MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details
Authors: Ruicheng Wang
Sicheng Xu
Yue Dong
Yu Deng
Jianfeng Xiang
Zelong Lv
Guangzhong Sun
Xin Tong
Jiaolong Yang
Venue: NeurIPS 2025 poster ()
Keywords: computer vision
monocular geometry estimation
Primary area: applications

Abstract: We propose MoGe-2, an advanced open-domain geometry estimation model that recovers a metric-scale 3D point map of a scene from a single image. Our method builds upon the recent monocular geometry estimation approach, MoGe, which predicts affine-invariant point maps with unknown scales. We explore effective strategies to extend MoGe for metric geometry prediction without compromising the relative geometry accuracy provided by the affine-invariant point representation. Additionally, we discover that noise and errors in real data diminish fine-grained detail in the predicted geometry. We address this by developing a data refinement approach that filters and completes real data using sharp synthetic labels, significantly enhancing the granularity of the reconstructed geometry while maintaining the overall accuracy. We train our model on a large corpus of mixed datasets and conducted comprehensive evaluations, demonstrating its superior performance in achieving accurate relative geometry, precise metric scale, and fine-grained detail recovery -- capabilities that no previous methods have simultaneously achieved.

MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details
Ruicheng Wang 1.2+ Sicheng Xu Yue Dong² Yu Deng² Jianfeng Xiang Zelong Lv 1,2+
Guangzhong Sun' Xin Tong² Jiaolong Yang²t
USTC Microsoff Research Tsinghua University
Abstract
We propose MoGe-2, an advanced open-domain geometry estimation model that recovers metric scale 3D point map of scene from single image. Our method builds upon the recent monocular geometry estimation approach, MoGe [61], which predicts invariint point maps with unknown scales. We explore effective strategies to extend MoGe for metric geometry prediction without compromising the relative geometry accuracy provided by the innirranv point representation. Additionally, we discover that noise and errors in real data diminish fine-grained detail the predicted geometry. We address this by developing unified data refinement approach that filters and completes real data from different sources using sharp synthetic labels, significantly enhancing the granularity of the reconstructed geometry while maintaining the overall accuracy. We train our model large corpus mixed datasets and conducted comprehensive evaluations, demonstrating its superior performance in achieving accurate relative geometry, precise metric scale, and fine- grained detail recovery capabilities that no previous methods have simultancously achieved.
Introduction
Estimating 3D geometry from single monocular image is challenging task with numerous applications in computer vision and beyond. Recent advancements in Monocular Depth Estimation (MDH) and Monocular Geometry Estimation (MGE) have been driven by foundation models trained on large-scale datasets 677
61 7 Compared to depth estimation, MGE approaches often also predict camera intrinsics, allowing pixels be lifted into 3D space, thus enabling a broader range of applications.
68
44
27
Despite the promising results of recent MGE models, they remain far from perfect and broadly applicable. We expect an ideal MGE method to excel in three key areas: 1) geometry accuracy, 2) metric prediction, and 3) geometry granularity. While accurate global and relative geometry is essential, metric scale is crucial for real-world applications such as SLAM [54] 36), Autonomous Driving [6 777. and Embodied AI
82
gT 46 In addition, recovering fine-grained details and sharp
features is also critical for these fields as well as others like image editing and generation (79 75 60)
To our knowledge, no existing method addresses all these needs well simultaneously.
In this paper, we introduce new MGE method towards achieving these goals, while maintaining a simple, principled, and pragmatic design. Our method is built upon the recent MoGe approach [61], which predicts affine-invariant point maps from single images and achieves state-of-the-a geometry accuracy. The cornerstone of MoGe is its optimized training scheme, including a robust and optimal point cloud alignment solver as well as a multi-scale supervision method which enhances local
Work done during internship at Microsoft Research t Corresponding author
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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RG
RG
RG
RG
RG
Ours
MoGet
Depth Pro
UniDepth V2
Metric3D V2"
Depth Anything
Figure I: Rankings in comprehensive evaluations. Our method achieves accurate Relative Geometry (RG), precise Metric Geometry (MG), and Sharp Detail recovery (SD) capabilities not simultane- ously achieved by previous approaches. Methods do not predict camera intrinsics and are evaluated on depth only. MoGe [61] does not predict metric scale. Please refer to Sec. 4.1 for details.
geometry accuracy. Our work extends MoGe 61 by introducing metric geometry prediction capabilities and improving its geometry granularity to capture intricate details.
For metric geometry estimation, straightforward solution involves directly predicting absolute point maps in metric space. However, this is suboptimal due to the focal-distance ambiguity issue [61]. To address this, explore simple, intuitive, yet effective alternatives. The first uses shift-invariant point map representation which directly integrates metric scale into point map prediction. The second retains affine-invariant representation but additionally predicts a global scale factor decoupled manner. Both strategies mitigate the focal-distance ambiguity, but the latter yields more accurate results, likely due its well-normalized point map space that better preserves relative geometry.
In the latter regard, we propose a pragmatic data refinement approach to generate sharp depth labels for real-world training data. Real data labels often noisy and incomplete, particularly at object boundaries, which impede fine geometry detail learning. Previous works such as Depth Anything V2 (68) have opted to use only synthetic data labels, sacrificing the geometry accuracy, despite being sharp upon 2D visualization. Similarly, Depth Pro 7 employs only synthetic data their second of embrace real data throughout the training to ensure high geometry
the two stages. In contrast, accuracy a critical goal for our method. Our pipeline filters mismatched or false depth values in real data, primarily found around object boundaries, followed by edge-preserving depth inpainting to fill missing regions using a model trained on synthetic data. This approach results in significantly finer details, with geometry accuracy comparable to models trained full unprocessed real data.
We train our model on an extensive collection of synthetic and real datasets and conduct compre- hensive evaluation across various datasets and metrics. Experiments demonstrate that method achieves superior performance in terms of relative geometry accuracy, metric scale precision, and fine-grained detail recovery, surpassing multiple recently proposed baselines, shown in Fig. 1
Our contributions are summarized follows:
We introduce a Metric MGE framework with the representation of decoupled affine-invariann pointmap and global scale. We provide both insights and empirical evidences for this design.
We propose a pragmatic real data refinement approach which enables sharp detail prediction while maintaining the generality by fully leveraging large scale real data.
Our method achieves state-of-the- of-the-art results in both geometry accuracy and sharpness, significantly surpassing prior methods in global and local geometry accuracy.
We believe our method enhances monocular geometry estimation's 's potential in real-world applications and can serve as a foundational tool facilitating diverse tasks such as 3D world modeling, autonomous systems, and 3D content creation.
2 Related Works
Monocular metric depth estimation. Early works in this field 15 71
20 primarily focused on predicting metric depth in specific domains like indoor environments or street views, using limited data from certain RGBD cameras or LiDAR sensors. With the increasing availability of depth data from various sources, recent methods 5 in open-domain settings. For example, Metrie3D
2 67
68
4E have aimed to predict metric depth utilized numerous metric depth datasets
74
23

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and introduced a canonical camera transformation module to address metric ambiguity from diverse data sources. ZoeDepth built on relative depth estimation framework [47 6 that pre- trained on extensive non-metric depth data and employed domain-specifft metric heads. UniDepth [44] 45 instead simultaneously learned from metric and non-metrie depth data to improve generalizability. Our method focuses on metric geometry estimation and also enables metric depth estimation by
directly using the z-channel from the predicted point map, outperforming existing approaches in open-domain metric depth predictions.
Monocular geometry estimation. This task aims to predict the 3D point map of scene from decouple point map prediction into depth estimation and camera parameter recovery. For instance, LeRes [72] estimates an affine-invariant depth map and camera focal and shift with two separate modules. UniDepth series
a single image. Common approaches 722 73 44 45
45 predicted
camera embeddings and facilitate depth map prediction with the estimated camera information. Along another line, DUSt3R [62) proposed an stereo images, bypassing explicit camera prediction. In similar vein, MoGe [6] predicted an affine-ing invariant point map for monocular input, achieving state-of sore-offtheeeee the performance with a robust and optimal alignment solver. However, does account for metric scale and lacks the finer details, thereby limiting its applicability in many downstream tasks.
3D point map prediction framework for
Depth prediction with fine-grained details. Numerous methods 440 35.35
45 68
25 39
80 (66) have been developed to recover fine-grained details in depth prediction. Some [40 35] enhance local details by fusing depth maps for image patches, but suffer from stitching artifacts. Other works [6] leverage pretrained image diffusion models [50] to generate detailed depth maps. Depth Anything v2 [68] highlights the importance of synthetic data labels by finetuning a DINOv2 [43 encoder with synthetic data and distilling from a larger teacher model. However, synthetic-to-red tealel domain gaps persist and hinder the prediction accuracy. Depth Pro (7] integrates multi-patch vision transformers [1]] and a synthetic data training stage, significantly improving depth map sharpness over previous methods, but still falls short in geometric accuracy. In contrast, our model achieves both fine detail recovery and precise geometry through the joint use of synthetic data and real data with a carefully designed real data refinement strategy.
RGB-deeph data misalignment artifacts Despite their overall accuracy, depth datasets captured with LiDAR 164 53 56 [99 or structurefrommmmoteee (SfM) reconstructions 778, 770 34 often exhibit various misalignment artifacts. Common issues include spatial misalignment caused by sensor asynchrony (76), ghost surfaces, and incomplete surface reconstruction (70). Existing methods
address LiDAR-specífe issues using stereo cues [56] or epipolar geometry 84, while StM artifacts
are mitigated by regenerating depth maps with neural rendering [37 2 However, these approaches
are often tailored to specific types of artifacts or rely on computationally expensive pipelines. We propose unified data refinement approach that can handle diverse misalignment artifacts in RGB- depth data regardless of their source or underlying error patterns.
3 Methodology
Our method processes a single image predict the 3D point map of the scene, achieving accurate relative geometry. metric scale, and fine-graines detail. It builds upon the recent MoGe approach [6]] that focuses on invareiatt point map reconstruction (See. B.1p. We explore effective strategies to extend it to accurate metric geometry estimation (Sec. 3.2). Additionally, we develop a data refinement approach that fully leverages real-world training data achieve both precise and detailed geometry reconstruction simultaneously (Sec. 3.3
3.1 Preliminaries: MoGe
Given single image I xx MoGe estimates affine-ivvrriant 3D point map P H xx3 with an unknown global scale and shift relative to the ground truth geometry P, achieved by learning through robust L, loss:
La Σ
-1 + -
(I)
3

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mmge takens
Horiznnaal Foy
Conv head
Vertical FOV
Conv. neck
DINOV2 VIT
Conv head
Affine iny. pantine
Inpul image
MLP
Mottc
head
scale lacker
3 A-Po
CLS
Metrio-scale
Metric depth
Figure 2: Overview of our model architecture. With the key insight of decoupling metric MGE into iniireinvaariant point map prediction [61] and global scale recovery, our network design extends MoGe [6]] with additional head for metric scale prediction. This design preserves the benefits of affine-invariant representations for accurate relative geometry while enabling metric scale estimation with the global features captured by the ViT encoder's classification token.
where M is the valid mask of ground truth point map, 1/2, is a weighting scalar using inverse ground truth depth, and s" and t" the optimal global scale and shift alignment factors derived by robust and optimal (ROE) alignment solver [61,
(s", (², argmin
Σ BEM
| + P.ll1
To enhance local geometry accuracy, it further applies the robust supervision in Eq. IT to multi-scale local spherical regions
Sj = {i |p: - P, 5 Sr,iE
centered at sampled ground truth point p, with different radius ry- After obtaining the affine-invarian point map, the camera's 's focal and shift can be recovered by simple and efficient optimization process (see [61] for more details).
While MoGe accurately predicts relative geometries, it falls short in addressing metric scale and lacks fine-grained details, limiting its broader applications. We explore these challenges and propose effective solutions to achieve accurate metric scale geometry estimation and fine-grained detail reconstruction, as detailed below.
3.2 Metric Scale Geometry Estimation
Conv head
DINOv2 ViT
Conv. neck
We explore two alternatives to extend MoGe with met- ric scale prediction, with corresponding design choices illustrated in Fig.
Conv
Shiftiny. point map
1. E.ilangled Slift-nwerisnf paintrap
Conv head
Mask M
Shift-invariant geometry prediction. As illustrated in Fig. 1,,, natural extension of MoGe is to predict point map by absorbing the metric scale into the affine point map, while computing only the global shift via ROE alignment during training and re- solving it again at inference time. This design bypasses the focal-distance ambiguity [61] and yields reasonable metric reconstruction results (Tab. 4
DINOv2 VIT
Conv. neck
Conv head
Affine-inv P
Conv head
Metric scale
2(0). Decoupled - Convolational head
Conv head
DINOv2 VIT
Conv. neck
However, due to the large variation scene scale across opmm-domain images (e.g., indoors vs. landscapes), the predicted values shift-invariant space span a wide range. This makes scale learning less stable, and inac- curate scale predictions can produce large gradients that interfere with relative geometry learning (i.e., the middle section Tab. 4.. This motivates our choice to decouple scale estimation from the point map prediction entirely.
Conv head
Affinc-inx point map
MLP
Metric scale factor
head
CLS token
2/b). Decoupled
Figure 3: Model design choices for metric scale geometry estimation.

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RGB Image
Gyn predicted depth
Raw GT depth
Filtered GT depth Completed GT depth
The ScanNet++ dataset [70] based on SfM reconstruction, struggles with thin structures and metallic surfaces. Our filtering process removes these artifacts, and our completion scheme reconstructs depth maps that maintain robust absolute depth while compensating for local details that align with the image. Bottom: the Argoverse2 dataset [64]. depth and color image discrepancies occur due to temporally unsynchronized sensors. Marking the vehicle boundary in color images (yellow lines) indicates a significant mismatch.
Figure 4: Filtering and completion for real captured datasets. Top:
Scale and relative geometry decomposition. To prevent scale affecting relative geometry accuracy. we maintain the geometry branch for affineinvariant point map as in MoGe, and introduce an additional branch for scale prediction with exclusive supervision:
C. ||log stopgrad(log(s ))
where log(š) (8) is the predicted metric scale in logarithmic space, and s" is the optimal scale calculated online by Eq. (2) between the predicted alfine-invarian point map and the ground truth using the ROE solver. The final metric scale geometry is obtained by multiplying the predicted scale with the affine-invariant point map. We explore two design options for the additional scale prediction branch:
(a) Convolutional head. A naive design, as shown in Fig. ((a), is to add convolution head to output a single scale value, sharing the convolution neck with the affine-invariant point map. However, this approach does not improve relative geometry and worsens metric scale predictions (see Tab. (4) We suspect that simply adding a convolution head results in most information being processed in the convolution neck, which fails to decouple scale prediction from its effect on relative geometry. Moreover, the small output head ineffective at aggregating local features from the convolution neck, while accurate metric scale prediction requires global information.
(b) A.S-token-condiiionee
MIP. To better decouple relative geometry and metric scale predictions, our second design (Fig. ((b) 2 uses an MLP head to learn the metric scale directly from the DINOv2 encoder's classification (CLS) token (see Fig. 2). The global information the token enables the network to predict accurate metric scale. As demonstrated in Table such simple design improves metric geometry accuracy compared to the convolution head method while maintaining accurate relative geometry. Thus, we adopt this design as our final configuration.
3.3 Real Data Refinement for Detail Recovery
We found that the MoGe model struggles accurately reconstruct fine- ine-grained structures due to noise and incompleteness in real training data. Previous studies 68 27 have also noted this issue and suggest training with synthetic data of sharp labels and pretrained vision foundation models for real-world generalization. However, this still limits geometry accuracy because synthetic data rarely captures real- world diversity. Therefore, using real datasets while reducing their noise and incompleteness is crucial for accurate geometry estimation. To address this, we design real data refinement pipeline that incorporates synthetic labels to mitigate common failure patterns in real data.
Failure pattern analysis. Real data often originated from LiDAR scans or Structure from Motion (SfM) reconstructions. LiDAR data can suffer from synchronization issues, causing depth and color mismatches, especially at object boundaries. SfM data might miss structures like reflective surfaces, thin structures, and sharp boundaries, as shown Fig. Our refinement approach leverages the fact that models trained on synthetic data achieve exact color-depth matching and capture sharp, complete
5

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Ground truth
Gsyn prediction
60%
2300%
Unfiered
200%
20%
Unde--ileedd
100%
OvenSleedd
Global map
Global filtering
Local filtering
Local map
Figure 5: Our mismatch filtering scheme with local geometry alignment effectively avoids depth bias of the predicted results and helps identify correct artifacts in the real data, whereas global alignment fails to address the bias and introduces foreground errors, making it unsuitable for filtering.
local geometries. These pseudo labels can help filter incorrect depths and fill in missing parts in real data given accurate local geometries.
Mismatch filtering. To filter real captured depth data, we train a MoGe model solely on synthetic data, denoted as Gsyn. The model is then applied to real images to infer geometry, which serves as reference for filtering. While the predicted local structures are generally plausible, Gsyn often yields biased estimates of the overall scene geometry and layout when used on real-world images. This bias arises from the lack of real-scene priors during training on purely synthetic data. As shown in Fig. [5] such bias can result in incorrect filtering when global errors are considered. Therefore, we focus on comparing the local structures between the real and predicted point maps.
Specifically, given the real-captured points (pt). corresponding predictions (p.} by G and the mask M of valid ground truth, select spherical region SJ centered each estimated point P; with a specific radius fy: S, = ( St, M}
(5)
Within this local region Sj, we align the corresponding real-captured points {pthes, with the predictions (Pshvss, via the ROE solver and mark a real-captured point an outlier deviates from the predictions beyond the specified radius, forming a set O,:
O = { |ssp, +t, - P.ll > f,,iE S,
(6)
with (s). tg) optimal alignment factors for local regions. The outlier sets derived from all sampled local regions of different fj are combined and excluded from the mask, yielding the final valid area
Milterod = MM (Uo,).
Regarding the choice of radius fy for each sampled anchor point, follow the same multi-scale strategy in MoGe's s local loss for capturing context at different spatial scales. Specifically, we set fj a- 2g 2 #2 where 2, is the depth of P,, f is the predicted focal length, W and H are image width and height, and E {1/4, 1/16, 1 1/64} controls the region's projected size approximately cover the respective ratio of the image size.
Geometry completion. After filtering out mismatch regions, we create complete depth map by integrating the detailed structures predicted by Gsyn with the remaining ground truth depth. Specifically, we reconstruct the depth in the filtere-outt regions {d] heee using logarithmic- space Poisson completion:
min
V(logd)) - ((lodd))l s.t. di = dr, Vi E aM filtered
(8)
MEMO Fittered

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Table 1: Quantitative evaluation for relative geometry. The numbers averaged across the 10 evaluation datasets. The metrics are visualized with color gradient from green (best) to red (worst). Numbers in gray cells indicate that some test datasets were used training. Non-applicable cases are marked with Detailed results on each dataset can be found in suppl. materials.
Depth
Avg.
Method
Scale-imv.
Afline-inv. inv.
Local
Scale-inv. 8
Alline-inv
Alline-ime (disp)
RelP J. of Rk.
RelP
8
Rk.
RelP
Rk.
Rel
Rk...
Rel"
81
Rk.
Rel" 1 8
Rk
R...
ZoeDepth
12.7
83.9
8.75
10.1
88.5
9.09
11.1
88.3
8.78
8.87
DA V1
11.7
85.8
8.22
8.76
90.4
6.91
8.63
92.2
5.62
6.92
DA V2
10.7
87.6
6.80
8.48
90.8
6.15
8.82
91.6
5.42
6.12
Metric3D
3.39
9.51
MAS0R
14.5
82.1
5.45
11.6
86.0
5.45
8.09
92.2
5.40
11.2
86.5
7.65
9.38
89.1
7.97
11.6
87.8
8.60
6.75
UniDepth
13.6
85.0
10.9
88.
3,95
5.58
10.1
89.1
5.12
8.61
91.0
9.75
89.9
5.01
UniDepth V2
11.6
87.7
2.88
8.56
91.9
2.55
6.34
94.9
3.10
8.61
90.8
3.10
6.42
93.9
2,80
7.35
93.0
2.75
2.88
Depth Pro
12.4
87.7
3.83
9.93
89.4
4.30
6.91
94.1
3.55
9.81
89.1
5.33
7.65
92.0
5.05
8.42
91.7
5.08
4.52
MoGe
7.46
94.1
2.14
5.69
95.2
2.14
5.50
95.6
2.05
5.77
94.5
2.72
4.51
96.1
2.94
5.58
95.4
3.17
2.53
Owr
108
88.5
2.40
7.98
91.7
2.23
5.33
95.9
1.35
7.35
92.2
2.12
5.62
94.8
2.02
6.66
93.8
2.17
2.05
Table Quantitative evaluation for metric geometry. The numbers are averaged across 7 datasets.
Table Evaluation of boundary sharp- ness using Fl scores (1) in percentages.
Method
Point
Depth
Depth (w/ GT Cam)
Arg. Rk_
Method ZeeDepth
IBims-1
НAMMER
Sintel
Spring
Avg. RE.
RelL 1 R
Ret 4 6 Rk.L
Rel &.t RX.
2.47
0.17
2.30
0.43
7.75
ZoeDopth
39.3
49.9
5,90
3.90
DA V1
3.68
0.76
5.64
1.09
6.75
DA V1
31.8
54.8
5.50
5.50
13,9
3.75
V2
29,9
56.6
4.43
4.43
Metre3D V2
7.36
1.40
25.3
7.23
5.25
MecrccD V2
18.3 73.9
2.75
2.75
1.24
1.72
9.50
MASER
49.7
VI
2.35
0.06
0.73
0.17
9,00
UniDepch VI
12.1
87.2 2.71
23.2
67.5
3.32
21.4
68.6
2.50
2.84
UniDeech V2
11.2
4.40
39.7
7.08
3.75
UniDepc
91.9
2.43
21.3
2.54
18.5
82.6
2.57
2.51
Depth Pro
14.3
5.36
41.6
1,50
Depth Pro
13.7
81.9
3.29
27.6
54.4
4.36
3.83
MoGe
11.4
3.89
26.3
8.36
4.67
Ours
8.19
93.6
1.64
15.7
76.8
2.21
13.6
87.4
2.00
1.95
Ours
17.9
35.2
1.75
where filtored is the complement area of Mättered d; and d, denote predicted depth by Gsy and the real captured depth, respectively. This strategy ensures that the reconstructed depth aligns with the gradient of the predicted depth at local regions while maintaining the ground truth depth as the boundary condition.
Figure Iillustrtee our filtering and completion process. Our method effectively removes mismatched depths from LiDAR completed depth map retains sharp geometric boundaries that align with the input image while preserving the robust absolute depth from the original map. The refined training data effectively enhances the model's 's sharpness and maintains accurate geometry estimation, as shown in Tab, 4
and fills in missing content in SfM-reconstruuted depth maps. The
4 Experiments
Implementation details.
We train our model using a combination 24 datasets with synthetic
55 73, 3 LiDAR scanned datasets [17 We follow MoGe [61] to balance the weights and loss functions among different datasets, and also adopt their approach for image cropping and data augmentation. More details of the training datasets can be found in suppl. material.
datasets [10 588 49
59
42
33
14
24
83
51
63
21
65
[3], and Sf-rrcoostrrected datasets 3
78
70
34
69
We use DINOv2-ViT-Larrg ViT-Large as the backbone for the full model, and DINOv2- 2--OO---VT--B.. ViT-Base model for all ablation studies to ensure efficiency. Our convolutional heads follow MoGe's 's design but remove all normalization layers in order to significantly reduce inference latency. The models are trained with initial learning rates of 1 x 10-5 3 for the VIT backbone and 10-4 for the neck and heads. The learning rate decays by half every 25K steps. The full model is trained for 120K iterations with 32 NVIDIA A100 GPUs for 120 hours. Ablation models are trained for 100K iterations. Additional implementation details and runtime analysis are provided in the supplementary material.
7

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RGB Image
Ground Truth
Ours
MoGe
UniDepth V2
Depth Pro
2.94m
2.90m
N.A.
2.56m
2.00m
72cm
8tcm
N.A.
83cm
80cm
T1Em
NA
128m
42M
available
5:4mm
0.70m
Not available
Figure 6: Qualitative comparison of metric scale point and disparity maps. The top two rows selected from unseen metric scale test datasets. We also labeled key metric measurements in both the ground truth and the estimated geometry. Our estimated metric geometry best matches the ground truth and maintains sharp details. For open-domain inputs, our method produces reasonable geometry with rich details, while results of Depth Pro [7] are severely distorted, Best viewed zoomed
4.1 Quantitative Evaluation
Benchmarks.
We evaluate the accuracy of our method on 10 datasets: NYUv2 41 KITTI 56
ETH3D (52, iBims-1
[31 30
GSO [2] Sintel [8 DDAD 19), DIODE Spring B8 and
HAMMER (26). These datasets encompass wide range of domains, including indoor scenes, street views, object scans, and synthetic animations.
Baselines. We compare method with several monocular geometry estimation methods, including UniDepth V1 and V2 [4 45), Depth Pro 7 MoGe [61], MASt3R (32), as well as depth estimation baselines: Depth Anything V1 (DA V1) and V2 (DA V2) 67 68) ZeeDepth (5) and Metric31) V2 [74). We evaluate the performance of these methods based on relative scale geometry, metric scale geometry, and boundary sharpness.
Relative geometry and depth. While the primary goal of our method is to estimate metric scale geometry, measuring relative geometry provides valuable insights into how each method reconstructs the geometric shape from the input image. We employ the evaluation metrics of MoGe, measuring over the eealeeinvariint point maps, affine-invaree invariant point maps, local point maps, scale-invariant depth, affine-invariant depth, and affinaninvariat disparity.
Table presents the average relative error for depth map), and the percentage of inliers (8f, where |p pllz/ [|pll < 0.25, and 84, where
- RelP (ipppp/llpplll) for point maps and Rel (|==2//2)

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Table 4: Ablation study results averaged over 10 datasets, conducted with a ViT-Base encoder.
Metric geometry
Relative geometry
Sharpness
Contiguration
Depth
Point Afline inv.
Depth Alline inv.
Scale inv.
Local
Scale inv.
Alline inv, (dis))
ReP4 4 8,
Rel
f
ReP L. 8,
Rell 6tt Rel" 8
Ret" 4 61
Rel 6
Ref
6
F1
prediction design
Entangled (SI-Log)
10.0 90.7
17.9
68.6
12.9
86.2
10.3
88.6
8.21
93.0
9.83
89.0
7.97
92.0
9.03
91.1
10.7
Entangled (Shift in.)
8.99 92.1
16.9
68.8
12.0
87.2
9.05
90.2
6.09
94.6
8.46
90.6
6.75
93.2
7,80
92.1
11.8
Decoupled (Com, head)
9.62 91.4
17.7
68.4
12.2
9.15
90.0
6.34
8.46
90.2
6.62
93.2
7.74
92.1
12.7
Decoupled (CT. S-LPP)
9.20 91.9
16.5
72.8
11.6
87.6
8.87
90.5
6.26
95.1
8.23
91.0
6.53
93.4
7,53
92.6
12.5
Training data
Synthetic data only
12.4 87.3
65.0
12.3
9.77
88.9
9.04
89.6
92.5
8.37
91.6
13.3
w/ Raw real data
9.01 92.2
15.8
75.7
11.4
87.8
8.69
90.7
6.37
94.9
8.40
90.4
6.63
93.3
7.69
92.2
10.3
Impruved data
72.8
11.6
90.6
7.53
RGB Image
w/ synthetic data only 86.7%
w/ raw real data 6 93.7%
w/ improved real data
93.9%
Figure 7: Showcase of ablation study models trained with different data.
max(d/d,d/d) d/d) < 1.25) across the 10 test datasets, along with the average ranking among the 8 methods. Note that Dephh, DA V1, DA V2, and Metric3D V2 are not evaluated for point settings due the lack of camera focal prediction. Our method outperforms all existing baselines across every evaluation metric and achieves results comparable to the state-of-the-ard relative geometry estimation method, MoGe. This demonstrates that model does not compromise the accuracy of relative geometry for achieving metric scale estimation.
Metric geometry and depth. We evaluate the accuracy of metric scale geometry and depth using 7 datasets with metric scale annotations, including NYUv2 [1]. KITTI [56], ETH3D [52], iBims-
1 B1 30), DDAD [19], DIODE (57) and HAMMER [26]. We measure the relative point error (RelP) and percentage of inliers (oP) on estimated metric point maps. Similarly, we evaluate the metric depth accuracy via relative depth error (Rel") and depth inliers (6t). Additionally, we evaluate metric depth estimation using ground truth camera intrinsics for methods that accept this input, which helps climinate the influence of inaccuracies in the estimated camera intrinsics. As shown in Table our method largely surpasses all existing methods across every metric measurement, demonstrating the advantages of our simple and effective design for decoupling metric scale and iitteeiivvarintt point estimation.
Boundary sharpness. evaluate the sharpness of the estimated geometry, we use two synthetic datasets, Spring (38] and Sintel [8), well as two real-world test datasets iBims-1 [3] and HAM-
MER (26), which contain high-quality, densely annotated geometry. We employ the boundary Fl score metric proposed by Depth Pro 7 measure boundary sharpness. As shown in Table 3
our method achieves boundary sharpness comparable to that of Depth Pro 7 and significantly outperforms it in terms of both relative and metric scale geometry accuracy.
4.2 Qualitative Evaluation
Figure presennss visual comparison of metric scale point maps and disparity maps estimated by different methods. We have annotated key metric scale measurements on both the ground truth and estimated geometry to facilitate comparison produces metric scale geometry with sharp details, whereas MoGe and UniDepth V2 miss significant geometric details. Depth Pro exhibits reduced geometric accuracy, particularly in the open-domain test image of crocodile.
metric scale accuracy. Our method successfully

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4.3 Ablation Study
Metric scale prediction. See. 3.2 we explored various strategies for accurate metric geometry estimation from open-domain images. We evaluate these configurations across the 10 test datasets using the aforementioned evaluation metrics. We also introduce naive baseline that directly predicts metric point map with entangled scale and shift factors using the commonly adopted SI-log loss [13
A the evaluation results, highlighting the importance of a decoupled design that separates metric scale from relative geometry estimation to improve overall performance. For the scale prediction head, the MLP module outperforms the convolutional head, particularly in metric geometry. This indicates the importance of using global information to predict the metric scale and better decoupling of relative geometry from scale prediction.
Real data refinement. To evaluate the impact of our data refinement pipeline, we conducted ablation study using different data configurations for training only synthetic data, raw real-world data, and our refined real-world data. As shown in Tab. 4 training exclusively synthetic data yields the highest sharpness but significantly reduces geometric accuracy. This supports the effectiveness of using synthetic-data-traine trained model predictions filter mismatched real data via local error. Training with real-world datasets enhances geometric accuracy but reduces sharpness. Our refined real-world datasets achieve nearly the same geometric accuracy the original datasets while maintaining reasonable sharpness, as further confirmed by the visual comparison in Figure 7
5 Conclusion
We have presented MoGe-2, a foundational model for monocular geometry estimation in open-domain images, extending the recent MoGe model to achieve metric scale estimation and fine- fine-grained detail recovery. By decoupling the task into relative geometry recovery and global scale prediction, our method retains the advantages of affine-invariant representations while enabling accurate metric reconstruction. Alongside, we proposed practical data refinement pipeline that enhances real data with synthetic labels, largely improving geometrie granularity without compromising accu- racy. M achieves superior performance in accurate geometry, precise metric scale and visual sharpness, advancing the applicability for monocular geometry estimation in real-world applications.
Limitations. Our method struggles with capturing extremely fine structures, such as thin lines and hair, and with maintaining straight and aligned structures under significant scale difference between the foreground and background. The ambiguity in real-world metric scale can also lead to deviations in out-of-distribution scenarios. We aim to address these challenges by enhancing network architectures and incorporating more real-world priors in the future.
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A Implementation Details
A.1
Network architectures
Image
Linear
UV wnooding
2x2 Upsample
2x2 Upsample
UV encoding
12h, 2w)
Libaar
ResEBlocit))
(2h, 2m)
Ineer
ReeSlockts)
Upsample
Lisaamlle
?
UV enooding
(4h, 4wl
Linear
ResBlock(s))
(4h,4w) 4w)
Lincar
RosBlockrs)
Upsample
Upsample
UV encoding
(th Bw)
Inear
RedBBoccit))
(0h, Bw)
Liner
Ree3Bockkg)
Upsample
UV woodding
(18h, 18w)
inseer
18k)
Linwer
Linwer
Output map
pyramid
Intarmedini
specifcciiic convelutional
Outgut map B
Task specifc convolutional head
Figure A.I: Illustration the convolutional neck and head module architectures.
The detailed architectures of our model components described follows.
DINOv2 Image Encoder. Our model supports variable input resolutions by leveraging the in- terpolatable positional embeddings of DINOv2 [43 The native resolution is determined by a user-specified number of image tokens. Given input image of arbitrary size and target number of tokens, we compute patch-level resolution x w that best matches the desired token count. The image is then resized to (14h, 14w) to match DINOv2's input requirement, and encoded into x w image tokens along with one classification token. We extract four intermediate feature layers from DINOv2-Specificaa
the 6th, 12th, 18th, and final transformer layers- them common dimension, reshape their spatial size to (h, w), and sum them to form the input for the dense prediction decoder.
Convolutional Neck and Heads. Inspired by prior multi-task dense prediction architectures [48 28 61], we design a lightweight decoder consisting of shared convolutional neck and multiple task-specific heads, as illustrated in Fig Both the neck and the heads composed of progressive residual convolution blocks (ResBlocks) [22] interleaved with transpose convolution layers (kernel size 2, stride for progressive upsampling from resolution (h, w) (16h, 16w). Finally, the output map is resized through bilinear interpolation to match the raw image size. To reduce inference latency modern GPUs, all normalization layers simply removed from the ResBlocks, without affecting performance or training stability.
At each scale level of the neck, inject UV positional encoding, defined mapping of the image's ' rectangular domain into unit circle, preserving the raw aspect ratio information. The resulting intermediate feature pyramid is shared across all heads, each of which independently decodes its respective output map. This design enables multi-scale feature sharing while maintaining head-specific decoding tailored each prediction task.
T.S-token-conditionel MLP Head. For scalar prediction, the CLS token feature from DINOv2 as input and outputs a single scale factor, followed by an exponential mapping to ensure positive scale output. The hidden layer size is equal to the input feature dimension.
two-layer MLP that takes
A.2 Training Data
The datasets used training our model are listed in Tab. A.I All datasets are publicly available for academic use, and their sampling weights follow the protocol established in MoGe [61].

---

Tab. A.2
provides rough summary of the number of training frames used by several representative monocular geometry estimation methods. As there no shared standardized training set this field, this table serves to contextualize the scale of training data across methods. Notably, model performance does not necessarily correlate with the amount training data used.
Table A.1: List of datasets used to train our model.
Name
Domain
#Frames
Type
Weight
Metrie Scale
A2D2
Outioor/Driving
196K
LiDAR
0.8%
Argoverse2
Outdoor/Driving
1.1M
LiDAR
7.1%
ARKitScenes
Indoor
149K
SIM
8.3%
BlendedM 69
In-the-wild
115K
SIM
11.5%
MegaDepth 34
Outdoor/In-the Lnwthhwwtthhwdd
92K
SIM
5,4%
ScanNet++
Indoor
176K
SIM
4.6%
Taskonomy 78
Indoor
3.6M
14.1%
Waymo 633
Outdoor/Driving
788K
LiDAR
6.2%
ApolloSynthetic
Outdoor/Driving
194K
Synthetic
3.8%
EDEN[73
Outdoor/Garden
369K
Synthetic
1.2%
GTA-SIM 58
Outloor/Inthhewilld
19K
Synthetic
2.7%
Hypersim 49
Indoor
75K
Synthetic
4.8%
[RS[59]
Indoor
101K
Synthetic
5.4%
KenBurns 42
In In-the-wild
76K
Synthetic
1.5
MatrixCity 33
Outdoor/Driving
390K
Synthetic
1.3%
MidAir[14
Outdoor/In-thewwild
423K
Synthetic
3.8%
MVS-Synth 24
Outdoor/Driving
12K
Synthetic
1.2%
Structured3D 833
Indoor
77K
Synthetic
4.6%
Synthia[5]]
Outdoor/Driving
96K
Synthetic
1.1%
Synscapes|65)
Outdoor/Driving
25K
Synthetic
1.9%
UnrealStereo4K 55
In-the-wild
8K
Synthetic
1.6
TartanAir(63]
In-the-wild
306K
Synthetic
4.8%
UrbanSyn 21
Outdoor/Driving
7K
Synthetic
2.0%
Objaverse V1 10
Object
167K
Synthctic
4.6%
Table A.2: Summary of labeled training frame counts and pretrained backbones for the models
compared in this paper.
Method
#Total Training Frames
Pretrained Backbone
ZoeDepth
5
2M
MiDaS BETT384-L [47
DA V1 67
1.5M
62M pseudo-labeled)
DINOv2 ViT-Large
DA
68
595K 62M pseudo-labeled)
DINOv2 VIT- ViT-Large
Metric3D V2
16M
DINOv2 ViT-Large
UniDepth V1
44
3.7M
DINOv2 ViT-Large
UniDepth V2
45
16M
DINOv2 ViT-L.rrge
Depth Pro
6M
DINOv2 ViT-Large
MoGe
DINOv2 ViT-Large
Owrs
8.9M
DINOv2 ViT-Large
A.3 Evaluation Protocol
Relative Geometry We follow the evaluation protocol of alignment in MoGe [61. Predictions and ground truth are aligned in scale (and shift, if applicable) for each image before measuring errors as specified below
Scale-invariant point map. The scale a" to align prediction with ground truth is computed as:
Σ E lap; -Pal1..
a" = argmin
(9)
EEM
Afiic-invariant point map. The scale a" and shift b* are computed as:
(a", ba) argmin a.b
- |ap; P.lli-
(10)
z

---

Scale-invariant depth map, the scale a" is computed as
a' argmin
02; zi/-
(11)
iEM
Affine-invariant depth map. The scale a" and shift by are computed
(a", b*) argmin
E EM
1 0a; + 2//-
(12)
Affine-invariant disparity map. We follow the established protocol for affine disparity alignment [47), using leaat-squares to align predictions in disparity space:
(²,b")' argmin Σ (ad, dd,)
(13)
iEM
where d, is the predicted disparity and de is the ground truth, defined as d; 1/2,- To prevent aligned disparities from taking excessively small negative values, the aligned disparity truncated by the inverted maximum depth 1/2max before inversion. The final aligned depth 2/ is computed as:
1
3 = max(a* d; + by, 1/ Emax)
(14)
Metric Geometry
Metric depth. The output is evaluated without alignment and clamping range of values for all methods, unless specific post-prone post-processing is hard-coded its model inference pipeline.
Metric point map. The point map prediction is aligned with the ground truth by the optimal translation:
b = argmin
11, + -P:ll
P:ll1-
(15)
iEM
B Additional Experiments and Results
B.1 Test-time Resolution Scaling
In ViT-based models. the native input resolution is determined by the number of image tokens derived from fixed-size patches, specifically, 142 for DINOv2 models. As such. resolution scaling can be effectively studied through varying token counts. Our model is trained across a wide range of token counts from 1200 to 3600, corresponding native input resolutions ranging approximately from 4842 to 1882. This training setup enables robust generalization to broad range of resolutions and flexible usage with details as follows.
Geometry Accuracy MoGe [61] and UniDepth V2 [44] are both trained on diverse input resolutions and aspect ratios, which helps them maintain accuracy under resolution shifts within moderate range (1200- 3000). In contrast, models such Depth Anything [67 68 and Metric3D V2 2 trained with fixed input resolution and exhibit substantial performance degradation when evaluated at resolutions that diverge from their training setting. Our method, trained over broader resolution spectrum, remains robust under test-time scaling. As shown in Fig. B.3a maintains the top accuracy when scaled up for improved detail or down for faster inference
beyond the training range.
Boundary Sharpness Higher input resolutions and more image tokens generally lead to sharper boundaries in dense prediction tasks, as observed in prior works [68 48 29] and also shown in Fig. B.2 In Fig. [B3b]] we evaluate several DINOv2-based methods for boundary sharpness at different test-time resolutions. Note that Depth Pro operates at a fixed high resolution due to its specialized multi-scale, patch-based architecture. Our approach consistently delivers the sharpest predictions at each resolution and outperforms Depth Pro using significantly fewer tokens reach similar levels of detail.

---

Latency Trade-off As shown in Fig. B.3c inference latency scales roughly linearly with the number of tokens. Although all compared methods share the same ViT backbone, overall runtime can vary due to differences in decoder complexity and architectural choices. Our model adopts a lightweight design that enables fast inference while maintaining strong accuracy, achieving favorable trade-off between latency and performance across a wide range of resolutions- single unified framework.
RGB Image
1200 tokens (29ms)
3600 (55ms)
7200 tokens (108ms)
Figure B.2: Trading lateney for improved visual sharpness by increasing image tokens.
233
Dasth
203
In Drprr V2
VebricD V/2
193
Necantle
333
Dura
Unbaam
01
Un
50-
1D0 V
Metricp V2
E412
DA.V
0.
1000 2000 5000 4850 5000
8903 Te0e 8750 20003
0.
3006 4000 s0ee 0600 7000 tôee 20000
1000 2000 3980 0503 1003 tiee 20000
Number Image Tikens
Number of Image Thloes
Number Image Tokene
(a) Geometry accuracy(t)
(b) Boundary sharpness
(c) Inference latency (4)
Figure B.3: Performance comparison under test-time resolution scaling. * denotes the default configuration for each method. (a) Percentile rank [_0=worstt averaged across all evaluated datasets and geometry metrics (metric and relative geometry accuracy). (b) Average percentile rank for boundary sharpness. Both are evaluated on a 1/10 subset uniformly sampled from the evaluation benchmarks. (c) Inference latency measured
NVIDIA A A100 GPU with FP16 precision. Our method demonstrates the most favorable balance between latency and performance across different resolutions.
B.2 Runtime Analysis
As shown in Table B.3 we evaluate the runtime performance of each method under their represen- tative test-time configurations. Specifically, we measure single-frame inference latency and peak GPU memory usage on an NVIDIA A100 GPU. These metrics provide practical comparison of computational efficiency and resource requirements across different architectures.
B.3 More Visual Results
More visual results for qualitative comparison are included in Fig. B.4 B.4 and Fig failure cases are shown Fig. B.6
B.5 Representative
B.4 Complete Evaluation Individual Datasets
In the paper, we only listed the average performance across multiple datasets for qualitative compari- and ablation study. Table B.4 Table B.5 list all the results for each individual datasets.

---

Table B.3: Runtime statistics measured on single NVIDIA A100 GPU for single-frame inference.
Native Resolution
Latency (ms)
Memory (GB)
Method
#Parameters
#Tokens
FP16
FP32
FP16
FP32
DA V2
335M
1369
5182
24
86
0.91
1.8
Metric3D V2
412M
3344
x064x616
87
255
1.4
2.3
1020
4482
33
84
1.1
1.8
UniDepth V2
354M
3061
774
2.5
Depth Pro
504M
20160
15362
139
906
3.7
8.0
1200
484
40
93
0.74
1.4
MoGe
314M
2500
700
70
192
0.88
1.6
1200
4842
82
0.96
1.7
2500
700
39
157
1.1
2.1
Ours
326M
3600
840
55
238
1.3
2.5
7200
11882
108
565
1.9
3.8

---

RGB Image
Ours
MoGe
UniDepth V2
Depth Pro
3.08m
3. 3.3m
3.14m
5.48m
N.A
1.000
1.20m
NA
1.42m
0.50m
Figure B.4: More visual results on open-domain images (1/2). Best viewed zoomed in.

---

RGB Image
Ours
MoGe
UniDepth V2
Depth Pro
ssonn
NA
Figure B.5: More visual results on open-domain images (2/2). Best viewed zoomed in.

---

70m
Figure B.6: Failure cases. Top: the reconstruction of very thin struetures appears distorted, even though the predicted depth maps look sharp. Bottom: the model predicts height of 70 for the Eiffel Tower, far below its true 330 m. Such scale errors occur when the scene contains atypical content or lacks familiar geometric cues.

---

Method
NYUv2
KiTl1
ETH3D
iBmns-1
GSO
Sintel
DDAD
DIODE
Spring
HAMMER Roll dxt
Arg.
Rell 6.1
Roll fx1
Rell 61
Rell 61
RelL 6:1 Rell 6xt
Rei 6rf
Rell 6.
Rel, t:
Roll S1t Rank)
Metric point mup
MAS0R
7.11 95.6
26.0 45.8
27.4
43.1
10.1 89.3
35.4 28.7
21.7 66.3
56.0
18.3
26.2
55.3
4,93
UniDepth V1
4.80 98.3
4.52
98.5
22.4
63.1
10.8
92.8
11.4 89.5
12.8 88.9
18.0 79.5
12.1
87.2
2.71
UniDepth V2
4.83 98.0
5.88
97.5
9.46 95.0
5.23
97.9
13.3 90.3
17.0 80.8
15.0 83.9
10.1
91.9
2.43
Depth Pro
6.13 97.3
11.1
85.3
21.2 64.9
6.89
96.9
22.6 61.3
13.5 81.8
14.5 86.0
13.7 81.9
3.29
Owrs
98.3
7.44
94.4
97.7
97.4
87.9
92.3
87.0
Metric depth mup (woi GT intrinsics)
ZoeDepth
11. 91.9
17.0
85.4
57.1
35.7
17.4
67.2
38.9 36.6
39.3
29.3
94.3 3.23
39.3
49.9
3,90
MASER
89.7
56.7
47.2
20.1
62.4 5.51
54.9
19.0
97.2
49.7
6.71
DA VI
10.5 94.9
11.6
94.5
40.2
24.0
12.9
81.8
14.5 44.7
58.0
16.2
54.8
27.3
31.8
54.8
5.50
DA V2
16.4 80.9
10.6 88.6
36.1
36.3
11.1
91.7
41.7 37.5
41.2
22.1
52.1 38.9
29.9
56.6
4.43
UniDepth V1
7.59 97.6
4.69
98.4
56.9
14.9
23.8
57.6
13.8 85.1
17.1
71.9
38.2 46.7
23.2
67.5
3.32
UniDepth V2
10.6 92.8
8.58
95.4
20.7
69.5
9.52
93.2
18.4 77.6
43.0
51.8
38.2
46.8
21.3
75.3
2.54
Depth
10.7 91.9
23.5
38.3
38.5
32.8
81.5
33.4 35.3
31,9
37.7
39.1
63.0
27.6
54,4
4.36
Ours
7.33 96.1
18.1 62.9
10.4
90.8
13.6
83.0
15.8 73.0
17.5
66.4
26.9
65.6
15.7
76.8
2.21
Metric depth map (W/ GT lntrinsics)
Metri3D
7.16 96.5
5.25
98.0
85.8
94.1
9.21 93.7
49.1
1998
35.7
73.9
2.75
UniDepth V1
5.98 97.9
4.43 98.5
44.5 26.7
22.6
60.5
13.0
87.2
21.0
63.5
38.6 45.9
21.4
68.6
2.50
UniDepth
7.81
96.0
5,98 97.7
7.71
89.3
41.0
667.
2.57
Ours
6.46 96.9
8.64
93.7
10.5
92.2
9.92
92.4
13.1 85.6
16.2
77.1
30.4
74.2
13.6
87.4
2.00
Scale invariant point map
MASERR
6.26 96.0
93.8
95.5
95.1
77.6
85.0
39.3
31.7
95.0
82.1
5.45
UniDepth VI
5,33
98.4
5.96
98.5
18.5
77.6
5.29
97.4
6.58
99.6
33.0
48.9
11.4 90.2
12.3
91.0
33
49.8
4.83
98.5
13.6
85.0
3.83
UniDepth V2
5,59
98.3
5.41 98.0
6.58
97.2
5.56
98.1
4.53
99.7
27.2
56.3
13.4 91.2
12.0
93.4
31.9
46.0
4.20
99.2
11.6
87.7
2.98
Depth Pro
5,04 97.7
10.6
95.1
11.2
92.0
5.84
97.1
4.94
99.8
26.9
63.9
15.8 81.0
8.52
91.6
28.1
60.5
6.82
98.7
12.4
87.7
3.83
MoGe
4.86
98.4
5.47
97.4
98.9 4.58
4.63
97.1
2.58
100
22.3
69.5
12.3
90.3
6.58
94.5
4.84
96.4
6.45
98
7.46
94.1
2.14
Ours
3,94 98.3
8.27
97.5
5.45
98.6
98.3
2.55
100
23.1
66.8
11.0
90.7
8:42
93.7
31.1
42.4
8.77
98.4
10.8
88.5
2.40
Affine invariant point map
MASGR
5.30
96.3
8.32
92.3
5.48
96.6
5.72
95.0
3,50
99.2
26.3
62.8
14.7
79.6
8.10
90.
31.3 51.1
5.34
96.6
11.6
86.0
5.45
UniDepth
3.93 98.4
4.29
98.6
12.2
89.6
4.65
98.0
99.8
28.5
10.3 90.5
8:56
90.9
29.6
58.5
4.15
98.7
88.1
3.95
UniDepth V2
3.66
98.4
4.75
98.0
4.35
98.4
4.05
98.1
2.91
99.9
17.9
76.5
12.0 90.8
7.45
92.4
25.1 66.9
3:45
99.4
8.56
91.9
2.55
Depih
97.9
9.15
90.7
94.0
81.2
93.7
25.0
66.0
98.8
89.4
4.30
MoGe
3.68 98.3
4.86
97.2
3.57
99.0
3.61
97.3
1.14
100
16.8
77.8
10.5 91.4
4.37
96.4
4.45
96.4
3.88
98. 1
5.69
95.2
2.14
Ours
3.33 98.4
6.47 96.4
98.7
98.5
1.16
17.4 77.0
10.1 90.3
5.13
94.9
24.5
63.7
4.19
99.1
7.98
91.7
2.23
Local
point
map
MASGR
5.54
95.3
6.19
95.0
11.4 87.9
8.58
91.8
8.75
90.9
8.09
92.2
5.40
UniDopth V1
8.61 92.6
5.92
96.0
13.4 84.3
8. 18 92.0
9.95
90.0
9.21 91.0
5.55
UniDepth V2
3.99 97.4
4.02
97.3
9.35 92.2
8.18
92.4
6.15
95.3
6.34
94.99
3.10
Depih Pro
4.76 96.9
4.11
97.5
10.8 89.5
92.4 8.08
6.80
94.
6.91 94.1
3.55
MoGe
3.21 98.1
4.16
96.8
8.63 92.7
6.74
94.3
4.78
96.3
5.50
95.6
2.05
Ours
3.27
98.2
3.61
97.7
8.13
93.2
6.57
94.3
5.09
96.1
5.33
95.9
1.35
Scale-invariant depth
map
ZeeDepth
5.62 7.27
91.9
10.4
87
7.45
93.2
3.23
99.9
27.4
61.8
17.0 72.8
11.3
85.2
30.3
55.9
7:42
94.7
12.7
83.9
8.75
MAS03
5.37 96.0
6.24 94.5
5.68
95.5
5.58
95.2
3.72
99.1
26.3
63.7
13.5 81.5
8.37
89.4
32.2
53.5
5.50
96.5
11.2
86.5
7.65
V1
4.77
5.61
95.6
81.5
87.5
27.3
59.1
96.4
8.22
DA V2
5.03 97.3
7.23
93.7
6.12
95.5
4.32
97.9
4.38
99.3
23.0
65.2
14.7 78.0
90.0 7.95
28.0
61.1
5.92
97.7
10.7
87.6
6.80
Metric3D V2
4.69 97.4
98.5 4.00
3.84
98.5
4.23
97.7
2.46
99.9
20.7
69.8
7.41
94.6
3.29
98.4
24.4
64.4
4.19
99.1
7.92
91.8
3.39
UniDepth VI
3.86 98.4
98.6 3.73
5.67
97.0
4.79
97.4
4.18
99.7
28.3
58.8
10.1 90.5
6.83
92.8
29.2
59.3
4.19
98.4
10.1
89.1
5.12
UniDepth V2
3.65 98.4
4.24 98.0
3.23
98.9
3.45
98.1
99.7 3.16
23.1
65.3
11.0 91.5
5.92
94.
24.9
65.1
3.48
99.1
8.61
90.8
3.10
Depth Pro
4.42 97.6
96.2 5.47
7.54
94.1
97.4
99.9
68.7
14.0 82.0
92.0 7.05
25.1
63.8
4.36
98. 9
9.81
89.1
5.33
MoGe
3.44 98.4
4.25 97.8
3.36
98.9
3.46
97.0
1.47
100
19.3
73.4
9.17
90.5
94.7 4.89
4.63
96.4
3.77
98.
3.77
94.5
2.72
Ours
98.2
98.0
98.7
91.2
5.30
94.6
20.0
72.4
3.96
92.2
2.12
Affine invariant
depph
ZoeDepth
4.76 97.3
5.59
95.1
7.27
94.2
5.85
95.7
2.54
99.9
21.8 69.2
14.2 80.1
7.80
90.9
24.3
66.6
6.65
95.7
10.1
88.5
9.09
MASER
4.67 96.7
5.79
95.1
97.0
83.4
5.79
94.1
27.4
96.8 8
89.1
7.97
DA VI
3.82 98.3
5.04
96.4
6.23
95.2
4.23
97.3
1.98
100
20.1
71.8
11.3 86.1
6.75
92.6
22.4
68.9
5.77
97.3
8.76
90.4
6.91
DA V2
4.16 97.9
6.77
94.3
4.63
97.2
3:44
98.3
1.44
17.1
76.6
13.4 81.8
5.41
94.6
23.7 68.7
4.73
98.9
8.48
90.8
6.15
Metric3D V2
3.94 97.6
3.50
98.4
3.24 99.0
3.28
98.3
2.10
99.4
26.6
71.7
7.15 94.8
2.75
98.7
21.0
72.5
3.02
99.0
7.66
92.9
4.53
UniDepth V1
3.40 98.6
98.7 3.55
4.92 97.5
98.2 3.76
2.48
99.9
24.9
64.1
90.8 9.46
4.90
96.2
25.2
67.3
3.55
98. 9
8.61
91.0
5.67
UniDepth V2
2,96 98.6
98.1
2.95 98.5
98.4
90.9
4.05
96.5
20.1
75.4
6
2.80
Depth Pro
3.67 98.2
5.12
96.8
4.97 96.4
3.23
98.3
1.46
100
80.1 15.8
12.6 84.1
95.6 4.66
21.7
70.5
3.30
99.6
7.65
92.0
5.05
MoGe
2,92 98.6
3,94
98.0
99.2
9
3.16
97.5
96.4
98.3
96.1
2.94
Ours
2,89 98.6
3.75
98.1
2.80
99.1
2.36
98.8
0.94
100
13.3 82.5
8.26 92.5
97.4 3.14
15.9
81.2
2.85
99.3
5.62
94.8
2.02
Afline invariant disparity
ZneDepth
5.21 97.7
5.84
95.6
8.07
96.1
66.
14.1 81.7
8.17
92.0
27.2
63.0
6.84
96.4
88.3
8.78
DA VI
4,20
98.4
5.40
97.0
4.68
98.2
4.18
97.6
1.54
100
20.2
77.6
12.7 86.9
5.69
95.7
22.2
72.5
5.56
98.0
8.63
92.2
5.62
DA V2
4.14 98.3
5.61
96.7
4.71 97.9
3.47
98.5
1.24
100
21.4
72.8
13.1 86.4
5.29
96.1
24.3 70.6
4.97
99.1
8.82
91.6
5.42
Metric3D V2
13.4 81.5
3.76
98.2
97.7 4.30
8.55
92.3
1.80
100
21.8
72.4
7.35
94.1
7.70
90.2
23.3
68.1
3.17
99.2
9.51
89.4
6.17
MAS03
5.07
96.8
5.93
95.5
5.25
96.4
5.39
95.7
2.98
99.7
30.2
65.1
13.0 83.6
6.41
94.3
37.3
53.2
4.41
97.2
11.6
87.8
8.60
UniDepth VI
3.78
98.7
98.7 3.64
97.2 5.34
98.1
2.56
99.9
28.6
60.7
89.1 9.94
5.95
95.5
30.0
61.6
3.64
99.
9.75
89.9
5.92
UniDepth V2
3.38 98.7
3.99
98.0
2.97
99.0
3.15
98.3
1.30
100
17.2 79.9
10.2 90.2
96.4 4.43
24.4 69.6
2.51
99.6
7.35
93.0
2.75
Depth
4.21 9
5.10 97.0
96.7
98.2
1.49
17.4
79.
11.7 87.1
96.4 4.84
27.5 64.5
3.31
99 6
8.42
91.7
5.08
MoGe
3.38 98.6
4.05
98.1
3.
98.9
3.23
98.0
0.96
100
18.4
79.5
8.99 91.5
3.98
97.2
6.43
93.7
3.30
98.5
5.58
95.4
3.17
Ours
3.35 98.6
3.92 98.1
3.21 98.9
2.85
98.7
0.96
100
18.0
78.7
8.69 92.1
4.03 97.2
18.7
76.6
2.90
99.5
6.66
93.8
2.17
Table B.4: Evaluation results of baselines and our method on each dataset.

---

Ablation Data
NYLv2
KITTI
ETHAD
iBims.I
Sintel
DDAD
Spring
HAMMER
Avg.
Raw real Scale Prediction
Rell 6.
Rell 6. Rell 6.t RelL tx1 RelL Roll 6.
Rel 811
Rell 6x
R
Rell 61
Rell 6.t
RRll 61
Metric point map
Improved real
Estangled (Si-Log)
6.00 97.3
8.33 93.4
11.6 89.4
83.1
88.4
11.4 88.7
10.0
Improved real
Ensangedd (Shift im.)
5.26 97.6
8.81 92.6
10.4
91.7 6.14 96.6
13.0 84.8
8.97
91.0
10.4 90.3
9.00
92.1
Improved real
Decoupled (Come)
5.37 97.8
9.56 91.8
9.46
94.1 6.49 95.6
13.3 83.5
8.93
91.9
14.2 85.1
9.62
91.4
Syathetic only Decoupled (MLP)
8.58 94.7
9.48 91.9 14.9 83.4 8.20 94.2
16.5 80.4
11.0
88.8
18.4 78.2
12.4
87.4
Raw real Decopped (MLP)
5.36 97.8
7.70
94.5
8.58
94.6 6.60 95.7
12.2 85.5
9:01
91.5
13.7 85.4
9.02
92.1
Improved real Decopped (MLP)
5.47 97.6
8.98
92.6
8.75
94.3 6.24
12.8
84.6
9.26
90.9
12.9 87.4
9.20
Metric depth map (wo/ GT intrinsics)
Improved real Entaggeed (St-Log)
9.65 91.4
14.5
77.3
16.4
73.7 20.1
56.2
19.1 67.7
22.3 54.3
23.0 59.8
17.9 68.6
Improved Entangled (Shift
93.1
19.1 56.8
15.5
18.0
59.8
22.0 55.3
16.9
Improved real
Decoupled (Conv)
9.22 92.7
20.3 51.8
13.8
79.8
15.8 71.0
18.1 66.6
19.0
61.8
27.5 54.9
17.7 68.4
Syathetic only Decoupted (MLP) P)
73.7
15.8 71.0
24.7
15.8
21.9
22.7 56.5
32.8
21.7
Decopped (MLP)
9.22 92.9
13.8
80,5
13.8
82,
16.7 72.4
16.5 72.3
19.7
61.5
20.8 67.9
15.8 75.7
Improved real Decoupled (MLP)
9.48 92.2
18.7
59.2
13.5
82.6
13.6 79.3
17.0 69.2
20.0
59.7
23.4
67.4
16.5
72.8
Scale-invariant point map
Improved real
Ensaggled (Si-Log)
6.03 97.4
9.68
95.3
8:13 95.3
8.65 96.8
4.01
100
26.6
59.4
13.8
84.8
10.3
89.8
31.2
48.0
10.4
95.6
12.9 86.2
Improved real
Entangled (Shift in.)
5.00 97.8
10.7
95.8
7.02
96.7 7.42
97.4
3.42
100
26.0
58.2
12.7
86.6
8.97
92.2
28.9
49.8
10.5
97.5
12.1 87.2
Improved real
Decoupled (Come)
4.84 97.8
12.1
94.8
6.55
96.9
7.15 96.9
3.19
100
26.3
55.8
12.9
85.5
9.11
91.8
29.9
46.9
10.4
97.0
12.2 86.3
Syathetic Decouptad (MLP)
6.66 96.9
11.3 93.0
6.85
95.8
5.99
96.7
3.14
100
25.4
61.3
15.0
81 10.5
89.4
30.9
46.8
7.39
97.5
12.3 85.8
real
Decoupled (MLP)
4.88 98.0
9.15 96.0
6.08
97.1 7.31
24.8
60.8
11.8
87.7
8.34
92.3
28.1
53.2
96.2 10.9
11.4
Improvad real Decoupled (MLP)
5.00 97.8
11.2
95.0
6.21
97.4 6.52
97.3
2.97
100)
25.6
60.3
12.6
87.0
8,76
92.3
28.3
51.0
9.10
98.3
11.6 87.6
Alline-iavarianl
point map
Improved Entangled Logg
3.00 97.7
8.22
93.0
6.72
96.
5.71
96.8
2.57
21.1
71.0
84.5
91.9
26.7
59.7
97.1
10.3 88.8
Improved real
Entangled (Shift in..)
4.17 97.9
8.58
93.0
5.42
97.0
4.74
96.8
1.78
100
19.7
72.7
11.7 86.6
6.07
93.5
23.3
66.3
5.02
98.6
9.05
90.2
Improved Decoupled (Conv)
98.0
9.65
90.8
5.12
72.2
12.0
93.4
23.4
67.9
98.0
Sywthetic only Decoupled (MI
5.48 97.0
9.11
90,2
5.93
96.2
4.94
96.6
1.56
100
20.0
73.
13.7
81.9
7.01
91.6
25.5 63.7
4.44
98.7
9.77
88.9
Raw real Decoupled (MLP)
4.06 98.2
7.37
94.5
4.89
97.5
4.74
96.8
1.61
100
19.0
73.6
10.9
87.9
5.89
93.7
23.4
67.0
5.09
98.2
8.70
90.7
Improved real Decoupled (MI
4.14 98.0
8.95
92.0
4.94
97.5
4.50
97.2
1.62
19.6
73.6
11.7
86.6
6,06
93.3
22.8
68.7
4.40
98.9
8.87
90.6
Local point map
Improved real Ensuggeed (SI- Log)
6.30
95.6
5,96
96.6
12.0
87.5
8.14
92.5
8.63
92.5
8.21 92.9
Improved real Ensangled (Shift
4.61 97.2
4.56
97.2
10.3 90.2
93.5
6.61
94.7
6.69 94.6
Improved real
Decoupled (Conc)
4.25 97.5
4.34
97.3
9.72 91.0
7.24
93.6
6.17
95.1
6.34 94.9
Syschetic only Decoupted (MLP)
9.33
93.3
6.44
94.9
Raw real Decoupled (MLP)
4.28
97.4
4.55
97.1
9.64 91.2
7.11
93.7
6.28
94.9
6.37 94.9
Improved real Decoupted (MIPP)
91.9
93.7
95.0
Scule-invariant
depth map
Improved real Entangled Log)
4.99 97.4
5.18
96.7
6.48
95.2
5,20 97.3
2.64
100
23.2
65
11.6 86.2
1.76
91.3
24.8
63.4
6.40
96.7
9.83
89.0
Improved real
Fotanglad (Shin inv)
4.17 97.8
4.57
97.5
5.03
96.7
4.42
22.4
66.7
10.3
88.0
93.5
20.8
4.69
98.6
8.46
Improved real
Decopped (Com-)
4.08 97.9
4.61
97.3
4.80
96.9
4.32
97.1
1.92
100
22,5
64.9
10.3
87.7
6.26
93.2
21.0
68.8
4.81
98.2
8.46
90.2
Synchetic only Decoupled (MLP)
5.05 96.9
5.47
96.3
5.64
95.9
4.76
96.9
1.50
100
21.7
68.0
12.0
85.1 7.16
91.2
22.2
67.4
4.66
97.9
9.05
89.6
real
Decoupled (MLP)
4,09 98.0
4.60
97.2
4.82
97.1 4.49
1.92
100
21.8
66.5
9.79
88.4
6.09
93.2
21.8
68.6
4.67
98.1
8.41 90.4
Improwad real Decupled (MIPP
4.16 97.8
4.59
97.2
4.62
97.4
4.20
97
1.89
100
21.9
67.8
10.1
88.4
6.08
93.3
20.4
71.6
4.34
98.9
8.23
91.0
Alline
depth
Improved real Estanglad (SI-Log)
4.32 98.0
4.91
97.0
5.21
96.9
4.15
97.8
2.11
100
17.4
76.3
10.8
87.9
5.17
95.7
20.3
72.6
5.26
97.6
7.96
92.0
Improwed real
Estinglal (Shin im:)
3.57 98.2
4.26
97.6
3.98
97.9
3.35
98.2
1.44
100
16.0
78.4
9.58
89.7
4.01
96.8
17.7
76.7
3.58
98.7
6.75
93.2
Improved Decopped (Cum-)
3.48 98.3
4.31
97.4
3.77
3.23
15.2
79.2
89
3.99
96.4
17.7
76.4
3.74
98.5
6.62
Syschetic only Decoupled (MLP)
4.19 97.8
5.04
96.6
4.40
97.6
3.73
97.7
1.27
100
15.6
78.9
11.1
86.8
95.7 4.50
19.1
75.0
3.58
98.8
7.25 92.5
real Decoupted (MLP)
98.4
4.29
97.3
3.75
98.3
3.37
96.7
18.6
75.7
3.48
98.4
Improved real Decopled (MLP)
3.53 98.3
4.30
97.3
3.69
98.3
3.16 98.3
1.33
100
79.3 15.3
9.28
90.1
3.95
96.6
17.6
76.8
3.19
99.0
6.53 93.4
Affinc-invrriant
disparity
Improved real Estanglad (SI-Log)
4.69 98.1
4.99
97.2
5.73
96.7
4.63
21.9
71.
88
95.8
23.8
68.4
5.46
98.3
Improved real
Entanglel (Shint in.)
4.03 98.3
4.39
97.7
4.48
8
3.85
98.2
1.47
100
20.2
73.8
84
89.8
4.75
96.5
21.3
69.5
3.74
99.0
7.80
92.1
Improved real Decoupled (Come)
3.96 98.4
4.49
97.5
4.29
97.9
3.75
98.4
1.39
100
19.9
74.4
9.89
89.7
4.75
96.3
21.1
70.0
3.81
98.8
7.73
92.1
Systhetic only Decoupted (MLP)
4.75 97.8
5.17
96.7
4.86
97.6
4.29
98.0
1.31
100
20,8
74.0
11.5
87
5.21
95.9
22.2
69.7
3.66
99.0
8.38 91.6
Raw real Decoupled (MLP)
3.92 98.4
4.50
97.4
4.23
98.0
3.95
98.2
1.35
100
19.9
74.8
9:22
90.6
4.78
96.3
21.4
69.6
3.61
98.7
7.69
92.2
Improved real Decopled (MIPP)
4,03 98.3
4.45
97.5
4.11
98.1
3.74
1.37
19.8
75.4
9.71
90.1
4.79
96.2
20.0
72.5
3.30
99.3
7.53 92.6
Table B.5: Evaluation results of ablation study on each sets
10


Reviews:
[{"review_id": "OcYtrzIQfK", "reviewer": "Reviewer_RFUA", "summary": "## Paper Summary\nThis paper addresses the challenging task of monocular geometry prediction through two key contributions:\n- Decoupled Geometry and Scale Prediction:\n    - Geometry prediction follows a method similar to prior work (MoGe).\n    - Scale is predicted using an MLP that processes the DINOv2 CLS token.\n- Novel Data Pipeline for Real-World Training:\n    - A two-step preprocessing approach improves training on real data:\n        - Removal of incorrect depth estimates.\n        - Inpainting of missing depth values.\n\nThe method is evaluated on multiple standard benchmarks, demonstrating significant improvements over previous state-of-the-art approaches.\n\n## Review Summary\nThe technical contributions are well-motivated and sounding, supported by comprehensive experiments. I don't see any major weaknesses.", "questions": "1. Was the DINOv2 backbone fine-tuned or kept frozen during scale prediction training?\nIf frozen, what is the hypothesized relationship between the CLS token and scene scale? For instance: Could a correlation analysis (e.g., x-axis: object/scene categories, y-axis: predicted scales) reveal biases (e.g., indoor, outdoor, and object-centric)? Does the MLP implicitly learn to classify scene types (e.g., \"object-centric\" vs. \"wide-area\") to infer scale?\n\n2. In Fig. 6 (3rd example), testing on a generic building may not fully validate scale accuracy. Maybe use landmarks with known heights (e.g., Eiffel Tower, Burj Khalifa, Taipei 101) to quantitatively compare predicted vs. true scales across methods. This could better demonstrate whether the decoupled scale predictor generalizes to extreme but measurable cases.", "limitations": "Yes", "rating": 5, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 4, "significance": 4, "originality": 4, "strengths_and_weaknesses": "## Strengths\n- The paper is very well written and easy to follow with strong analysis and visualisations.\n- The idea of decoupling scale prediction from geometry is simple and effective. It's interesting to see DINOv2 cls token can be used for scale prediction.\n- The proposed two-step data pipeline (incorrect depth removal + inpainting) effectively enables training on real-world data while mitigating inaccuracies in depth measurements.\n- Experiments are comprehensive, covering multiple benchmarks, and results show promising improvements over prior work.\n\n## Weaknesses\nI don't see any major weaknesses. I do have a few questions (see Questions section).", "was_revised": true, "final_justification": "I appreciate the detailed author response and I don't have further questions. I'd like to keep my original positive score.", "rebuttal": "We sincerely thank the reviewer for the constructive and encouraging feedback. Your clear summary and thoughtful questions help us better articulate the implications and potential extensions of our work.\n\n1. **DINOv2 backbone finetuning; Scale correlation with scene types.**\n\n    All model parameters, including the DINOv2 backbone, are trainable during training. We follow the same initialization and optimizer setup as MoGe: DINOv2 pretrained weights for the backbone, and randomly initialized heads, trained end-to-end (see lines 219–225 in Implementation Details).\n\n    The reviewer raises an insightful point regarding the potential correlation between the CLS token and scene types. Indeed, the CLS token of DINOv2 encodes strong global semantics. For example, according to DINOv2's official benchmarks, the frozen ViT-Large backbone's CLS token achieves **83.5% (k-NN)** and **86.3% (linear probe)** top-1 accuracy on ImageNet classification, demonstrating its strong capacity to represent scene and object semantics. We believe this capacity to implicitly recognize scene types and iconic objects with known sizes is directly relevant to metric scale estimation. \n\n    As suggested by the reviewer, we analyzed the predicted global scale factor from the MLP across two datasets:\n\n    - **Indoor (NYUv2)**: Mean = 1.79, Std = 0.44\n\n    - **Street-view (KITTI)**: Mean = 11.9, Std = 2.0\n\n    This clustering suggests that our MLP head over the CLS token indeed captures meaningful scene-type-aware priors for metric scale estimation.\n\n2. **Including more examples of recognizable landmarks**\n\n    We appreciate the reviewer’s thoughtful suggestion. While the rebuttal format limits us from including more examples, we clarify that the scene in **Figure 6 (3rd row)** comes from a real-world photo of **135 East 57th Street (Tower Fifty Seven)** in Manhattan, New York. This building has a known height of **131.1 m**, and our model estimates it as **116 m**. In the same image, a **New York yellow taxi** (**Ford Crown Victoria**, approximately **1.45** m tall) is also present. Our model predicts it as **1.49 m**. These consistent predictions across both large-scale and small-scale objects suggest that our model can effectively ground metric estimates using recognizable reference cues in the scene.\n\n    We agree that using landmarks with known heights is a valuable way to validate scale estimation accuracy. At the same time, we note that certain landmarks with **highly distinctive and atypical structures** may present challenges for our model, due to the lack of familiar contextual cues. For example, when tested on images of the **Eiffel Tower**, the model estimates a height of **70–100 m**, which may appear reasonable in isolation but underestimates the **true height (330 m)**. Interestingly, on the Las Vegas replica, which shares a similar appearance but stands at roughly half the height (**165 m**), the model predicts a similar range of **70–120 m**, which is closer to the true height. These cases highlight potential areas for future improvement and underscore the importance of visual context in scale reasoning. We will release an open demo to help users explore and better understand such generalization behaviors.\n\n---\n\nPlease let us know if you have further questions."}, {"review_id": "nGZFXb3Fn4", "reviewer": "Reviewer_vmnM", "summary": "This paper focuses on improving the geometric accuracy, metric scale estimation, and geometric detail quality for monocular metric point cloud reconstruction from a single image. The authors propose an extension of the previous MoGe method to jointly estimate an affine-invariant point cloud along with a global metric scale. The metric scale is predicted by extracting image-level information stored in the CLS token through an additional MLP head. To better utilize real-world data, the authors filter out inaccurate ground truth regions and complete them using a MoGe model trained solely on synthetic data.", "questions": "See above", "limitations": "yes", "rating": 3, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strengths contributions:\n1. The paper is clearly written and easy to follow, which helps in understanding both the technical contributions and experimental results.\n2. The authors provide extensive quantitative evaluations, including relative and metric geometric accuracy and F1-score sharpness, demonstrating the robustness and reliability of the proposed approach.\n3. The proposed robust, accurate, and fine-grained monocular point cloud estimation method has practical value and can benefit the research community in relevant areas.\n\n\n\n\nLimitations weaknesses:\n\n\nMy primary concerns relate to the novelty of the proposed contributions. While the method is robust and practically valuable, its conceptual novelty appears somewhat limited.\n\n\nMajor Concerns:\n\n1. Novelty of Metric Scale Estimation. \n\nThe design for metric scale estimation shown in Figure 3(a) shares similarities with existing approaches such as ZoeDepth[1] and UniDepth[2]. Moreover, for Figure 3(c), the use of the CLS token to capture global image-level information for scale estimation is conceptually aligned with prior works like ScaleDepth[3] and RSA[4], which also predict global scale based on features from CLIP or language models. The main contribution of MoGe-2 appears to be extending metric scale estimation from depth estimation to point cloud estimation frameworks.\n\n\n2. Novelty of Leveraging Real-World Data.\n\nThe proposed approach filters inaccurate regions in real-world ground truth and completes them using a model trained on synthetic data. This design is similar to the training strategy of DepthAnything-v2, which initially trains on synthetic data and further refines on pseudo-labels from real data. The main difference lies in using RGB-D rather than RGB data for pseudo-label generation. However, compared to the diversity of RGB data, the advantages of RGB-D remain unclear. An ablation study of \"w/ improved real data\" and \"w/ pseudo label of the same RGB real data\", would help clarify its effectiveness better.\n\nMinor Comments:\n\nLine 178, \"However, this still limits geometry accuracy because synthetic data rarely captures real-world diversity.\" While this highlights the motivation for incorporating real-world data, to my knowledge, the diversity and quality of synthetic datasets are not inherently inferior to those of real-world RGB-D datasets. Therefore, this justification may not be entirely convincing from my perspective.\n\n\n[1] Bhat, Shariq Farooq, et al. \"Zoedepth: Zero-shot transfer by combining relative and metric depth.\" arXiv preprint arXiv:2302.12288 (2023).\n[2] Piccinelli, Luigi, et al. \"UniDepth: Universal monocular metric depth estimation.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.\n[3] Zhu, Ruijie, et al. \"Scaledepth: Decomposing metric depth estimation into scale prediction and relative depth estimation.\" arXiv preprint arXiv:2407.08187 (2024).\n[4] Zeng, Ziyao, et al. \"RSA: Resolving Scale Ambiguities in Monocular Depth Estimators through Language Descriptions.\" The Thirty-eighth Annual Conference on Neural Information Processing Systems.", "was_revised": false, "final_justification": "", "rebuttal": "### On the novelty of scale prediction.\n---\n\nWe appreciate the reviewer’s feedback and the insightful references. \n\n#### **Clarification about the references**\n\n1. > *\"The design for metric scale estimation shown in Figure 3(a) shares similarities with existing approaches such as ZoeDepth[1] and UniDepth[2]\"*\n    \n    *Figure 3 has three subfigures, 3-1, 3-2(a) and 3-2(b). We assume the reviewer is referring to either* ***3-1*** *or* ***3-2(a)*** *(please let us know otherwise)*\n\n    We would like to clarify that both **Figure 3-1** (\"Entangled\") and **Figure 3-2(a)** (\"Decoupled, Conv head\"), which the reviewer associates with ZoeDepth and UniDepth, are in fact part of our **ablation baselines** for the purpose of validating our **final design in Figure 3-2(b)**. \n\n    While these baseline settings may resemble prior works, they are **not the MoGe-2 architecture**, but part of our contribution to provide controlled comparisons and demonstrate the effectiveness of our decoupled metric scale estimation design.\n    \n    - **UniDepth** separates camera and depth modules, **but does not decouple global metric scale from depth**. In fact, our ablation experiment **\"Entangled + SI-log Loss\"**  is designed to replicate UniDepth's formulation and learning objectives (SI-log for depth, MSE for camera UV map) to offer a fair and controlled baseline comparison. \n\n    - **ZoeDepth** adopts an **AdaBins-style** formulation with **per-pixel bin classification** for both depth and metric modules.  While ZoeDepth includes a scale estimation component, its formulation and objective are different from our **simpler direct regression design**.\n    \n2. > *\"for Figure 3(c), the use of the CLS token to capture global image-level information for scale estimation is conceptually aligned with prior works like ScaleDepth[3] and RSA[4]\"*\n\n    *We assume the reviewer is referring \"Figure 3(c)\" to the 3rd subfigure, Figure 3-2(b).*\n\n    Regarding **ScaleDepth** and **RSA**, while we acknowledge their use of global features to resolve metric scale, they both **rely on text input** and **language models** (e.g. CLIP) to provide such semantic feature. In contrast, our method explores how to leverage **purely image-based features** from **visual-only pretraining** (e.g., DINOv2), tailored for metric point map estimation. Our approach tackles a different and much simpler setting, and is also evaluated as effective by experiments.\n\n#### **Clarification of the novelty of our scale prediction**\n\nTo our knowledge, no prior work has proposed or systematically validated directly predicting the global scale from CLS tokens in this context, with exclusive supervision and loss alignment as we propose. \nBehind the simple yet effective final design is the result of both a key insight of decoupling relative geometry and metric scale and careful empirical validations. We believe this type of simple design with strong performance, supported by analysis and experiment, constitutes a meaningful contribution.\n\n&nbsp;\n\n### On novelty of leveraging real-world RGB-D data\n---\n\n#### **Differences from Depth Anything V2**\n\n> *\" This design is similar to the training strategy of DepthAnything-v2... The main difference lies in using RGB-D rather than RGB data for pseudo-label generation.\"*\n\nWe wish to point out that the **goals** and **mechanisms** of Depth Anything V2's pseudo labeling and our real-world data pipeline **differ fundamentally**. They serve **orthogonal purposes** as summarized below:\n\n| Method                | Synthetic Data (e.g. Hypersim) | Real-World Labeled Data (e.g. Taskonomy) | Real-World Unlabeled Data (e.g. OpenImageV7) | Purpose                                   |\n| --------------------- | ------------------- | ------------------------------------ | ------------------------------ | ----------------------------------------- |\n| **Depth Anything V2** | √ Used | × Not used   | √ Pseudo-labeled                | Transfer knowledge from a *stronger teacher model* via pseudo labels on RGB-only data. |\n| **Ours**              | √ Used | √ Refined    | × Not used                              | Learning accurate depth supervision from *real-world RGB-D* while mitigating sensor and structural artifacts  |\n\nDepth Anything v2 focuses on distillation using the pseudo labels generated by a stronger teacher model, where the *unlabeled* *diverse* RGB images serve as a bridge for knowledge transfer. In contrast, we do incorporate real RGB-D ground truth labels, and propose a new filtering and refinement pipeline to address its artifacts to **improve metric geometry accuracy without sacrificing sharpness**.\n\nUsing a larger teacher model for pseudo-label distillation is **orthogonal** to our real-world data refinement pipeline. In fact, the two strategies could be used **on top of each other**, e.g., further distilling from a ViT-Giant MoGe-2 trained on refined real data.\n\nTo isolate the impact of pseudo-labeling itself—rather than gains from scaling model size, we followed the reviewer’s suggestion and **replaced our filtered real GT depth with pseudo-labels** from a model of **the same capacity** trained with synthetic data in ablation studies (ViT-B). As shown in the table below, the resulting performance is close to using synthetic-only training, and significantly inferior than using real labels.\n\n| Configuration | Metric Point Rel↓ | Metric Point δ₁↑ | Metric Depth Rel↓ | Metric Depth δ₁↑ | \n| ------------- | --- | -----| -----|--- |\nSynthetic data only| 12.4 | 87.3 | 21.7 | 65.0 |\nw/ Pseudo label| 12.5 | 87.4 | 21.6 | 65.8 | \nw/ Improved real label| **9.20** | **91.9** | **16.5** | **72.8** \n\nThese results indicate that **pseudo labels alone do not provide free gains from a same-capacity model**, and highlight the value of leveraging refined real RGB-D label for metric supervision.\n    \n#### **The necessity and effectiveness of real-world RGB-D data**\n    \n> *\"The diversity and quality of synthetic datasets are not inherently inferior to those of real-world RGB-D datasets\"*\n\nWe respectfully disagree with the reviewer's assertion that (currently available) synthetic data alone can sufficiently cover the real-world RGB-D distributions.\n\nAs clearly shown in **Table 4** in the paper, compared to those trained with real data, models trained solely on synthetic data suffer from a **notable drop in accuracy** (e.g. metric depth AbsRel increases 15.8%→21.7%, affine-invariant disparity 7.53%→8.37%) on all metrics except for sharpness.\nThis reflects the **known limitations of synthetic data**: *Domain gap* and *limited scene coverage* (also recognized in Depth Anything V2's Section 3, but they choose to use giant model distillation to improve large and base model's performance).\n\nOur work builds on this understanding, and provides a principled and effective pipeline to **incorporate real RGB-D data** while **mitigating its artifacts to avoid degrading sharpness**. This is a core motivation backed by both existing literature and our ablation results.\n\n---\n\nPlease let us know if you have further questions."}, {"review_id": "CNslU6q5mG", "reviewer": "Reviewer_2tk2", "summary": "MoGe2 extends the prior work MoGe to recover metric-scale 3D point maps from a single image while preserving local shape details. Two scale-prediction strategies (1) embedding scale into a shift-invariant point map, and (2) decouple and predict a global scale with affine-invariant representation. To sharpen fine structures, MoGe2 introduces a data-refinement pipeline that first filters out noisy real-world depth via local alignment against synthetic finetuned MoGe,  and then completes missing regions with Poisson inpainting guided by synthetic pseudo-labels. Experiments on different benchmarks demonstrate SoTA performance in relative and metric error metrics and boundary F1 scores.", "questions": "1. **Why MLP > Conv head?**  It is still unclear to me why CLS token prediction works better than conv head.  The authors attribute the CLS-token MLP’s superior performance to “global information”. Could you simply concatenate global DINOv2 features into the conv head to achieve the same effect?\n\n2. **Use GT scale**? Instead of computing $\\hat s^{\\ast}$ by aligning the predicted point map to the GT with the ROE solver, could you simply derive a scale target directly from the GT pointmap, e.g., using the median or mean norm of the GT points as supervision? What are the pros and cons of this simpler “closed-form” approach compared to your alignment-based $\\hat s^{\\ast}$?\n\n3. **Depth filtering / completion**. What happens when large portions of the depth map are filtered out? In cases of heavy misalignment (e.g., reflections), does the subsequent Poisson completion introduce noticeable bias or smoothing artifacts in the reconstructed geometry?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "- Strengths\n\n\t1.\t**Motivation is well stated**. The need for metric scale and fine-detail recovery in real-world applications is clearly motivated.\n\t2.\t**Effective scale decoupling**. A lightweight CLS-token MLP head for scale prediction is more effective compare to the convolutional head prediction.\n\t3.\t**Effective real-data filtering**. Training with refined real data with local alignment filtering and synthetic-guided inpainting, yields better geometry without sacrificing accuracy, demonstrating that incorporating real data rather than only synthetic is key to fine-detail recovery \n\t4.\t**Comprehensive evaluation achieves SoTA**. Across multiple diverse datasets, MoGe2 sets new SoTA in both relative-geometry and metric-depth benchmarks.\n\t5.\t**Simplicity**. The proposed pipeline builds on MoGe’s framework  robust alignment solver and multi-scale L1 loss with minimal extra supervision or complex architectural changes.\n\n- Weaknesses\n\t1.\t**Limited novelty**. Key contribution (additional scale prediction and real data filtering / completion) are straightforward extensions of MoGe; It feels more like a solid engineering effort that carefully stitching together existing techniques and finetuning them.\n\n\t2.\t**Supervision for metric scale is under-explained**. Equation (3) uses the alignment-derived scale $\\hat s^{\\ast}$ from MoGe as a training target. This supervisory signal is only mentioned in passing on lines 124–125. The paper doesn't detail how $\\hat s^{\\ast}$ is computed via the ROE solver. A detail description of the optimal-scale calculation and the justification is needed.", "was_revised": true, "final_justification": "Thanks author for the detailed replies and additional experiments. I think my concerns are fully solved. I'll keep my ratings.", "rebuttal": "We thank the reviewer for the detailed and constructive feedback, as well as the recognition of our motivation, design, and experimental results. We reply to the reviewer's concerns as follows.\n\n1. **Concerns about Novelty.** \n\n    We would like to clarify the novelty and key insights behind our design:\n    - **Network design for scale prediction:** While the final design of using a CLS-token MLP appears simple, it is the result of both **a key insight and careful empirical validation**. \n    This insight is that **metric scale is inherently a global property** and is better predicted by decoupling from affine dense prediction and leveraging global feature. \n    Importantly, this was **not an arbitrary architectural choice**. We experimentally compared several alternative designs and found that the CLS-token MLP yielded better results (Tab. 4). \n    To the best of our knowledge, this specific strategy has not been explored or experimentally validated in prior works on metric depth or 3D point map recovery.\n    - **Real-world data refinement**: To achieve ***both*** real-world geometry ***accuracy*** and ***sharp*** details, we propose a nontrivial refinement pipeline that addresses structured artifacts commonly present in real-world RGB-D data (e.g., SfM, LiDAR). Our empirical observations reveal a duality in such labels that they are partially accurate but often degraded by systematic noise. The key challenge and also motivation behind our design is to ***exploit the reliable regions while avoiding degradation from corrupted areas***. Our method selectively filters out noisy regions and fills them in using a model trained on clean synthetic data. This pipeline is essential for bridging the synthetic-to-real domain gap, enabling robust and high-fidelity supervision across diverse real-world sources.\n\n    In summary, our designs emphasize simplicity without compromising effectiveness. We believe this type of simple design with strong performance, supported by analysis and experiment, constitutes a meaningful contribution.\n\n2. **MLP v.s. Conv head.** \n\n    We thank the reviewer for the insightful suggestion. The proposed alternative, which repeats and concatenates the CLS-token feature to the Conv head input for metric scale prediction, indeed introduces global information and partially decouples the scale from structure.  We implemented the reviewer's suggested design and found that **it does improve performance over Conv head without CLS-token feature**, confirming that global features are important for predicting scale. However, **it remains less effective than directly applying an MLP to the CLS-token**. As summarized in the table below, the MLP head demonstrates slightly better overall performance (*wins in 11 out of 17 metrics, ties in 2*), and remains the most straightforward and effective design while being simple and lightweight in practice.\n\n\n    | Configuration | \\| | Metric |  |  | \\| |   |   |   |   | Relative |   |  |  |  ||  | \\|Sharpness      |\n    | ------------- | --- | -----| -----|-- | ----- | -----|----- | -----|--|----|------ | ------ | ----- | ----- | ------|-----|------- |\n    |               | \\|Point           |       | \\|Depth     ||\\|Point |  |    |     |      | |\\|Depth |||||  |\\|\n    | | | | | | \\|Scale-inv. | | Affine-inv. | | Local |  | \\|Scale-inv. || Affine-inv. || Affine-inv.(disp)| |\\| |\n    |  | \\|Rel↓ | δ₁↑ | Rel↓ | δ₁↑ | \\|Rel↓ | δ₁↑ | Rel↓ | δ₁↑ | Rel↓ | δ₁↑ | \\|Rel↓ | δ₁↑ | Rel↓ | δ₁↑ | Rel↓ | δ₁↑ | \\|F1   |\n    | Conv head | \\|9.62 | 91.4 | 17.6 | 68.4 | \\|12.2 | 86.3 | 9.15 | 90.0 | 6.34 | 94.9 | \\|8.46 | 90.2 | 6.62 |93.2 | 7.74 | 92.1 | \\|**12.7** |\n    | CLS token + Conv head  | \\|**9.02** | **92.0** | 16.7 | 69.7 | \\|11.9 | **87.6** | 8.93 | 90.5 | **6.21** | **95.1** | \\|8.36 | 90.6 | 6.57 | **93.5** | 7.62 | 92.3 | \\|12.3 |\n    | CLS token + MLP head  | \\|9.20 | 91.9 | **16.5** | **72.8** | \\|**11.6** | **87.6** | **8.87** | **90.6** | 6.26 | **95.1** | \\|**8.23** | **91.0** | **6.53** | 93.4 | **7.53** | **92.6** | \\|12.5 |\n\n\n3. **Using of GT scale factor ($s^*$).** \n\n    We thank the reviewer for pointing out that the details of the ground-truth scale $s^\\*$ were under-explained. As proposed in MoGe, the $s^*$ is the optimal scale solution under a scale-and-shift alignment with respect to a weighted *least absolute deviation* (LAD) problem:\n    $$\n    (s^\\*, \\mathbf{t}^\\*) = \\arg\\min_{s, \\mathbf{t}} \\sum_{i \\in \\mathcal{M}} w_i \\Vert s \\hat{\\mathbf{p}}_i + \\mathbf{t} - \\mathbf{p}_i\\Vert_1\n    $$\n    The ROE solver divides this problem into a series of searching subproblems in parallel, utilizing the piece-wisely linear property of the objective landscape. We will include additional explanation to supplement the preliminary section.\n\n    Regarding the **choice of GT scale**, we believe the use of the ROE-derived scale $s^\\*$ as ground truth is well-motivated and principled. By definition, $s^\\*$ **minimizes exact ROE loss to best match $s \\hat{\\mathbf{P}}$ with $\\mathbf{P}$**, and is thus a natural supervisory signal aligning with the training strategy of the affine-invariant ROE loss in MoGe  to inherit its superior performance.\n\n    To address the reviewer’s suggestion of using **a simpler closed-form target (e.g., mean distance from centroid)**, we implemented a variant using median-centered normalization and scale defined by average distance—similar to the approach in MiDaS. However, this alternative led to **degraded performance** on metric prediction:  \n    - Metric depth AbsRel error increased from **16.5% → 17.8%**,  \n    - Metric point AbsRel error increased from **9.20% → 10.2%**.\n\n    These results confirm that using ROE-aligned $s^*$ yields better supervision under our task formulation.\n\n4. **Depth filtering / completion on large missing areas.** \n\n    Our refinement pipeline remains effective even when large regions are filtered out. Although the inpainted content may not be perfectly accurate, the **Poisson inpainting preserves structural sharpness and edge alignment due to its gradient-based formulation**. Crucially, the filled regions are at worst comparable in quality to the original synthetic predictions, ensuring that supervision quality is not degraded. In contrast, leaving these regions empty would create large unsupervised areas during training, which could harm model performance in those regions.\n\n---\nPlease let us know if you have any further questions."}, {"review_id": "js6IEz4q13", "reviewer": "Reviewer_bgf3", "summary": "In this paper, MoGe-2 is proposed to extend MoGe for predicting metric geometry. The main contribution of this paper is introducing an additional branch for scale prediction, in which the global information in tokens from DINOv2 is utilized to predict an accurate metric scale. Additionally, a real data refinement approach is proposed for accurate geometry estimation. The visualization in Figure 5 shows the effectiveness of the proposed mismatch filtering scheme. The experiment results show the better performance of MoGe-2 compared to the latest ones.", "questions": "Please refer to the weakness part.", "limitations": "Yes.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The paper is well-organized and easy to follow. The motivation of this paper is reasonable.\n\n2. Decouple relative geometry and metric scale predictions is a nice idea, and the explanation of the reason for the final configuration is detailed.\n\n3. The method proposed to process the raw real data is straightforward and effective, which has been shown in Figure 4 and Figure 5. The visualization clearly explains the real data refinement.\n\n4. The qualitative and quantitative results show the effectiveness of the proposed method compared to the latest methods.\n\n\nWeaknesses:\n1. It is not clear how to choose the value of $\\hat r_j$ in equations (4)  and (5), which should be an important parameter in using mismatch filtering.\n\n2. I am curious about how to get the global shift $t$ in MoGe-2, since the global shift $t$ can be determined by a robust and optimal (ROE) alignment solver in MoGe, which only outputs point maps with unknown scales.\n\n3. It would be better if the authors could explain more about 'the absolute bias of the former' in the Lines 198-199.\n\n4. It would be better if the authors could show some failure cases with visualization, which will give an idea for further research works.", "was_revised": true, "final_justification": "I appreciate the authors’ thorough responses, which have addressed all of my initial concerns. I recommend incorporating these discussions into the final version for completeness. Accordingly, I will retain my original positive rating.", "rebuttal": "We appreciate the reviewer’s valuable feedback and insightful questions.\n\n1. **Regarding the choice of radius $r$ in mismatch filtering.**\n    \n   Our strategy follows the same multi-scale radius selection as in MoGe's local loss, which has been verified as effective for leveraging context at different spatial scales. Specifically, we select three radii such that their projected image regions approximately cover **1/4, 1/16, and 1/64 of the image size**, respectively. This multi-scale design allows the filtering to capture and suppress misalignments of varying scales using both coarse and fine local evidence.\n    We will include this explanation and the exact computation formula in the revised version for completeness.\n\n2. **Clarification on global shift in training.**\n\n    We assume the reviewer is referring to the *training-time* global shift used in supervision. As stated in Line 154–155, *\"we maintain the geometry branch for affine-invariant point map as in MoGe, ...\"* We are happy to elaborate further for clarity.\n\n    In both the main model and all scale-decoupled ablations, the affine point map branch follows **exactly the same ROE alignment strategy as MoGe** in training supervision. Specifically, the predicted affine point map is aligned to the ground truth point map via ROE alignment, which jointly solves for a global scale and shift to minimize the ROE loss (i.e., L1 under best affine alignment). \n    \n    $$\n    (s^*, \\mathbf{t}^\\*) = \\arg\\min_{s, \\mathbf{t}} \\sum_{i \\in \\mathcal{M}} w_i \\Vert s \\hat{\\mathbf{p}}_i + \\mathbf{t} - \\mathbf{p}_i\\Vert_1\n    $$\n    \n    This alignment yields:\n\n    - the optimal global scale $s^*$, which is also used as ground-truth for supervising the metric scale prediction branch as decribed in Eq.(3).\n\n    - the optimal global shift $\\mathbf t^*$.\n\n    The metric scale prediction branch thus learns to regress the global scale $\\hat s$ directly, with supervision from the ROE-derived $s^*$ as decribed in Eq.(3).\n \n    Only in the special ablation, *Entangled Shift-invariant*, where the predicted point map is not affine-invariant but metric-scaled with unknown shift, the alignment is done with shift only:\n    \n    $$\n    \\mathbf{t}^* = \\arg\\min_{\\mathbf{t}} \\sum_{i \\in \\mathcal{M}} w_i \\Vert\\hat{\\mathbf{p}}_i + \\mathbf{t} - \\mathbf{p}_i\\Vert_1\n    $$\n\n    We will make this distinction more explicit in the final version and include the relevant formulation for completeness.\n\n3. **Clarification on \"absolute bias\".**\n\n    We appreciate the reviewer’s attention to this point. The term *absolute bias* refers to the systematic deviation of geometry predictions caused by the lack of real-scene prior knowledge when training purely on synthetic data. While synthetic-trained models can often recover local relative geometry accurately, they may produce biased estimation on scene-level geometry and layout when applied to real-world images.\n\n    This is especially evident in large-scale indoor or outdoor scenes, where synthetic models are unfamiliar with real-scene object proportions or layouts. As illustrated in **Figure 5**, the model trained on synthetic data reconstructs the relative shape of the chair well, but places it **too close and too small** compared to the ground truth, revealing a global misinterpretation of the scene layout. We use the term *absolute bias* to describe such behavior, where the **local structure appears plausible**, but the **global layout is misaligned**.\n\n    This observation motivates our choice of a **local filtering strategy** (rather than relying on global consistency), which is more robust to these domain-induced absolute errors. We will clarify this term further in the revision to avoid ambiguity.\n    \n4. **Failure cases** \n\n    We agree that visualizing failure cases can provide useful insights. As noted in our paper, failure typically arises in scenes with extremely fine structures, significant foreground-background scale disparity, or strong out-of-distribution ambiguity in metric scale. We will add some visualizations of the failure cases in the revised version of our paper.\n\n---\n\nPlease let us know if you have further questions."}]

Author rebuttal:

Meta-review: The paper extends MoGe for zero-shot metric depth estimation by predicting absolute scale from the CLS-token of the underlying DINOv2 architecture, and proposes a procedure for refining real-world RGB-D datasets.

The reviewers appreciated the simplicity and effectiveness of the scale prediction, the effectiveness of the proposed filtering and refinement of real-world data, the clear writing, and resulting improvements over prior work.
Reviewers were initially concerned about the novelty of the scale prediction and the refinement procedure, and missing implementation details. The rebuttal and discussion clarified differences to prior work and effectively resolved these concerns. Reviewers recommending acceptance found their concerns resolved, while the remaining reviewer vmnM neither engaged in the discussion nor updated their final rating. The AC agrees with the assessment and recommends accepting.
Decision: Accept (poster)