach in the broader landscape of geometry foundation models.\nFurther reference recommendation for depth-completion task:\n- [1] Jeong, Chanhwi, et al. \"Test-Time Prompt Tuning for Zero-Shot Depth Completion.\" ICCV 2025.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "\tThe proposed Poisson-based initialization framework is architecturally simple yet highly effective, avoiding complex model design.\n\tThe paper provides extensive empirical evaluation through cross-dataset, zero-shot testing on multiple benchmarks (KITTI, iBims-1, DIODE, ETH3D, etc.), demonstrating strong generalization.", "weaknesses": "\tTable 3 evaluates different alignment strategies (Global, LWLR, Poisson), but it would be insightful to include a comparison using Equation (1) only, i.e., a baseline without the global affine correction ($\\alpha, \\beta$) to show the quantitative gain from incorporating the global affine term.\n\n\tSince DepthAnything, VGGT, and MoGe also predict relative depth as the authors mentioned, in Tables 1 and 5, it would further strengthen the paper to show their results after applying the same Poisson-based alignment used in this paper. Demonstrating that the current result of this paper still outperforms these baselines after identical alignment would make the contribution more compelling. Also, in table 2 (point map evaluation), it would further strengthen the evaluation if the authors could include comparisons with recent point map models such as VGGT or DUSt3R, even under RGB-only settings.\n\n\tAs shown in Appendix Tables 8 and 9, the performance degrades more noticeably than some baselines when depth noise is added. The paper lacks an analysis explaining this sensitivity, e.g., whether it arises from their framework design.\n\n\tThe inference time comparison is not discussed in the methodology, experimental results, or tables. Including the inference time would help verify the effectiveness and practicality of the proposed method.\n\nI will reconsider the score when all the concerns are handled well.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "8q77bvbmqu", "reviewer": "Reviewer_LhSg", "summary": "This paper presents the Large Depth Completion Model (LDCM), a novel framework for generating dense, metrically accurate depth maps from sparse and irregular depth inputs. The key innovation lies in a two-stage approach: first, it proposes a preprocessing pipeline that uses a pretrained depth foundation model to estimate a gradient field, which is then integrated via a Poisson solver to generate a coarse but geometrically coherent initial depth map. Second, for depth prediction, LDCM introduces the use of a pointmap representation—a 3D point cloud-like structure—to directly model scene geometry, enabling more precise depth completion.\n\nThe method achieves state-of-the-art performance across six major benchmark datasets, excelling in both depth completion and pointmap estimation tasks, with consistent top rankings on all evaluated metrics.", "questions": "N/A", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The paper is well-structured and transparent, offering comprehensive implementation details, including a thorough list of training datasets, which enhances reproducibility. Also, the code in the supplementary material shows the details of the model.\n2. The proposed gradient-field-based initialization using a foundation model and Poisson reconstruction effectively preserves geometric structure and metric scale, providing a strong starting point for refinement.\n3. This work is the first to introduce the pointmap as a core representation in depth completion, enabling direct 3D scene modeling and improving structural fidelity.\n4. Evaluation is comprehensive, covering multiple benchmarks, with detailed quantitative results, ablation studies, and comparisons that convincingly demonstrate the superiority of the proposed method.", "weaknesses": "1. While the quantitative performance is impressive, the paper would benefit significantly from more visual comparisons (e.g., side-by-side depth map visualizations) to intuitively illustrate LDCM’s advantages over baselines, especially in challenging regions like object boundaries or low-sampling areas.\n2. Recent advances in diffusion-based models have demonstrated strong performance in depth estimation and completion (e.g., [1][2]), despite their typically longer inference times. While LDCM may prioritize efficiency, including a comparison with these state-of-the-art diffusion methods would provide a more comprehensive benchmarking landscape.\n3. The training data includes synthetic and high-quality real datasets. However, it is crucial to discuss the impact of introducing more real-world data.\n4. The paper compares with monocular depth estimation models, which are inherently scale-ambiguous. Providing these details of scale alignment is crucial for fair and reproducible comparison.\n\nOverall, I’m inclined to accept this paper, but I encourage the authors to provide more comparison and ablation experiments.\n\n\n[1] Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion\n\n[2] DepthLab: From Partial to Complete\n\n\n Minor weakness\n\n1. There is a minor inconsistency in Table 8 (e.g., \"Unidepthv1\" vs. \"Unidepth V1\") that also appears in subsequent tables. This should be corrected for clarity and consistency.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "VCZMfKB1b5", "reviewer": "Reviewer_KZnL", "summary": "The paper introduces LDCM, a transformer-based model to estimate dense metric depth from a single image and a sparse depth. It proposed a Poisson-based preprocessing strategy that leverages a pretrained depth foundation model (Depth Anything V2) to produce an initial coarse depth map and replaced the conventional depth head with a pointmap head. Trained on a large collection of high-quality datasets, the proposed model demonstrates superior performance across various benchmarks under different sparse levels, outperforming previous state-of-the-art methods.", "questions": "Please see \"Weaknesses\" section.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The proposed model is simple but effective. It outperforms a recent SOTA baseline, such as PriorDA and PromptDA.\n2. Compared to previous coarse alignment method, the proposed Poisson-based strategy is effective, providing a strong initialization.\n3. The authors perform a detailed set of zero-shot experiments on multiple datasets, demonstrating the effectiveness of LDCM in both depth completion and point map estimation. The model consistently outperforms prior methods in a variety of settings.\n4. This paper is well written and the implementation details are comprehensive and easy to follow.", "weaknesses": "1. The main concern comes from the limited qualitative comparison. The manuscript mainly provides quantitative results, where the proposed LDCM ranks first on most of the benchmarks. The authors may consider adding more visualized results to better illustrate the performance differences and provide more intuitive insights into the effectiveness of the proposed LDCM.\n2. The inference time of each component should be mentioned to provide a comprehensive evaluation for the proposed model.\n3. Recently, there are lots of 3D reconstruction models which introduce additional depth priors (e.g., WorldMirror, MapAnything, Pow3R). To may understanding, the sparse prior provides both metric and relative geometry guidance. Although these methods generate relative geometry, the metric scale can be recovered by the proposed Poisson strategy or least square alignment. Comparing performance with these methods would provide a more comprehensive evaluation.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "GssADRIfUY", "reviewer": "Reviewer_TPZH", "summary": "This paper proposes the Large Depth Completion Model (LDCM) to address the problem of estimating dense and metrically accurate depth maps from sparse depth observations. It mainly involves a Poisson-based coarse depth initialization and a point map regression head for 3D structure prediction. The authors performs comprehensive evaluations across multiple benchmark datasets under varying sparsity of inputs, demonstrating state-of-the-art results.", "questions": "The sparse points shown in the head figure are difficult to visualize clearly. To improve clarity and provide a better qualitative analysis, it would be beneficial to plot the sparse points directly on the RGB images.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Robust Results. The extensive experiments on diverse benchmarks (including KITTI, ETH3D, DIODE, etc.) demonstrate that LDCM consistently outperforms existing methods, especially in zero-shot settings and under sparse input conditions. This indicates a high level of generalization and robustness of the proposed model.\n\n2. Extensive Training and Comprehensive Evaluation. The authors have invested considerable effort in training LDCM on a large number of datasets. The extensive evaluation spans a wide range of sparsity levels, providing a comprehensive comparison. \n\n3. Effective Preprocessing Method. The Poisson-based depth initialization strategy that incorporates geometric priors from monocular depth models to construct initial coarse depth is interesting.", "weaknesses": "1. Inference Time. While LDCM demonstrates excellent performance across multiple benchmarks, the model’s inference time has not been explicitly discussed. It would be useful to include an analysis of the model's runtime efficiency.\n\n2. More Comparison Methods. Pow3R introduces sparse priors to predict pointmaps, a direct comparison with the pow3r model is necessary.\n\n3. Visualization Results. Although the paper provides extensive quantitative evaluations, additional visualizations of the depth completion and point map estimation results would enhance the reader's understanding of the model’s performance.\n\n[1] CVPR 2025, Pow3R: Empowering Unconstrained 3D Reconstruction with Camera and Scene Priors", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)