Depth Anything with Any Prior
Authors: Zehan Wang
Siyu Chen
Lihe Yang
Jialei Wang
Ziang Zhang
Hengshuang Zhao
Zhou Zhao
Venue: ICLR 2026 Poster (2026)
Keywords: Depth Estimation
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: This work presents Prior Depth Anything, a framework that combines incomplete but precise metric information in depth measurement with relative but complete geometric structures in depth prediction, generating accurate, dense, and detailed metric depth maps for any scene. To this end, we design a coarse-to-fine pipeline to progressively integrate the two complementary depth sources. First, we introduce pixel-level metric alignment and distance-aware weighting to pre-fill diverse metric priors by explicitly using depth prediction. It effectively narrows the domain gap between prior patterns, enhancing generalization across varying scenarios. Second, we develop a conditioned monocular depth estimation (MDE) model to refine the inherent noise of depth priors. By conditioning on the normalized pre-filled prior and prediction, the model further implicitly merges the two complementary depth sources. Our model showcases impressive zero-shot generalization across depth completion, super-resolution, and inpainting over 7 real-world datasets, matching or even surpassing previous task-specific methods. More importantly, it performs well on challenging, unseen mixed priors and enables test-time improvements by switching prediction models, providing a flexible accuracy-efficiency trade-off while evolving with advancements in MDE models.

Published as a conference paper at ICLR 2026
DEPTH ANYTHING WITH ANY PRIOR
Zehan Wang',, Siyu Chen' Lihe Yang", Jialei Wang', Ziang Zhang', Hengshuang Zhao?, Zhou Zhao't
ZZhejingg University; "The University of Hong Kong
httpss//prrorr-eeeth-aanyn itw.arayhiiigggth...i...... github.io/
ABSTRACT
This work presents Prior Depth Anything, framework that combines incomplete but precise metric information in depth measurement with relative but complete geometric structures in depth prediction, generating accurate, dense, and detailed metric depth maps for any scene. To this end, we design coarse-to-fine pipeline to progressively integrate the two complementary depth sources. First, we in- troduce pixel-level metric alignment and distance-aware weighting to pre-fill di- verse metric priors by explicitly using depth prediction. effectively narrows the domain gap between prior patterns, enhancing generalization across varying scenarios. Second, we develop a conditioned monocular depth estimation (MDE) model refine the inherent noise of depth priors. By conditioning the nor- malized pre-filled prior and prediction, the model further implicitly merges the complementary depth sources. Our model showcases impressive zero-shot generalization across depth completion, super-resolution, and inpainting over 7 real-world datasets, matching even surpassing previous task-specific methods. More importantly, it performs well on challenging, unseen mixed priors and en- ables test-time improvements by switching prediction models, providing a flexible accuracy-efficiency trade-off while evolving with advancements in MDE models.
INTRODUCTION
Fine-detailed and dense metric depth information is a fundamental pursuit in computer vision and
robotics applications (Zhang et al.
2023a Deng et al. 2022 Chung
Genneetlle on (Daytt Aoythin))
Mecie Maranemen: (SMM Saisor, (ic)
ct al.] 2024; Roessle et al.
2022
Esser al.
[023) Wang et
2024
Prior Depth Anydhing
Zhen et al.,
2024; Wang et al. 2019
Wofk
et al. 2019 Wang et al. ZZhan et al., 2025; Wang
S Time desaile
P ignored detiils
Tine detolls
2025b
S complete content good geomeury
P incomplese content
complete content
ct al." 2025c 2023; Huang et al. Although monocular depth estimation (MDE) models (Ranft] eet al.]
missing geometry
good grometry
2024
invculed metric
precise metric
precise
Figure 1: Core Motivation. We progressively integrate com- plementary information from metric measurements (accurate metrics) and relative predictions (completeness and fine de- tails) to produce dense and fine-grained metric depth maps.
2020 Yang et al., 2024a
Ke et al. 2024; Bochkovskii et al.
2025
2025 Hu al. Yin et al.
He et al.
2024
2023 have made
significant progress, enabling complete and detailed depth predictions. predicted depths are relative and lack precise metric information. On the other hand, depth measurement technologies, such as Structure from Motion (SfM) (Schonberger Frahm 2016) or depth sensors (Lange & Seitz 2001), provide precise but often incomplete and coarse metric information.
"Equal Contribution. Corresponding Author.

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Published as a conference paper at ICLR 2026
Sparse Point
Low-
Missing Area
Methods
Target Task
Mixed
S/M LJDAR Extreme resolution
Range Shape Object
Marigold-DC (Viola al.
2024
Depth Completion
x
x
Omni-DC: (Zao et al. 2024
Depth Completion
1
/
x
X
X
PromptDA [Lmn ct al. 2025
Depth Susert-esolution
X
X
/
X
X
x
DepthLab Liu ot al.
2024a
Depth Inpainting
X
x
x
/
x
Prior Depth Anything
All-Rounder
V
/
Applicable scenarios of current prior-based monocular depth estimation models. SfM: sparse matching points from SfM, LiDAR: sparse LiDAR line patters, Extreme: extremely sparse points (100 points), Range: missing depth within a specific range, Shape: missing regular-shaped areas, Object: missing depth of an object.
Table 1:
In this paper, we explore prior-based monocular depth estimation, which takes RGB images and measured depth priors as inputs to output detailed and precise metric depth maps. We unify different depth estimation tasks by abstracting various types of depth measurements as depth prior. To clarify the scope, we first outline common types of depth priors and their primary applications:
1) Sparse points (depth completion):
Depth from LiDAR or SfM (Schonberger & Frahm 2016) is
typically sparse. Completing sparse depth priors crucial for applications such as 3D reconstruc- tion (Roessle et 2022, Chung al.) (024) and autonomous driving (Tao et al. 2022: Häne al.)
2017 Carraaza-Garefa et al. (022). 2) Low-resolution (depth super-resolution): Low-power Time of Flight (ToF) cameras (Lange & Seitz 2001), commonly used in mobile phones, capture
low-resolution depth maps. Depth super-resolution is essential for spatial perception. VR (Rasla &
Beyeler )022). and AR (Slater & Wilbur 1997) in portable devices (He al. 2021a 3) Miss-
ing areas (depth inpainting): Stereo matching failures (Lowe 2004 Rublee et al.] 2011) or 3D)
Gaussian splatting edits (Liu et al., 2024b; Yu al. 2024) may leave large missing areas in depth maps. Filling these gaps is vital for 3D scene generation and editing (Yu et al., 2024). 4) Mixed
prior: In real-world scenarios, different depth priors often coexist. For instance, structured light cameras (Herrera et al. 2012) often generate low- oww-resouution and incomplete depth maps, due to their limited working range. Handling these mixed priors is vital for practical applications.
In Tab. D we detail the patterns of each prior. Existing methods mainly focus on specific limited priors, limiting their use in diverse, real-world scenarios. In this work, we propose Prior Depth Any thing, motivated by the complementary advantage between predicted and measured depth maps, as illustrated in Fig. []] Technically, we design a coarse-to- fine pipeline to explicitly and progressively combine the depth prediction with measured depth prior, achieving impressive robustness to any image with any prior.
We first introduce coarse metric alignment pre-fill incomplete depth priors using predicted relative depth maps, which effectively narrows the domain gap between various prior types. Next, we apply the fine structure refinement to rectify misaligned geometric structures in the pre-filled depth priors caused by inherent noises in the depth measurements. Specifically, the pre-filled depth prior (with accurate metric data) and the relative depth prediction (with fine details and structure) are provided as additional inputs to a conditioned MDE model. Guided by the RGB image input, the model can combine the strengths of complementary depth sources for the final output.
We evaluate our model on 7 datasets with varying depth priors. It achieves zero-shot depth com- pletion, super-resolution, and inpainting within single model, matching or outperforming previous models that are specialized for only one of these tasks. More importantly, our model achieves sig- nificantly better results when different depth priors are mixed, highlighting its effectiveness in more practical and varying scenarios.
Our contribution can be summarized as follows:
We propose Prior Depth Anything, a unified framework to estimate fine-detailed and com- plete metric depth with any depth priors. Our model can seamlessly handle zero-shot depth completion, super-resolution, inpainting, and adapt to more varied real-world scenarios.
We introduce coarse metric alignment to pre-fill depth priors, narrowing the domain gap between different types of depth prior and enhancing the model's generalization.
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Published as a conference paper at ICLR 2026
We design fine structure refinement to alleviate the inherent noise in depth measurements. This involves a conditioned MDE model to granularly merge the pre-filled depth prior and prediction based on image content.
Our method exhibits superior zero-shot results across various datasets and tasks, surpassing even ofte-of-theearr methods specifically designed for individual tasks.
2 RELATED WORK
2.1 MONOCULAR DEPTH ESTIMATION
Monocular depth estimation (MDE) is fundamental computer vision task that predicts the depth of each pixel from a single color image (Eigen et al. 2014 Fu et al. 2018 Bhat al., (021). Recently
with the success of "foundation models" (Bommasani et al. [202] some studies (Ranfil et al. 2020
Yang et al. 2024a b Xu et al. 2025 He et al. 2025 Ke al. 2024,, Bochkovskii et al. 2025 Yin
al. 2023 Xinlai ai et al. 2026 have attempted to build depth foundation models by scaling up data
and using stronger backbones, enabling them to predict detailed geometric structures for any image.
MiDaS (Ranftl al. 2020) made the pioncering study by training an MDE model on joint datasets to
improve generalization. Following this line, Depth Anything v1 (Yang et al. (022a)) scaled training
with massive unlabeled image data, while Depth Anything v2 Yang et al. 2024b) further enhanced
its ability to handle fine details, reflections, and transparent objects by incorporating highly precise
synthetic data (Wang al. 2021 2020 Yao al. 2020 Cabon al. 2020 Roberts al. (021).
Although these methods have demonstrated high accuracy and robustness, they primarily produce unscaled relative depth maps due the significant scale differences between indoor and outdoor scenes. While Metric3D (Yin et al. [0233 Hu et al. (2024) and Depth Pro (Bochkovskii et al. 2025)
achieve zero-shot metric depth estimation through canonical camera transformations, the precision remains limited compared to mcasurement technologies.
Our method builds on the strength of existing depth foundation models, which excel precisely capturing relative geometric structures and fine details in any image. By progressively integrating accurate but incomplete metrie information in the depth measurements, our model can generate dense and detailed metric depth maps for any scene.
2.2 PRIOR-BASED MONOCULAR DEPTH ESTIMATION
In practical applications, depth measurement methods like multi-view matching (Cordts et al. 2016) or sensors (Silberman al.) 2012; Geiger al. 2013) can provide accurate metric information, but due to their inherent nature or cost limitations, these measurements often capture incomplete information. Some recent studies have tried to use this measurement data prior knowledge in the depth estimation process to achieve dense and accurate metric depth. These methods, however, primarily focus specific patterns of depth measurement, which can be categorized into three types based on their input patterns:
Depth Completion As noted in (Roessle et al. 2022), SfM reconstructions from 19 images often result in depth maps with only 0.04% valid pixels. Completing the sparse depth maps with observed RGB images is fundamental computer vision task (Tang et al., 2024 Cheng al. 2020; 2019 Zuo & Deng 2024 (Zhang et al., 20236 Park et al. 2024). Recent approaches like Omni-DC (Zo al. (2024) and Marigold-DCC (Vola et al. 2024) have achieved certain levels of zero-shot general- ization across diverse scenes and varying sparsity levels. However, due to the lack of explicit scene
geometry guidance, they face challenges in extremely sparse scenarios.
Depth Super-resolution Obtaining high-resolution metric depth maps with depth cameras usually demands significant power. A more efficient alternative is to use low-power sensors to capture low- resolution maps and then enhance them using ruper-tesolution. Early efforts (Zhong et al.) 2023] Xian al. 2020 Zhao et al. 2022 He et al. 2021b), however, show limited generalization unseen scenes. Recent PromptDA (Lin et al., (2025) achieves effective zero-shot super-iesolution by using the low-resolution map prompt for depth foundation models (Yang et al. 2024b).
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Published as a conference paper at ICLR 2026
pred
Coarse Metric Alignment (Explicit Fusion)
Fine Structure Refinement (Implicit Fuxion)
prior
D prior
Conditioned MDD
Scale
Condition Cons
Pixdl Algnment
Reweegling
Align
Figure 2:
Prior Depth Anything. Considering RGB images, any form of depth prior D priors and relative prediction D pred from a frozen MDE model, coarse metric alignment first explicitly com- bines the metric data in Price and geometry structure D Fine structure refinement implicitly merges the complementary information produce the final metric depth map.
Depth Inpainting As discussed in (Yang al. 2024b Ke et al. 2024), due to inherent limitations stereo matching and depth sensors, even "ground truth" depth data in real-world datasets often have significant missing regions. Additionally, in applications like 3D Gaussian editing and gener- ation (Liu et al. 2024b Yu et al. 2022 Chung et al. 2023), there is a need to fill holes in depth maps. Depthlaab (Liu et al. 20224) first fills holes using interpolation and then refines the results with a depth-guided diffusion model. However, interpolation errors reduce its effectiveness for large missing areas or incomplete depth ranges.
These previous methods have two main limitations: 1) Poor performance when prior is limited. 2) Difficulty generalizing to unseen prior patterns. Our approach, Prior Depth Anything, tackles these challenges by explicitly using geometric information from depth prediction in a coarse-to- lo-fine process, achieving impressive generalization and accuracy across various patterns of prior input.
PRIOR DEPTH ANYTHING
Advanced monocular depth estimation models excel in predicting detailed, complete relative depth maps with precise geometric structures for any image. In contrast, depth measurement technologies can provide metric depth maps but suffer from inherent noise and varying patterns of incomplete- ness. Inspired by the complementary strengths of estimated and measured depth, we introduce Prior Depth Anything to progressively and effectively merge the depth sources. To handle diverse real-world scenarios, we take measurement depth in any form as the metric prior, producing fine- grained and complete metric depth maps for any image with any prior.
3.1 PRELIMINARY
Given an RGB image I E R3xHxW and its corresponding metric depth prior D Dprior E HW prior-based monocular depth estimation takes the and D Dpiee as input, aiming to output the depth map D Douppur lfx that is detailed, complete, and metrically precise. We uniformly represent the coordinates of valid positions in D prior as {z,, yi which N pixels are valid.
3.2 COARSE METRIC ALIGNMENT
As shown in Fig. different types of depth priors exhibit distinct missing patterns (e.g. sparse points, low-resolution grids, or irregular holes). These differences in sparsity and incompleteness restrict models' ability to generalize across various priors. To tackle this, we propose pre-filling missing regions to transform all priors into a shared intermediate domain.
We first use frozen MDE model to obtain a relative depth prediction D pred E # Then, by explicitly utilizing the accurate geometric structure the predicted depth,
Pixel-level Metric Alignment
we fill the invalid regions in D peior pixel by pixel. Considering the pre-fille fillid coarse depth map D priors
which inherits all the valid pixels in D p
D prior (x, y) D pred (3r, y), where (x, v) E P
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Published as a conference paper at ICLR 2026
For each missing pixel (á, (). we first identify its k-nearest neighborhood (kNN) points {æR, yk ) from the valid pixel set P. Then, we compute the optimal scale 8 and shift t that minimizes the least- squares error between the depth values of D pred and D prior at the K supporting points:
K Σ R
s, t = arg min
- M1e) + D peior (Zk, 11)
(2)
s,t
With the optimal scale and shift t. we fill the missing pixels in D peior by linearly aligning the predicted depth value at (i, f) metric prior:
D prior (2, ()
pred (2, 0)
(3)
Distance-aware Re-weighting Although simple pixel-level metric alignment achieves reasonable accuracy and generalization, two limitations remain: 1) Discontinuity Risk: Adjacent pixels in miss- ing regions may select different k-nearest neighbors, resulting in abrupt changes. 2) Uniform Weight- ing: Nearby supporting points offer more reliable metric cues than distant ones, but equal weighting in the least squares overlooks this geometrical correlation, leading suboptimal alignment.
To handle this issue, we further introduce distance-aware weighting for more smooth and accurate alignment. Within the alignment objective of Eq. [ we re-weight each supporting point based on its distance to the query pixel, modifying Eq. to:
|s D Drrea (Trk Uk.) + D prior (Kk, yk 1
s, arg min 8,t
(4)
[1 1) (Z Vx2
This simple modification ensures smoother transitions between regions and improves robustness by emphasizing geometrically closer measurements.
In summary, by explicitly integrating the accurate metric information from D Dpiior and the fine ge- ometric structures from Dpedd we cultivate the pre-filled dense prior D Deiice which offers two main advantages: 1) Similar Pattern: Filling the missing area narrows the differences between various prior types, improving the generalization across different scenarios. 2) Fine Geometry: The filled regions, derived from linear transformations of depth prediction, natively preserve the fine geometric structures, significantly boosting performance when prior information is limited.
3.3 FINE STRUCTURE REFINEMENT
Although the prefilled coarse dense depth is generally accurate in metric, the parameter-free ap- proach is sensitive to noise in depth priors. A single noisy pixel on blurred edges can disrupt all filled regions relying it supporting point. To tackle these errors, further implicitly lever- age the MDE model's ability in capturing precise geometric structures in RGB images, learning to rectify the noise in priors and produce refined depth.
Metric Condition Specifically, we incorporate the pre-filled prior D priorr as extra conditions the pre-trained MDE model. With the guidance of RGB images, the conditioned MDE model is trained to correct the potential noise and error in D prior- To this end, we introduce condition convolutional layer parallel the RGB input layer, as shown in Fig. 2 By initializing the condition layer to zero, our model can natively inherit the ability of pre-trained MDE model.
Geometry Condition In addition to leveraging the MDE model's inherent ability in capturing ge- ometric structures from RGB input, we also incorporate existing depth predictions geometry condition help refine the coarse pre-filled prior. The depth prediction D pred from frozen MDE model, is also passed into the condition MDE model through zero-initialized conv layer.
external
Scale Normalization Then, we normalize the metric condition Dpiior and geometry condition Dpred to [0,1] for two key benefits: 1) Better Scene Generalization: different scenes (e.g. indoor vs. outdoor) have significant depth scale differences. Normalization removes this scale variance, im- proving performance across diverse scenes. 2) Better MDE Model Generalization: predictions from different frozen MDE models also have varying scales. Normalizing D peed enables test-time model
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Published as a conference paper at ICLR 2026
Model
Uncoder
NYUv2
ScanNet
ETH-3D
DIODE
KITTI
Ave.
S+M
L+M
S+L
S+M
L+M
S+L
S-M
L+M
S+L
S-M
L+M
S+L
S+M
L-M
S-L
Rank
DAv2
VIT-L
4.59
4,95
5.08
4.34
4.55
4.62
6.82
10.99
7.49
11.57
10.25
12.46
10.20
11.12
10.97
7.5
Depth Pro
VTT-
4.46
4.87
5.41
4.22
4.44
4.51
6.39
7.08
9.29
12.92
8.42
8.91
6.29
9.24
9.18
6.7
Omni-DC
2.86
3.26
3.81
2.88
2.76
3.64
4.59
4.23
5.40
4.36
8.63
Marigold.De DC
SDv2
2.26
3.38
3.82
2.19
2.87
3.37
2.15
4.77
5.13
4.98
6.73
7.25
5.82
9.67
10.05
5.1
DepthLab
SDv2
3.96
5.80
5.16
3.38
5.24
7.45
8.83
39.29
23.12
30,96
PromptDA
VIT.I
17.00
3.76
17.13
15:27
4.21
15.44
18.34
9.01
18.73
18:24
9.97
18.55
21.61
54.35
22.14
8.4
PriorDA
DAV--B+VIT-
2.09
2.88
3.17
2.14
2.56
2.94
1.65
3.96
4.16
3.76
4.43
4.89
3.97
8.38
8.53
2.9
DAv2-B-NIT-B
2.04
2.82
3.09
2.11
2.50
2,86
1.56
3.84
4,088
3.62
4.30
4.73
3.86
8.40
8.57
2.0
(ours)
Depth ViT-
2.01
2.82
3,08
2.06
2,48
2,82
1.61
3,83
4,05
3.40
4.23
4,60
3,37
8.12
8.25
1.1
Table 2: Zero-shot depth estimation with mixed prior. All results are reported in AbsRel 4. "S":
"Extreme" setting in sparse points, "L": "x 16" in low-Resolution, "M" "Shape" (square masks of 160) in missing area. "Depth Pro+ViT-B" indicates the frozen MDE and conditioned MDE. DAv2-
B: Depth Anything v2 ViT-B (Yang et al. 2024b), SDv2: Stable Diffusion v2. (Rombach et al.
2022). We highlight Best, second best results.
Model
Ensoder
NYUv2
ScunNet
ETH.3D
KITTI
Ave.
SIM
LiDAR Extreme SIM
LiDAR Extrem: SIM
LiDAR Extreme SIM
LIDAR
Extrme
SIM
LIDAR Estreme Rank
DAy
VIT-L
5.31
4,85
4.77
5.88
4.60
4.68
5.84
7.41
6.61
12.45
12.55
14.37
9.83
8.86
9.25
7.3
Depth Pro
VIT-L
4.80
4.47
4.41
5:27
4.12
4.23
5.68
5.31
6.51
10.53
9.08
8.98
6.45
6.05
6.19
6.1
Omni DC
2.87
2.12
2.63
6.09
2.02
2.71
2.57
1.58
1.98
4.99
3.96
4.13
3.34
5.27
4.17
3.7
Marigold-IC
SDv2
2.68
1.90
2.13
4.32
1.76
2.12
4.65
2.27
2:03
5.12
4.77
6.19
5.62
4.3
DepthLab
SDv2
5.92
4,30
6.30
9.87
3.56
5.09
13.82
6.40
8.01
16.67
7.45
8.66
25.91
37.17
40.29
7.6
PromptIA
ViT-L.
18.68
17.59
16.96
18.13
15.99
15.18
25 02
18.86
18.18
25,46
18.58
17.93
22.26
21,96
21 59
8.8
PAiorDA
(ours)
Depth Pro+NiT-B
DAVZ-RAVIT-B
2,42
2.36
2.38
2.01
1.95
1,96
1.97
2.01
1.96
3.85
3.90
3.84
2.14
2.15
2.19
2.09
2.01
2.04
3.26
3.10
3.13
1,90
1.84
1.82
1.50
1.54
1.61
5.07
5.25
3.77
3.66
3.88
3.37
3.65
3.73
3.35
3.74
4.81
4.12
4.23
3.76
3,76
3.28
3.3
2.2
1.7
Table 3: Zero-shot depth completion (e.g. sparse points prior). All results are reported in AbsRel
J. "SfM": points sampled with SIFT (Lowe) 2004) and ORB (Rublee et al.] (001T) following Omni- DC (Zuo et 2024), "LiDAR": 8 LIDAR lines, "Extreme": 100 random points.
switching, offering flexible accuracy-efficient trade-off's for diverse demands, and enabling seam- less improvements as MDE models advance.
Synthetic Training Data As discussed in (ee et al. 2024 Yang et al. 2024b). real depth datasets of- ten face issues such as blurred edges and missing values. Therefore, we leverage synthetic datasets, Hypersim (Roberts et al. 202T) and vKITTI (Cabon et al. 2020), with precise GT to drive our conditioned MDE model to rectify the noise in measurements. From the precise ground truth, we randomly sample sparse points, create square missing areas, or apply downsampling to construct different synthetic priors. To mimic real-world measurement noise, we add outliers and boundary noise to perturb the sampled prior, following (Zuo et al. 2024).
Learning Objective As mentioned earlier, both the metric and geometry conditions are normalized. Thus, we apply the de-normalization transformation to convert the output into the ground truth scale. Following ZoeDepth (Bhat et al.) 2023), we use the scale-invariant log loss for supervision.
4 EXPERIMENT
4.1 EXPERIMENTAL SETTING
During training, we utilize the Depth Anything V2 ViT-B model as the frozen MDE model to produce relative depth predictions. During inference, the frozen MDE model can be swapped with any other pre-trained model. The k-value of the ANN process in Sec. 3.2 is set to We initialize the conditioned MDE Model with two versions of Depth Anything V2: VIT-S and ViT-B. We train the conditioned MDE model for 200K steps with a batch size of 64, using 8 GPUs. The Adam W optimizer with a cosine scheduler is employed, where the base learning rate is set to 5e-6 for the MDE encoder and 5e-5 for the MDE decoder.
Implementation Details
Benchmarks Our method aims to provide accurate and complete metric depth maps in a zero-shot manner for any image with any prior. To cover "any image", we evaluate models on 7 unseen real- world datasets, including NYUv2 (Silberman al. (012)) and ScanNet (Dai et al.) 2017) for indoor, ETH3D (Schops et al.) 2017) and DIODE ( Vasiljevic et al. 2019) for indoor/outdoor, KITTI (Geiger al. 2012) for outdoor, ARKitScenes (Baruch et al. 2021) and RGB-D-D (He etal) 2021b) for cap-

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Published as a conference paper at ICLR 2026
Model
Encoder
ARKitScenes
RGB-I-D
NVUv2
Nean
ETH-3D
DIODE
KITTI
Ave.
AbsRell
RMSEL
AbsRall
RMSEL
8x
16x
8x
16x
8x
16x
8x
16x
8x
16x
DAv2
VIT-L
3.67
0.0764
4.67
0.1116
4.77
5.13
4.64
4.85
6.27
7.38
12.49
11.20
9.54
11.22
8.9
Depth Pro
VIT-L
3:25
0.0654
4.28
0.1030
4,48
4.83
4.17
4,40
5.88
6.79
8.20
8.33
6.76
9.16
7.8
Omai-DC
2.14
0.0435
2.09
0.0685
1,57
3.11
1.29
2.65
1.66
4.09
2.81
4.71
4.05
8.35
3.9
Marigold-DCC
S1N2
2.17
0.0448
2.15
D.672
1,83
3.32
1.63
2.83
2.33
4.75
4.28
9,47
6.2
Depihalab
SID2
2.10
0.0H11
2.13
0.0624
2.60
3.73
1.89
3.19
2.60
4.50
4.42
6.16
17.17
22, 90
6,4
PrompiDA
VIT-L
1.34
0.0547
2.79
0.0708
1.61
1.75
1.87
1.93
1.80
2.56
3.18
3.73
3.92
4.95
2.6
PrioeDA
(ours)
Depth Pro+VIT
DAVE-R-VIER
DAVZ-B+VIE-S
2.09
1.95
1.94
0.0414
0.0404
0.0408
2.02
2.07
0.0597
0.0581
0J0581
1.72
1.73
1.72
2.74
2.79
2.73
1660
1,58
1.61
2.43
2.50
2.45
1.99
2.06
3.91
3.77
3.79
3.01
3.09
3.10
4.36
4.15
4.44
4.54
8.24
8.20
7.99
2.3
3.9
Table 4: Zero-shot depth super-resolution (e.g. low-resolution prior). ARKitScenes and RGB-D-
D provide captured low-resolution depth. For other datasets. the low-resolution maps are created by downsampling the GT depths by 8 or 16 times, and the results are reported in AbsRel +..
Model
Encader
Runge
NYUv2
Shape
Object
Range Shape Object
ScanNet
Range
ETH-3D
Shape
Object
Range
DIODE
Shape
Object
Range
KITTI
Shape
Object
Rank
Ave.
DAv2
VIT-L
17.40
5.24
6.56
16.75
4,64
6,74
68.76
8.23
19.22
51.55
29.20
13.41
31.12
14,93
17.94
6.4
Depth Pro
VIT-L
10.89
9.20
6.52
16.76
15.39
6.80
10.37
34,28
17.28
37.44
34.74
13.53
14,51
16.11
8.19
5.4
Omni-DC
23.24
5.94
13.79
22.89
5:44
8.71
29.47
4.81
17.97
38.83
7.75
25.43
35:42
8.94
15.06
6.6
Mariood-DCC
SDv2
19.83
2.37
6.18
17.14
1.97
6.66
25.36
2.15
7.72
39.33
7.59
18.97
33.44
9.21
7.72
4.7
DepthLab
SDv2
23.85
21.17
30.61
41.01
17.17
PrompiDA
VIT-L
36.67
20.88
23.14
35.86
17.87
21.89
46.21
24.94
27.42
49.50
25.66
28.29
55.79
32.74
38.29
8.7
PriorDA
DAVE-B-VITSS
16.85
2.30
5.72
14.29
2.01
5.87
21.16
1.98
6.52
36.59
5.58
10.77
30.04
6.67
7,99
2.7
(ours)
Depth Pro+NiT-B
DAvZ-RAVIT-R
16.31
16.61
2.17
5.59
5.49
14.18
1,99
1,98
5.87
5.73
22.72
1.76
6.21
34,90
4.86
11.99
9.72
30.44
30.79
5.47
6.04
7.52
2.3
1.9
Table 5: Zero-shot depth inpainting (e.g. missing area prior). All results are reported in AbsRel
J. Metries are calculated only on the masked (inpainted) regions. "Range": masks for depth beyond 3m (indoors) and 15m (outdoors), "Shape": average result for square masks of sizes 80, 120, 160, and 200, "Object": object segmentation masks detected by YOLO (Jocher et al. 2023).
tured low-resolution depth. To cover "any prior", we construct 9 individual patterns: sparse points (SIM, LiDAR, Extremely sparse), low-resolution (captured, x8, x16), and missing areas (Range, Shape, Object). We also mix these patterns to simulate more complex scenarios.
Baselines We compare with two kinds of methods: 1) Post-aligned MDE: Depth Anything v2 (DAv2) (Yang et al.
(024b) and Depth Pro (Bochkovskii et al. 2025); and 2) Prior-based MDE:
Omni-DC (Zuo et al. 2024), Marigold-De (Viola et al. 2024), DepthLab (Liu et al. 2024a) and
PromptDA (inn et al.
2025).
4.2 COMPARISON ON MIXED DEPTH PRIOR
We quantitatively evaluate the ability to handle challenging unseen mixed priors in Tab In terms of absolute performance, all versions of our model outperform compared baselines. More importantly, our model is less impacted by the additional patterns. For example, compared to the setting that only uses sparse points in Tab 3 adding missing areas or low-resolution results in only minor performance drops (1.96,2.01, 3.08 in NYUv2). In contrast, Omni-DC (2.63+2.886 3,81), and Marigold-DC (3.13-22.28 3.82) show larger declines. These results highlight the robustness of our method.
4.3 COMPARISON ON INDIVIDUAL DEPTH PRIOR
Zero-shot Depth Completion Tab 3 shows the zero-shot depth completion results with different kinds and sparsity levels of sparse points as priors. Compared to Omni-DC (Zuo et al. 2024 and Marigold-Do (Viola et al. 2024). which are specifically designed for depth completion and rely on sophisticated, time-consuming structures, our approach achieves better overall performance with simpler and more efficient designs.
Zero-shot Depth Super-resolution In Tab 4 we present results for super-resolution depth maps. On benchmarks where low-resolution maps are created through downsampling (e.g. NYUv2 (Silber- man et al. 2012), ScanNet (Dai et al. (017), etc), our approach achieves performance comparable to -fthe-tf methods. However, since downsampling tends to include overly specific details from the GT depths, directly replicating noise and blurred boundaries from GT leads to better results instead. Therefore, AR ARtiSSeenes (Baruch et al., (22TI) and RGB-Da -DD (He et al., (022bb are more representative and practical, as they use low wwwpowwr cameras to capture the low row-resouution depths. On these two benchmarks, our method significantly outperform other zero-shot methods.
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RGB image
Ours
Marigold-DC
Omni-DC
PromptDA
DepthLab
Figure 3: Qualitative comparisons with previous methods. The error map (GT vs. prediction) is shown directly below each prediction. The used depth prior is provided under the RGB image.
RGB image
GT & Prior
Ours
Error
Figure 4: Error analysis on widely used but indeed noisy benchmarks (Silberman et al. al. 2017). Red means higher error, while blue indicates lower error.
2012 Dai
Zero-shot Depth Inpainting Tab 5 we evaluate the performance of inpainting missing regions in depth maps. In the practical and challenging "Range" setting, our method achieves superior results, which is highly meaningful for improving depth sensors with limited effective working ranges. Additionally, it outperforms all alternatives in filling square and object masks, demonstrating its potential for 3D content generation and editing.
4.4 QUALITATIVE ANALYSIS
In Fig provide qualitative comparison of the outputs from different models. Our model consistently outperforms previous approaches, offering richer details, sharper boundaries, and more accurate metrics.
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$
S+M
L+M S+I.
Metric Geometry
S
L
M
S+M
L+M
S+L
Interpolation
7.93 3.88
8.96
8.38
4.55
7.99
5.46 5.29
5.48
5.36
5.30 5.46
Ours (w/o re-weight)
2.92 3.44 6.91 3.22
4.53
4.36
x
2.10 2.94
2.58
2.17
3.02
3.31
Ours
2,42 3.51 6.70 2.60 4.32 4.40
1.96 2.74 2.48 2.01
2.82
3.08
Table 6: Accuracy of pre-filled depth maps with different strategies. We compare the pre-filled dense prior (i.e., Dpeioe) with ground truth.
Table 7: Effect of each condition for condi- tioned MDE models.
Seen
Unseen
Model
Eneoder
S
S+M
S+I.
S
L
M
S+M
L+M
S+L
ViT-S
2.15 2.77 2.68
2.22
2.87 3.20
None
2.50
3.71
46.07
2.50
3.74
3.64
Depth
VIT-B
1.97 2.73
2,50
2.02
2.82 3.09
Interpolation
3,40
2.68
4.28
3.53
2.94
3.56
Anything V2
ViT-I.
1.92 2.71 2.29
1.97
2.79 3.04
Ours (w/o re-weight)
2.13 2.86
2.58
2.19
2.94
3.25
ViT-G
1.87 2.70 2.22
1994
2.76 3.02
Ours
1.99 2.82
2.26
2.06
2.90
3.11
Depth Pro
VIT-L 1.96 2.74 2.35 2.01 2.82 3.08
Table 9: Effect of pre- pre-filled strategies on gener- alization. We train models with various pre-fill strategies using only sparse points and evaluate their ability to generalize to unseen types.
Table 8: Effect of using different frozen MDE models. The conditioned MDE model is ViT-B
version here.
Fig 4 visualizes the error maps of our model. The errors mainly occur around blurred edges in the "ground truth" of real data. Our method effectively corrects the noise in labels while aligning with the metric information from the prior. These "beyond ground truth" cases highlight the potential of our approach in addressing the inherent noise in depth measurement techniques. More visualizations can be found in the supp.
4.5 ABLATION STUDY
We use Depth Anything V2 ViT-B as the frozen MDE and ViT-S as the conditioned MDE for abla- tion studies by default. All results are evaluated on NYUv2.
Accuracy of different pre-fill strategy As shown in Tab our pre-fill method outperforms simple interpolation across all scenarios by explicitly utilizing the precise geometric structures in depth prediction. Additionally, the re-weight mechanism further enhances performance.
Effectiveness of fine structure refinement Comparing the pre-filled coarse depth maps in Tab with the final output accuracy in Tab 3 and 2 the performance improvements after fine struc- ture refinement (sparse points: 2.42 +2.01, low-resolution: 3.51-2.77, missing areas: 6.70-+2.48, S+M: 2.60-22009, L+M: 4.32-+2.88, S+L: 4.40-3.177 17) demonstrate its effectiveness in rectifying misaligned geometric structures in pre-filled depth maps while maintaining its metric information.
Effectiveness of metric and geometry condition We evaluate the impact of metric and geometry guidance for the conditioned MDE model in Tab The results show that combining both conditions achieves the best performance, emphasizing the importance of reinforcing geometric information during the fine structure refinement stage.
Testing-time improvement We investigate the potential of test-time improvements in Tab 8 Our findings reveal that larger and stronger frozen MDE models consistently bring higher accuracy, while smaller models maintain competitive performance and enhance the efficiency of the entire pipeline. These findings underscore the flexibility of our model and its adaptability to various scenarios.
Pre-fill strategy for generalization From Tab 9 we observe that our pixel-level metric alignment helps the model generalize to new prior patterns, and the re- weighting strategy further enhances the robustness by improving the accuracy of the pre-filled depth map.
CONCLUSION
In this work, we present Prior Depth Anything, a robust and powerful solution for prior-based monocular depth estimation. We propose coarse-to-fine pipeline to progressively integrate the metric information from incomplete depth measurements and the geometric structure from relative depth predictions. The model offers three key advantages: 1) delivering accurate and fine-grained

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Published as a conference paper at ICLR 2026
depth estimation with any type of depth prior; 2) offering flexibility adapt to extensive applica- tions through test-time module switching, and 3) showing the potential to rectify inherent noise and blurred boundaries in real depth measurements.
ACKNOWLEDGEMENTS
This work was supported by National Natural Science Foundation of China under Grant (No.62572423, No. No.624B2128, No.62422606), Hong Kong Research Grant Council General Re- search Fund (No. and State Grid Zhejiang Electric Power Cooperation Technology Project (No.B31IDS240012)
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A
APPLICATION
Monocular Depth Estimation
Multi-view Depth Estimation
NYU
ETH-3D
KITTI
ETH-3D
KITTI
VGGT
3.54 (-)
4.94 (-)
6,56 (-)
2.46 (-)
18.75 (-)
+Omni-D
4.12 (0.58)
6.08 (1.14)
6.85 (0.29)
2.64 (0.18)
18.66 (-0.09)
+Mrrigold-DC:
4.06 (0.52)
5,43 (0.49)
7.63 (1.07)
2.81 (0.35)
18.86 (0.11)
DaptthLab
3.56 (0.02)
4.92 (-0.02)
7.97 (1.41)
2.25 (-0.21)
19.47 (0.72)
ProomptD
3.43 (-0.11)
4.97 (0.03)
6.50 (-0.06)
2.48 (0.02)
18.91 (0.16)
+PriorDA
3.45 (-0.09)
4.43 (-0.51)
6.39 (-0.17)
1.99 (-0.47)
18.61 (-0.14)
Table 10: Results of refining VGGT depth prediction with different methods. All results are reported as AbsRel.
To demonstrate model's real-world applicability, employ prior-based monocular depth esti- mation models to refine the depth predictions from VGGT Wang et al. (2025a), a state-of-the-art 3D reconstruction foundation model. VGGT provides both depth and confidence map. We take the top 30% most confident pixels as depth prior and apply different prior- prior-based models to obtain finer depth predictions.
Table reports VGGT's performance in monocular and multi-view depth estimation, along with effectiveness of different prior-based methods as refiners. We observe that only our PriorDA consistently improves VGGT's predictions, primarily due to its ability to adapt to diverse priors. These surprising results highlight PriorDA's broad application potential.
B INFERENCE EFFICIENCY ANALYSIS
Model
Encoder
Param
Latency(ms)
Omni-DC
85M
223
Marigold-DC
SDv2
1,290M
28,607
DepthLab
SDv2
2,080M
9,078
PromptDA
ViT-L
340M
108
DAv2-B-ViT-S
97M+25M
79 49-9-21
PriorDA
DAv2-B+ B+TT-B
97M+98M
107 49,9149
(ours)
Depth PIO+VIT-B
952M+98M
941 883+9-19
Table 11: Analysis of inference efficiency. "xtx*x represents the latency of the frozen MDE model, coarse metric alignment, and conditioned MDE model, respectively.
In Tab 11 we analyze the inference efficiency of different models on one A100 GPU for an image resolution of x80.600. Overall, compared to previous approaches, our model variants achieve lead- ing performance while demonstrating certain advantages in parameter number and inference latency. For more detailed breakdown, provide the time consumption for each stage of our method. No- tably, the coarse metric alignment, which relies on kNN and least squares, introduces negligible overhead. Our method demonstrates significant efficiency advantages over previous methods.
C MORE ABLATION STUDY
Effect of k-value in Coarse Metric Alignment In Tab 12 we analyze the impact of the k-value on the accuracy of the final output Duupun Overall, method is non-sensitive the selection of k-value, with most selections yielding strong structural results. This highlights the effectiveness of the nearest neighbor approach in preserving detailed metric information.
For models less adept at handling missing pixels (Depthl lab, PromptDA), the entire VGGT depth prediction was provided as prior.
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L
S+M
L+M
S+L.
k=3
2.00
2.52
2.74
2.10
2.83
3.07
k=5
1.97
2.16
2.73
2.04
2.82
3.09
k=10
2.00
2.31
2.74
2.09
2.83
3.12
k=
2.10
2.27
2.76
2.16
2.83
3.14
Table 12: Impact of different k-value on the accuracy of the final output D output-
NYU
S
M
S+M
L+M
S+L
Omni-DC
2.6
3.1
6.6
2.8
3,3
3.8
PromptDA
17.0
1.8
21.4
17.0
3.8
17.1
PriorDAsp.only
2.0
2.8
2.3
2.1
2.9
3.1
PLiorDA
2.2
3.0
3.4
2.4
3.3
3.7
PriorDAALL
2.0
2.7
2.5
2.0
2.8
3.1
Table 13: Impact of used prior patterns during training.
Trained on Single Prior PriorDA trained with sparse point low-resolute resouusottion prior is compared
with baselines in Tab. 13 showing advantages over baseline trained with indicates that method's ability to generalize any form of prior stems from the coarse metric alignment, rather than the use of multiple patterns during training.
same single prior. This
D MORE QUALITATIVE RESULTS
To further explore the boundaries our model's capabilities and its potential to rectify the "ground truth" depth, offer more analysis with different patterns of priors the 7 unseen datasets (Figure for RGB-D-D, Figure 6 for ARKitScenes, Figure 7 for NYUv2, Figure g for ScanNet, Figure for ETH-3D, Figure DIODE, Figure I1 for KITTI).
E MORE TRAINING DETAILS
For each training sample, randomly select one of three patterns (c.g.. sparse points, low- resolution, or missing areas) with equal probability to sample the depth prior from the ground truth depth map. Specifically: for sparse points, we randomly select 100 2000 pixels valid; for low-resolution, we downsample the GT map by a factor of 8; and for missing areas, we generate random square mask with side length of 160 pixels. using multiple patterns or only using sparse points lead to similar results, as shown in Tab [3] This indicates that our method's 's ability to generalize any form of prior stems from the coarse metric alignment, rather than the use of multiple patterns during training.
worth mentioning that, find that
F MORE DISSCUSION
Why PromptDA preforms poor in mixed prior, sparse point and missing area? PromptDA only trained low-resolution but complete priors. Therefore, it struggles handle incomplete priors, even if we already apply DepthLab's pre-processing to fill in missing areas.
G USAGE OF LARGE LANGUAGE MODELS
In this work, we employed Large Language Models (LLMs) in the writing phase for sentence-level polishing.
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H LIMITATIONS AND FUTURE WORKS
Currently, our largest conditioned MDE model is initialized with Depth Anything v2 ViT-B. Given that larger versions of the Depth Anything v2 model exhibit stronger capabilities, training condi- tioned MDE models based on larger backbones is an important direction for future work. Addition- ally, following Depth Anything, all training images are resized to 518x518. In contrast, PromptDA is natively trained at 1440x 1920 resolution. Therefore, training at higher resolutions to better handle easily accessible high-resolution RGB images another crucial direction for our future research.
RGB image
GT & Prior
Ours
Error
Figure 5: Error analysis RGB-D-D.
RGB image
GT & Prior
Ours
Error
Figure 6: Error analysis ARKitScenes.
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RGB image
GT Prior
Ours
Error
Figure 7: Error analysis on NYUv2.
RGB image
GT & Prior
Ours
Error
Figure 8: Error analysis on ScanNet.
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RGB image
GT & Prior
Ours
Error
Figure 9: Error analysis ETH-3D.
RGB image
GT & Prior
Ours
Error
Figure 10: Error analysis on DIODE.
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Published as a conference paper at ICLR 2026
RGB image
GT Prior
Ours
Error
Figure 11: Error analysis on KITTI.
RGB
Ours
Marigold-DC
Omni-DC
Prompt-DA
DepthLab
Figure 12: Qualitative comparison on NYU.
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Published as a conference paper at ICLR 2026
RGB
Ours
Ma
OmniDC
PromptDA
DepthLab
Figure 13: Qualitative comparison on DIODE.
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Published as a conference paper at ICLR 2026
RGB
Ours
Marigold-DC
Omni-DC
Prompt-DA
DepthLab
Figure 14: Qualitative comparison on RGBDD.
RGB
Ours
Marigold-DC
Omni-DC
Prompt-DA
DepthLab
Figure 15: Qualitative comparison on ScanNet.
22


Reviews:
[{"review_id": "hdQrDBEFjW", "reviewer": "Reviewer_Zgrm", "summary": "The paper introduces a framework for prior-based monocular depth estimation, which accepts RGB images and depth priors (e.g. LiDAR or SfM in depth completion, low-resolution Time of Flight camera depth maps, incomplete depth maps, etc.) and outputs a dense metric depth map. Instead of focusing on any particular prior, this work proposes a unified method that can generate metric depth from any of these prior sources. The method consists of two stages. The first generates a coarse depth map based on depth priors and the predictions from a pre-trained monocular depth model (e.g. Depth Anything V2 is used in training experiments). This is done with the proposed \"pixel-level metric alignment\", where for each missing pixel, a set of $k$ nearest neighbors present in the prior are computed. These set of points are then used to compute a scale and shift parameter which best aligns (weighted by distance to the query pixel) these points with that predicted by the frozen MDE. The scale and shift parameters used to compute the missing pixel's values from the frozen MDE prediction. The second stage aims to improve robustness against noise in priors using another conditioned MDE. The input condition to this MDE is generated from RGB input, normalized frozen MDE prediction, and the normalized coarse prediction from the first stage via convolution layers. The output is then de-normalized to obtain the final predictions. The model is trained with synthetic datasets using several augmentations / corruptions to generate synthetic priors, and then evaluated zero-shot across multiple different real-world tasks with different sources of priors, across multiple datasets, and shown overall to perform comparably or better than existing methods.", "questions": "- The authors provided an intuition for the two-stage approach, where the first seems to provide a coarse (possibly noisy) estimate, and the second refines this. How strongly is this observed is this in practice? In the extreme case, how robust is the method given a very noisy (or perhaps even randomly initialized) depth prior map, would it be possible to at least recover the performance of that from the pre-trained MDEs?\n\n- The authors show that it is possible to stack multiple (2) MDEs to produce better depth predictions. I wonder if this is possible to improve results further at test-time by applying the conditional MDE in a recursive manner (i.e. repeatedly feeding in the output of the conditioned MDE in place of $\\hat{D}_{prior}$ back into itself), which can allow using \"adaptive\" inference-time compute to produce outputs at various levels of refinement.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 3, "strengths": "- Extensive experiments are conducted to show that the method performs strongly across different sources of depth priors, which is shown to be a key strength of the model compared to existing works. The proposed method convincingly generalizes significantly better compared to existing state-of-the-art approaches.\n\n- The pixel-level alignment method for in-painting missing depth regions seems novel, and shown to be significantly better than naive interpolation in Table 6 and 9. There is reason to believe that this insight can be generalized to improve other works in the field.\n\n- Extensive ablations are also performed, ablating the different input conditions for the conditioned MDE model (Table 7), different frozen MDE models (Table 8), and in-painting strategy (Table 6 and 9).", "weaknesses": "- The method first requires using a heuristic-based approach for densifying a depth prior. This might not be effective or practical especially when the prior map is extremely sparse, from both a latency (since densification requires solving a least-square regression for each missing pixel) and performance (the nearest neighbors might be extremely far away from the query pixel) standpoint.\n\n- Minor: I am not sure whether it is an issue with my PDF viewer, but the formatting of the paper seems off, especially in the first page. The hyperlinks seem to jump all over the place, and overlays / occludes existing text.", "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": "KONN0a0X2P", "reviewer": "Reviewer_thPV", "summary": "This paper presents a framework that integrates incomplete but accurate metric depth measurements with complete yet relative geometric predictions from monocular depth estimation (MDE) models to generate detailed and metrically consistent depth maps. The motivation stems from the complementary nature of these inputs: MDE produces dense, fine-grained relative depth but lacks absolute scale, whereas metric measurements provide precise scaling information but are often sparse, low-resolution, or partially missing. The proposed method adopts a coarse-to-fine pipeline that first performs pixel-level metric alignment, followed by a conditioned MDE refinement stage. The framework demonstrates strong zero-shot generalization across depth completion, super-resolution, and inpainting tasks on seven real-world datasets.", "questions": "1. What specific problem does the proposed distance-aware weighting (line 229) aim to solve? The authors are encouraged to provide clearer intuition or theoretical justification for this formulation.\n2. Why are recent baselines such as SharpDepth [1] not included in the comparison?\n3. How were the baseline methods implemented, particularly with respect to the use of different types of depth priors? Additional implementation details would improve clarity and reproducibility.\n4. It would strengthen the paper to include more qualitative comparisons with other benchmarks—ideally 10–20 diverse examples—to better illustrate the method’s generalization ability and visual performance.\n\n[1] Pham, D.-H., Do, T., Nguyen, P., Hua, B.-S., Nguyen, K., & Rang, N. (2024). SharpDepth: Sharpening Metric Depth Predictions Using Diffusion Distillation. arXiv preprint arXiv:2411.18229.", "limitations": "", "rating": 2, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 1, "strengths": "1. The paper presents extensive experiments that effectively validate the proposed method and justify its performance against benchmark approaches.\n2. The proposed framework achieves enhanced depth estimation quality through a simple and well-structured pipeline.", "weaknesses": "1. The proposed method shows limited novelty, as it primarily predicts per-pixel scale and shift values in MDE to align with the depth prior.\n2. The paper states, “We highlight best and second-best results” in the quantitative results section; however, only a few columns in Tables 2–5 are correctly annotated. The remaining columns contain inconsistencies, including missing annotations, unclear labeling, or incorrect markings.\n3. The reported quantitative and qualitative results do not convincingly demonstrate the proposed method’s superiority over baseline approaches. Specifically, the method fails to outperform in:\n    + 14 out of 16 experiments in Table 4,\n    + 4 out of 16 experiments in Table 5,\n    + 5 out of 16 experiments in Table 3, and\n    + 1 out of 3 comparisons in Figure 3 (the second experiment).\n\nThese results indicate that the proposed method underperforms in a significant portion of the evaluations.\nOverall, the improvements reported do not sufficiently support the claimed performance gains.", "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": "JAjrlKprmt", "reviewer": "Reviewer_uZ3K", "summary": "This paper proposes Prior Depth Anything, a unified framework that fuses precise but incomplete metric depth priors (e.g., sparse points, low-resolution maps, masked regions) with complete but relative monocular depth predictions to produce dense and accurate metric depth. The pipeline is coarse-to-fine:(i) Pixel-wise scale–shift alignment with distance-aware weighting fills missing regions using a frozen monocular predictor, reducing gaps among different prior types;(ii) A conditioned monocular depth network refines results from the RGB image, the pre-filled prior, and the prediction (normalized), mitigating measurement noise and reconciling geometry.Experiments on seven datasets show strong zero-shot results in completion, super-resolution, and inpainting, with robustness to mixed priors. Test-time backbone swapping supports accuracy–efficiency trade-offs. Overall, the design provides a clear fusion recipe and broad, competitive performance across settings.", "questions": "Effect of predictor quality: How do metrics change with lighter/faster monocular predictors versus the default, and with different alignment settings? Please provide an accuracy–throughput curve and key ablations.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "Broad applicability and novelty: One framework handles completion, upsampling, inpainting, and their combinations, covering common real-world inputs that previous methods often treat separately.\n\nStrong empirical performance: Consistent zero-shot results across multiple datasets and tasks, often matching or surpassing task-specific baselines without per-task fine-tuning.\n\nRobust to mixed priors: Maintains accuracy when prior types co-occur (e.g., sparse + low-res + holes), a challenging but practical scenario.\n\nClear integration pipeline: Pixel-level metric alignment plus a conditioned refiner is simple, well-motivated, and effective in practice; normalization enables flexible backbone choices.", "weaknesses": "Efficiency underreported: The two-stage design (predictor + kNN-style alignment + conditioned refiner) lacks detailed latency, memory, and component-wise cost, leaving deployability unclear.\n\nAblations and sensitivity limited: The impact of weaker/faster predictors, distance-aware weighting, neighborhood size, or removing coarse alignment is not fully quantified.", "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": "Qnsm6zPaZF", "reviewer": "Reviewer_pbPR", "summary": "This work proposes a metric-scale depth completion framework that can handle various types of sparse depth measurements, including LiDAR-like, SfM, masked, or range-out. The method combines affine-invariant depth estimation (referred to as geometric estimation in the paper) and metric measurement into a single pipeline to take advantage of both geometric consistency and metric accuracy. The framework is composed of two sequential stages: a monocular depth estimation (MDE) model and a conditional MDE model. The latter takes as input the RGB image, the output of the first MDE, and the filled metric measurements. Under the proposed evaluation setup, the model shows strong zero-shot performance in depth completion, super-resolution, and inpainting tasks. Ablation studies on how to fill metric measurements and how to configure the conditional MDE input further justify the design choices.", "questions": "1. For the de-normalization process converting the predicted depth to metric scale, could the authors clarify whether the de-normalization process corresponds to applying the inverse of the normalization function used for metric measurements?\n2. In Section 4.5 (Effectiveness of Metric and Geometry Condition and Table 7), was the training setup identical to the main experiments?\n3. In Line 313, the paper mentions training with 8 GPUs. Which GPU model was used? and how long does it take?\n4. In Section 4.5, is the conditional MDE trained using a single MDE backbone, or are multiple backbones used for the ablation study in Table 8?\n5. There appears to be a typo in Line 455. It likely refers to Table 5 instead of Fig. 6.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 4, "contribution": 3, "strengths": "1. The proposed pipeline, which sequentially connects an MDE and a conditional MDE, may appear simple but is both reasonable and effective, as it integrates the strengths of geometric estimation and metric measurement within a unified framework.\n2. Through ablation studies on pre-filled depth and the input configuration of the conditional MDE, the authors validate their design choices. The final model achieves consistently strong performance across diverse evaluation settings.", "weaknesses": "1. Experimental details\n- The experimental section lacks sufficient detail regarding how different types of degraded depth data were generated. Specifically, it is unclear how sparse points (from SfM, LiDAR, or extremely sparse settings), low-resolution inputs (captured, ×8, ×16), and missing-area cases (range, shape, object) were obtained for each dataset. I would recommend providing explicit descriptions in the supplementary material on how these variations were constructed per dataset.\n- The comparison table does not include citations for the baseline methods, and some references are missing from the main text as well. The visual emphasis in the table (e.g., bold, underline, gray shading) is inconsistent. What does “gray” indicate?\n\n2. Figures and presentation clarity\n- The visual presentation in Figures 3 and 4 is somewhat confusing. It is not entirely clear whether the error maps correspond to differences between output of proposed method and GT. It would be better to make the visualization and caption clearer to avoid misinterpretation.\n\n3. Evaluation protocol and additional benchmarks\n- While the proposed evaluation setup demonstrates strong performance, it would strengthen the paper to include additional experiments following the evaluation setups used in Marigold-DC or Zero-DC [a1], particularly on the VOID dataset. If feasible, including results on IBims would also help assess the outstanding performance of proposed method.\n\n[a1] Hyoseok et al., \"Zero-shot Depth Completion via Test-time Alignment with Affine-invariant Depth Prior\", AAAI 2025.", "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)