Title: MoGe-2: Accurate Monocular Geometry with Metric Scale and Sharp Details
Venue: NeurIPS 2025
Accepted: yes
Keywords: computer vision

monocular geometry estimation
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

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 [61], 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 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 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.

1 Introduction

Estimating 3D geometry from a single monocular image is a challenging task with numerous applications in computer vision and beyond. Recent advancements in Monocular Depth Estimation (MDE) and Monocular Geometry Estimation (MGE) have been driven by foundation models trained on large-scale datasets [67, 68, 44, 27, 61, 7]. Compared to depth estimation, MGE approaches often also predict camera intrinsics, allowing pixels to be lifted into 3D space, thus enabling a broader range of applications.

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 [56, 77], and Embodied AI [82, 81, 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 a 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-art 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

UniDepth V2

MoGe

†

Depth Anything V2*

Ours

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RG

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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, a 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, we explore two simple, intuitive, yet effective alternatives. The first uses a 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 in a decoupled manner. Both strategies mitigate the focal-distance ambiguity, but the latter yields more accurate results, likely due to 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 are 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 in their second of the two stages. In contrast, we embrace real data throughout the training to ensure high geometry 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 on full unprocessed real data.

We train our model on an extensive collection of synthetic and real datasets and conduct a comprehensive evaluation across various datasets and metrics. Experiments demonstrate that our method achieves superior performance in terms of relative geometry accuracy, metric scale precision, and fine-grained detail recovery, surpassing multiple recently proposed baselines, as shown in Fig. 1.

Our contributions are summarized as follows:

We introduce a Metric MGE framework with the representation of decoupled affine-invariant 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-art results in both geometry accuracy and sharpness, significantly surpassing prior methods in global and local geometry accuracy.

Webelieve our method enhances monocular geometry estimation'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 [13, 15, 71, 4, 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, 74, 23, 67, 68, 44, 7] have aimed to predict metric depth in open-domain settings. For example, Metric3D [74, 23] utilized numerous metric depth datasets

and introduced a canonical camera transformation module to address metric ambiguity from diverse data sources. ZoeDepth [5] built on a relative depth estimation framework [47, 6] that is pre-trained on extensive non-metric depth data and employed domain-specific metric heads. UniDepth [44, 45] instead simultaneously learned from metric and non-metric 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 a scene from a single image. Common approaches [72, 73, 44, 45] 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 [44, 45] predicted camera embeddings and facilitate depth map prediction with the estimated camera information. Along another line, DUSt3R [62] proposed an end-to-end 3D point map prediction framework for stereo images, bypassing explicit camera prediction. In a similar vein, MoGe [61] predicted an affine-invariant point map for monocular input, achieving state-of-the-art performance with a robust and optimal alignment solver. However, it does not account for metric scale and lacks the finer details, thereby limiting its applicability in many downstream tasks.

Depth prediction with fine-grained details. Numerous methods [40, 35, 45, 68, 27, 7, 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 [27, 16, 18] 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-real domain gaps persist and hinder the prediction accuracy. Depth Pro [7] integrates multi-patch vision transformers [11] 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-depth data misalignment artifacts Despite their overall accuracy, depth datasets captured with LiDAR [64, 53, 56, 19] or structure-from-motion (SfM) reconstructions [78, 70, 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-specific issues using stereo cues [56] or epipolar geometry [84], while SfM 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 a unified data refinement approach that can handle diverse misalignment artifacts in RGBdepth data regardless of their source or underlying error patterns.

3 Methodology

Our method processes a single image to predict the 3D point map of the scene, achieving accurate relative geometry, metric scale, and fine-grained detail. It builds upon the recent MoGe approach [61] that focuses on affine-invariant point map reconstruction (Sec. 3.1). 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 to achieve both precise and detailed geometry reconstruction simultaneously (Sec. 3.3).

3.1 Preliminaries: MoGe

Given a single image I ∈ R H × W × 3 , MoGe estimates an affine-invariant 3D point map ˆ P ∈ R H × W × 3 with an unknown global scale and shift relative to the ground truth geometry P , achieved by learning through a robust L 1 loss:

Ƹ

Conv.

neck

Image tokens

DINOv2 ViT

CLS token

Conv

head

Conv

head

MLP

head

Ƹ 𝑠

Metric

scale factor

point map

෡

𝐏

𝑠 ⋅ ෡

𝐏 ⊙ ෡ 𝐌

Mask

෡ 𝐌

Metric depth

Metric-scale points

116cm

Horizontal FOV = 63

°

Vertical FOV = 44

°

Input image

where M is the valid mask of ground truth point map, 1 /z i is a weighting scalar using inverse ground truth depth, and s ∗ and t ∗ are the optimal global scale and shift alignment factors derived by a robust and optimal (ROE) alignment solver [61],

To enhance local geometry accuracy, it further applies the robust supervision in Eq. (1) to multi-scale local spherical regions

centered at sampled ground truth point p j with different radius r j . After obtaining the affine-invariant point map, the camera's focal and shift can be recovered by a 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

We explore two alternatives to extend MoGe with metric scale prediction, with corresponding design choices illustrated in Fig. 3.

Shift-invariant geometry prediction. As illustrated in Fig. 3-1, a natural extension of MoGe is to predict a shift-invariant point map by absorbing the metric scale s into the affine point map, while computing only the global shift t via ROE alignment during training and resolving it again at inference time. This design bypasses the focal-distance ambiguity [61] and yields reasonable metric reconstruction results (Tab. 4).

However, due to the large variation in scene scale across open-domain images ( e.g. , indoors vs. landscapes), the predicted values in shift-invariant space span a wide range. This makes scale learning less stable, and inaccurate scale predictions can produce large gradients that interfere with relative geometry learning ( i.e. , the middle section of Tab. 4). This motivates our choice to decouple scale estimation from the point map prediction entirely.

Conv.

neck

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head

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point map

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neck

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neck

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Scale and relative geometry decomposition. To prevent scale affecting relative geometry accuracy, we maintain the geometry branch for affine-invariant point map as in MoGe, and introduce an additional branch for scale prediction with exclusive supervision:

where log (ˆ s ) is the predicted metric scale in logarithmic space, and s ∗ is the optimal scale calculated online by Eq. (2) between the predicted affine-invariant 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. 3-2(a), is to add a 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 is ineffective at aggregating local features from the convolution neck, while accurate metric scale prediction requires global information.

(b) CLS-token-conditioned MLP. To better decouple relative geometry and metric scale predictions, our second design (Fig. 3-2(b)) uses an MLP head to learn the metric scale directly from the DINOv2 encoder's classification (CLS) token (see Fig. 2). The global information in the token enables the network to predict an accurate metric scale. As demonstrated in Table 4, 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 to accurately reconstruct fine-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 a 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 in Fig. 4. Our refinement approach leverages the fact that models trained on synthetic data achieve exact color-depth matching and capture sharp, complete

Local error map

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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 G syn. The model is then applied to real images to infer geometry, which serves as a reference for filtering. While the predicted local structures are generally plausible, G syn 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 { p i } , corresponding predictions { ˆ p i } by G syn, and the mask M of valid ground truth, we select a spherical region ˆ S j centered at each estimated point ˆ p j with a specific radius ˆ r j :

Within this local region ˆ S j , we align the corresponding real-captured points { p i } i ∈ ˆ S j with the predictions { ˆ p i } i ∈ ˆ S j via the ROE solver and mark a real-captured point as an outlier if deviates from the predictions beyond the specified radius, forming a set O j :

with ( s ∗ j , t ∗ j ) as optimal alignment factors for local regions. The outlier sets derived from all sampled local regions of different ˆ r j are combined and excluded from the mask, yielding the final valid area

Regarding the choice of radius ˆ r j for each sampled anchor point, we follow the same multi-scale strategy in MoGe's local loss for capturing context at different spatial scales. Specifically, we set ˆ r j = α · ˆ z j · √ W 2 + H 2 2 · f , where ˆ z j is the depth of ˆ p j , ˆ f is the predicted focal length, W and H are image width and height, and α ∈ { 1 / 4 , 1 / 16 , 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 a complete depth map by integrating the detailed structures predicted by G syn with the remaining ground truth depth. Specifically, we reconstruct the depth in the filtered-out regions { d c i } i ∈M c filtered using logarithmicspace Poisson completion:

Method

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8.75

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8.22

8.76

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6.91

8.63

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5.62

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DA V2

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-

-

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10.7

87.6

6.80

8.48

90.8

6.15

8.82

91.6

5.42

6.12

-

-

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-

-

-

-

-

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7.92

91.8

3.39

7.66

92.9

4.53

9.51

89.4

6.17

4.70

MASt3R

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

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87.8

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6.75

UniDepth V1

13.6

85.0

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UniDepth V2

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87.7

2.98

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

Ours

10.8

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

where M c filtered is the complement area of M filtered , ˆ d i and d i denote predicted depth by G syn 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 4 illustrates our filtering and completion process. Our method effectively removes mismatched depths from LiDAR scans and fills in missing content in SfM-reconstructed depth maps. The 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 sharpness and maintains accurate geometry estimation, as shown in Tab. 4.

4 Experiments

Implementation details. We train our model using a combination of 24 datasets with 16 synthetic datasets [10, 58, 49, 59, 42, 33, 14, 24, 83, 51, 63, 21, 1, 65, 55, 73], 3 LiDAR scanned datasets [17, 64, 53], and 5 SfM-reconstructed datasets [3, 78, 70, 34, 69]. 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 .

We use DINOv2-ViT-Large as the backbone for the full model, and DINOv2-ViT-Base model for all ablation studies to ensure efficiency. Our convolutional heads follow MoGe's design but remove all normalization layers in order to significantly reduce inference latency. The models are trained with initial learning rates of 1 × 10 -5 for the ViT backbone and 1 × 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 .

Depth Pro

UniDepth V2

Ours

MoGe

V1

Ground Truth

72cm

81cm

50cm

83cm

2.94m

2.08m

2.98m

2.66m

N.A.

N.A.

Not

available

RGB Image

1.49m

0.70m

5.4m

128m

116m

42m

Not

available

N.A.

N.A.

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 [12] , Sintel [8], DDAD [19], DIODE [57], Spring [38], and HAMMER [26]. These datasets encompass a wide range of domains, including indoor scenes, street views, object scans, and synthetic animations.

Baselines. Wecompare our method with several monocular geometry estimation methods, including UniDepth V1 and V2 [44, 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], ZoeDepth [5] and Metric3D 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 scale-invariant point maps, affine-invariant point maps, local point maps, scale-invariant depth, affine-invariant depth, and affine-invariant disparity.

Table 1 presents the average relative error - Rel p ( ∥ ˆ p -p ∥ 2 / ∥ p ∥ 2 ) for point maps and Rel d ( | ˆ z -z | /z ) for depth map), and the percentage of inliers ( δ p 1 , where ∥ ˆ p -p ∥ 2 / ∥ p ∥ 2 < 0 . 25 , and δ d 1 , where

w/

synthetic data only

𝛿 P = 86.7%

w/

raw real data

𝛿 P = 93.7%

w/

improved real data

𝛿 P = 93.9%

RGB Image

max( ˆ d/d, d/ ˆ d ) < 1 . 25 ) across the 10 test datasets, along with the average ranking among the 8 methods. Note that ZoeDepth, DA V1, DA V2, and Metric3D V2 are not evaluated for point settings due to 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-art relative geometry estimation method, MoGe. This demonstrates that our 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 [41], KITTI [56], ETH3D [52], iBims1 [31, 30], DDAD [19], DIODE [57] and HAMMER [26]. We measure the relative point error (Rel p ) and percentage of inliers ( δ p 1 ) on estimated metric point maps. Similarly, we evaluate the metric depth accuracy via relative depth error (Rel d ) and depth inliers ( δ d 1 ). Additionally, we evaluate metric depth estimation using ground truth camera intrinsics for methods that accept this input, which helps eliminate the influence of inaccuracies in the estimated camera intrinsics. As shown in Table 2, 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 affine-invariant point estimation.

Boundary sharpness. To evaluate the sharpness of the estimated geometry, we use two synthetic datasets, Spring [38] and Sintel [8], as well as two real-world test datasets iBims-1 [31] and HAMMER [26], which contain high-quality, densely annotated geometry. We employ the boundary F1 score metric proposed by Depth Pro [7] to 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 6 presents a 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 the estimated geometry to facilitate comparison of metric scale accuracy. Our method successfully 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 a crocodile.

4.3 Ablation Study

Metric scale prediction. In Sec. 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 a naive baseline that directly predicts a metric point map with entangled scale and shift factors using the commonly adopted SI-log loss [13].

Table 4 shows 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 on synthetic data yields the highest sharpness but significantly reduces geometric accuracy. This supports the effectiveness of using synthetic-data-trained model predictions to 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 as the original datasets while maintaining reasonable sharpness, as further confirmed by the visual comparison in Figure 7.

5 Conclusion

Wehave 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-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 a practical data refinement pipeline that enhances real data with synthetic labels, largely improving geometric granularity without compromising accuracy. MoGe-2 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 a 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

2

×

2 Upsample

ResBlock(s)

2

×

2 Upsample

ResBlock(s)

ResBlock(s)

2

×

2 Upsample

2

×

2 Upsample

Linear

Linear

Linear

Linear

Linear

2

×

2 Upsample

ResBlock(s)

2

×

2 Upsample

ResBlock(s)

ResBlock(s)

2

×

2 Upsample

2

×

2 Upsample

Linear

+

Linear

+

+

+

(h, w)

(2h, 2w)

(4h, 4w)

(8h, 8w)

(16h, 16w)

Shared convolutional neck

Task-specific convolutional head A

Intermediate feature pyramid

Image features

⊕

UV encoding

Output map A

Linear

Linear

Linear

Linear

UV encoding

UV encoding

UV encoding

UV encoding

+

Input feature pyramid

+

+

+

(h, w)

(2h, 2w)

(4h, 4w)

(8h, 8w)

(16h, 16w)

Task-specific convolutional head B

Output map B

…

…

The detailed architectures of our model components are described as follows.

DINOv2 Image Encoder. Our model supports variable input resolutions by leveraging the interpolatable positional embeddings of DINOv2 [43]. The native resolution is determined by a user-specified number of image tokens. Given an input image of arbitrary size and a target number of tokens, we compute a patch-level resolution h × w that best matches the desired token count. The image is then resized to (14 h, 14 w ) to match DINOv2's input requirement, and encoded into h × w image tokens along with one classification token. We extract four intermediate feature layers from DINOv2-specifically, the 6th, 12th, 18th, and final transformer layers-project them to a 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 a shared convolutional neck and multiple task-specific heads, as illustrated in Fig A.1. Both the neck and the heads are composed of progressive residual convolution blocks (ResBlocks) [22] interleaved with transpose convolution layers (kernel size 2, stride 2) for progressive upsampling from resolution ( h, w ) to (16 h, 16 w ) . Finally, the output map is resized through bilinear interpolation to match the raw image size. To reduce inference latency on modern GPUs, all normalization layers are simply removed from the ResBlocks, without affecting performance or training stability.

At each scale level of the neck, we inject a UV positional encoding, defined as a mapping of the image's rectangular domain into a 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 to each prediction task.

CLS-token-conditioned MLP Head. For scalar prediction, we use a two-layer MLP that takes the CLS token feature from DINOv2 as input and outputs a single scale factor, followed by an exponential mapping to ensure a positive scale output. The hidden layer size is equal to the input feature dimension.

A.2 Training Data

The datasets used for training our model are listed in Tab. A.1. All datasets are publicly available for academic use, and their sampling weights follow the protocol established in MoGe [61].

Tab. A.2 provides a rough summary of the number of training frames used by several representative monocular geometry estimation methods. As there is no shared or standardized training set in this field, this table serves to contextualize the scale of training data across methods. Notably, model performance does not necessarily correlate with the amount of training data used.

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:

Affine-invariant point map. The scale a ∗ and shift b ∗ are computed as:

Scale-invariant depth map , the scale a ∗ is computed as

Affine-invariant depth map. The scale a ∗ and shift b ∗ are computed as

Affine-invariant disparity map. We follow the established protocol for affine disparity alignment [47], using least-squares to align predictions in disparity space:

where ˆ d i is the predicted disparity and d i is the ground truth, defined as d i = 1 /z i . To prevent aligned disparities from taking excessively small or negative values, the aligned disparity is truncated by the inverted maximum depth 1 /z max before inversion. The final aligned depth ˆ z ∗ i is computed as:

Metric Geometry

Metric depth . The output is evaluated without alignment and clamping range of values for all methods, unless specific post-processing is hard-coded in its model inference pipeline.

Metric point map . The point map prediction is aligned with the ground truth by the optimal translation:

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, 14 2 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 to native input resolutions ranging approximately from 484 2 to 1188 2 . This training setup enables robust generalization to a 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 a moderate range (1200 - 3000). In contrast, models such as Depth Anything [67, 68] and Metric3D V2 [23] are trained with fixed input resolution and exhibit substantial performance degradation when evaluated at resolutions that diverge from their training setting. Our method, trained over a broader resolution spectrum, remains robust under test-time scaling. As shown in Fig. B.3a, it maintains the top accuracy when scaled up for improved detail or down for faster inference-even 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. B.3b, 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 to 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 a favorable trade-off between latency and performance across a wide range of resolutions-within a single unified framework.

1200 tokens (29ms)

3600 tokens (55ms)

7200 tokens (108ms)

RGB Image

1000

2000

3000

4000

5000

6000

7000

8000

0.0

0.2

0.4

0.6

0.8

1.0

20000

Number of Image Tokens

Percentile Rank

Ours

MoGe

Depth Pro

UniDepth V2

DA V2

(a) Geometry accuracy(

↑

)

1000

2000

3000

4000

5000

6000

7000

8000

0.0

0.2

0.4

0.6

0.8

1.0

20000

Number of Image Tokens

Percentile Rank

Ours

MoGe

Depth Pro

UniDepth V2

DA V2

(b) Boundary sharpness (

↑

)

1000

2000

3000

4000

5000

6000

7000

8000

0

50

100

150

200

250

20000

Number of Image Tokens

Latency (ms)

Ours

MoGe

Depth Pro

UniDepth V2

DA V2

(c) Inference latency (

↓

)

B.2 Runtime Analysis

As shown in Table B.3, we evaluate the runtime performance of each method under their representative test-time configurations. Specifically, we measure single-frame inference latency and peak GPU memory usage on an NVIDIA A100 GPU. These metrics provide a 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 and Fig. B.5. Representative failure cases are shown in Fig. B.6.

B.4 Complete Evaluation on Individual Datasets

In the paper, we only listed the average performance across multiple datasets for qualitative comparison and ablation study. Table B.4 and Table B.5 list all the results for each individual datasets.

3.08m

N.A.

3.36m

3.14m

1.69m

6.10m

N.A.

5.49m

0.89m

1.28m

1.42m

N.A.

Depth Pro

UniDepth V2

Ours

MoGe

RGB Image

Depth Pro

UniDepth V2

Ours

MoGe

RGB Image

15.4cm

16.5cm

16.8cm

N.A.

70m

Method

NYUv2

KITTI

ETH3D

iBims-1

GSO

Sintel

DDAD

DIODE

Spring

HAMMER

Rel

↓

δ

1

↑

Rel

↓

δ

1

↑

Rel

↓

δ

1

↑

Rel

↓

δ

1

↑

Rel

↓

δ

1

↑

Rel

↓

δ

1

↑

Re

↓

δ

1

↑

Rel

↓

δ

1

↑

Rel

↓

δ

1

↑

Rel

↓

δ

1

↑

Rel

↓

δ

1

↑

Rank

↓

Metric point map

MASt3R

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

Ours

4.44

98.3

7.44

94.4

7.19

97.7

5.63

97.4

-

-

-

-

11.4 87.9

7.85

92.3

-

-

13.4

87.0

8.19

93.6

1.64

Metric depth map (wo/ GT intrinsics)

ZoeDepth

11.0

91.9

17.0

85.4

57.1

33.7

17.4

67.2

-

-

-

-

38.9 38.6

39.3

29.3

-

-

94.3

3.23

39.3

49.9

5.90

MASt3R

10.8

89.7

56.7

9.84

47.2

20.1

18.7

61.5

-

-

-

-

62.4 5.51

54.9

19.0

-

-

97.2

6.74

49.7

30.3

6.71

DA V1

10.5

94.9

11.6

94.5

40.2

24.0

12.9

81.8

-

-

-

-

34.5 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 Pro

10.7

91.9

23.5

38.3

38.5

32.8

15.9

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 intrinsics)

7.16

96.5

5.25

98.0

11.8

88.8

9.96

94.1

-

-

-

-

9.21 93.7

49.1

1.98

-

-

35.7

44.3

18.3

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 V2

7.81

96.0

5.98

97.7

15.0

85.2

7.71

95.5

-

-

-

-

14.1 89.3

41.0

67.1

-

-

37.7

47.1

18.5

82.6

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

MASt3R

6.26

96.0

10.0

93.8

6.28

95.5

7.55

95.1

5.03

99.0

31.5

50.2 15.9 77.6

12.8

85.0

39.3

33.7

10.7

95.0

14.5

82.1

5.45

UniDepth V1

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

33.1

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

4.58

98.9

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

98.1

7.46

94.1

2.14

Ours

3.94

98.3

8.27

97.5

5.45

98.6

5.34

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

MASt3R

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

90.1

33.3

51.1

5.34

96.6

11.6

86.0

5.45

UniDepth V1

3.93

98.4

4.29

98.6

12.2

89.6

4.65

98.0

2.99

99.8

28.5

58.4 10.3 90.5

8.56

90.9

29.6

58.5

4.15

98.7

10.9

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

Depth Pro

4.36

97.9

9.15

90.7

7.73

94.0

4.34

97.4

3.16

99.7

19.6

74.5 14.4 81.2

6.28

93.7

25.0

66.0

5.31

98.8

9.93

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

3.89

98.7

3.65

98.5

1.16

100

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

MASt3R

-

-

-

-

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

UniDepth 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

94.9

3.10

Depth Pro

-

-

-

-

4.76

96.9

4.11

97.5

-

-

10.8

89.5 8.08 92.4

6.80

94.1

-

-

-

-

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

ZoeDepth

5.62

96.3

7.27

91.9

10.4

87.3

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

MASt3R

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

DA V1

4.77

97.5

5.61

95.6

9.41

88.9

5.53

95.8

5.49

99.3

28.3

56.7 13.2 81.5

10.3

87.5

27.3

59.1

6.88

96.4

11.7

85.8

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

7.95

90.0

28.0

61.1

5.92

97.7

10.7

87.6

6.80

4.69

97.4

4.00

98.5

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 V1

3.86

98.4

3.73

98.6

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

3.16

99.7

23.1

65.3 11.0 91.5

5.92

94.1

24.9

65.1

3.48

99.1

8.61

90.8

3.10

Depth Pro

4.42

97.6

5.47

96.2

7.54

94.1

4.13

97.4

2.18

99.9

23.9

68.7 14.0 82.0

7.05

92.0

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

4.89

94.7

4.63

96.4

3.77

98.1

5.77

94.5

2.72

Ours

3.44

98.2

4.11

98.0

3.55

98.7

3.16

98.2

1.49

100

19.6

71.6 8.91 91.2

5.30

94.6

20.0

72.4

3.96

99.2

7.35

92.2

2.12

Affine-invariant depth

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

MASt3R

4.67

96.7

5.79

95.1

4.64

97.0

4.62

95.6

2.85

99.4

21.3

70.3 12.5 83.4

5.79

94.1

27.4

62.8

4.21

96.8

9.38

89.1

7.97

DA V1

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

100

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

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

3.55

98.7

4.92

97.5

3.76

98.2

2.48

99.9

24.9

64.1 9.46 90.8

4.90

96.2

25.2

67.3

3.55

98.9

8.61

91.0

5.67

UniDepth V2

2.96

98.6

3.85

98.1

2.95

98.5

2.64

98.4

1.37

100

13.3

83.2 10.5 90.9

4.05

96.5

20.1

75.4

2.48

99.6

6.42

93.9

2.80

Depth Pro

3.67

98.2

5.12

96.8

4.97

96.4

3.23

98.3

1.46

100

15.8

80.1 12.6 84.1

4.66

95.6

21.7

70.5

3.30

99.6

7.65

92.0

5.05

MoGe

2.92

98.6

3.94

98.0

2.69

99.2

2.74

97.9

0.94

100

13.0

83.2 8.40 92.1

3.16

97.5

4.34

96.4

3.00

98.3

4.51

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

3.14

97.4

15.9

81.2

2.85

99.3

5.62

94.8

2.02

Affine-invariant disparity

ZoeDepth

5.21

97.7

5.84

95.6

8.07

94.0

6.19

96.1

2.60

99.9

26.9

66.3 14.1 81.7

8.17

92.0

27.2

63.0

6.84

96.4

11.1

88.3

8.78

DA V1

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

13.4

81.5

3.76

98.2

4.30

97.7

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

MASt3R

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 V1

3.78

98.7

3.64

98.7

5.34

97.2

4.06

98.1

2.56

99.9

28.6

60.7 9.94 89.1

5.95

95.5

30.0

61.6

3.64

99.1

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

4.43

96.4

24.4

69.6

2.51

99.6

7.35

93.0

2.75

Depth Pro

4.21

98.1

5.10

97.0

4.94

96.7

3.74

98.2

1.49

100

17.4

79.1 11.7 87.1

4.84

96.4

27.5

64.5

3.31

99.6

8.42

91.7

5.08

MoGe

3.38

98.6

4.05

98.1

3.11

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

Ablation

NYUv2

KITTI

ETH3D

iBims-1

GSO

Sintel

DDAD

DIODE

Spring

HAMMER

Data

Rel

↓

δ

1

↑

Rel

↓

δ

1

↑

Rel

↓

δ

1

↑

Rel

↓

δ

1

↑

Rel

↓

δ

1

↑

Rel

↓

δ

1

↑

Re

↓

δ

1

↑

Rel

↓

δ

1

↑

Rel

↓

δ

1

↑

Rel

↓

δ

1

↑

Rel

↓

δ

1

↑

Metric point map

Improved real

Entangled (SI-Log)

6.00 97.3

8.33 93.4

11.6 89.4

7.78 94.6

-

-

-

-

14.4 83.1

10.6 88.4

-

-

11.4

88.7

10.0 90.7

Improved real

Entangled (Shift inv.)

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 (Conv)

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

Synthetic 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

Decoupled (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

Decoupled (MLP)

5.47 97.6

8.98 92.6

8.75 94.3

6.24 96.1

-

-

-

-

12.8 84.6

9.26 90.9

-

-

12.9

87.4

9.20 91.9

Metric depth map (wo/ GT intrinsics)

Improved real

Entangled (SI-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 real

Entangled (Shift inv.)

9.04 93.1

19.1 56.8

15.5 76.8

15.1 72.1

-

-

-

-

18.0 67.9

19.9 59.8

-

-

22.0

55.3

16.9 68.8

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

Synthetic only Decoupled (MLP)

18.1 73.7

15.8 71.0

24.7 53.2

15.8 76.6

-

-

-

-

21.9 62.4

22.7 56.5

-

-

32.8

62.0

21.7 65.1

Raw real

Decoupled (MLP)

9.22 92.9

13.8 80.5

13.8 82.1

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

Entangled (SI-Log)

6.03 97.4

9.68 95.3

8.13 95.3

8.63 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 inv.)

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 (Conv)

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

Synthetic only Decoupled (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 81.1

10.5 89.4

30.9 46.8

7.39

97.5

12.3 85.8

Raw real

Decoupled (MLP)

4.88 98.0

9.15 96.0

6.08 97.1

7.31 96.8 3.06 100

24.8 60.8 11.8 87.7

8.34 92.3

28.1 53.2

10.9

96.2

11.4 87.8

Improved 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

Affine-invariant point map

Improved real

Entangled (SI-Log)

5.00 97.7

8.22 93.0

6.72 96.1

5.71 96.8 2.57 100

21.1 71.0 12.9 84.5

7.36 91.9

26.7 59.7

6.37

97.1

10.3 88.8

Improved real

Entangled (Shift inv.)

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 real

Decoupled (Conv)

4.08 98.0

9.65 90.8

5.12 97.0

4.67 97.0 1.66 100

19.6 72.2 12.0 85.4

6.11 93.4

23.4 67.9

5.24

98.0

9.15 90.0

Synthetic only Decoupled (MLP)

5.48 97.0

9.11 90.2

5.93 96.2

4.94 96.6 1.56 100

20.0 73.1 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 (MLP)

4.14 98.0

8.95 92.0

4.94 97.5

4.50 97.2 1.62 100

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

Entangled (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

Entangled (Shift inv.)

-

-

-

-

4.61 97.2

4.56 97.2

-

-

10.3 90.2 7.38 93.5

6.61 94.7

-

-

-

-

6.69 94.6

Improved real

Decoupled (Conv)

-

-

-

-

4.25 97.5

4.34 97.3

-

-

9.72 91.0 7.24 93.6

6.17 95.1

-

-

-

-

6.34 94.9

Synthetic only Decoupled (MLP)

-

-

-

-

4.37 97.4

4.45 97.2

-

-

9.33 91.7 7.51 93.3

6.44 94.9

-

-

-

-

6.42 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

Decoupled (MLP)

-

-

-

-

4.20 97.5

4.31 97.3

-

-

9.34 91.9 7.21 93.7

6.21 95.0

-

-

-

-

6.25 95.1

Scale-invariant depth map

Improved real

Entangled (SI-Log)

4.99 97.4

5.18 96.7

6.48 95.2

5.26 97.3 2.64 100

23.2 65.7 11.6 86.2

7.76 91.3

24.8 63.4

6.40

96.7

9.83 89.0

Improved real

Entangled (Shift inv.)

4.17 97.8

4.57 97.5

5.03 96.7

4.42 97.6 2.09 100

22.4 66.7 10.3 88.0

6.16 93.5

20.8 69.2

4.69

98.6

8.46 90.6

Improved real

Decoupled (Conv)

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

Synthetic only Decoupled (MLP)

5.05 96.9

5.47 96.3

5.64 95.9

4.76 96.9 1.90 100

21.7 68.0 12.0 85.1

7.16 91.2

22.2 67.4

4.66

97.9

9.05 89.6

Raw real

Decoupled (MLP)

4.09 98.0

4.60 97.2

4.82 97.1

4.49 97.0 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

Improved real

Decoupled (MLP)

4.16 97.8

4.59 97.2

4.62 97.4

4.20 97.4 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

Affine-invariant depth

Improved real

Entangled (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

Improved real

Entangled (Shift inv.)

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 real

Decoupled (Conv)

3.48 98.3

4.31 97.4

3.77 98.1

3.23 98.0 1.35 100

15.2 79.2 9.48 89.5

3.99 96.4

17.7 76.4

3.74

98.5

6.62 93.2

Synthetic 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

4.50 95.7

19.1 75.0

3.58

98.8

7.25 92.5

Raw real

Decoupled (MLP)

3.48 98.4

4.29 97.3

3.75 98.3

3.37 98.0 1.32 100

15.2 80.1 8.95 90.2

3.91 96.7

18.6 75.7

3.48

98.4

6.63 93.3

Improved real

Decoupled (MLP)

3.53 98.3

4.30 97.3

3.69 98.3

3.16 98.3 1.33 100

15.3 79.3 9.28 90.1

3.95 96.6

17.6 76.8

3.19

99.0

6.53 93.4

Affine-invariant disparity

Improved real

Entangled (SI-Log)

4.69 98.1

4.99 97.2

5.73 96.7

4.63 97.8 2.14 100

21.9 71.1 11.3 88.0

5.69 95.8

23.8 68.4

5.46

98.3

9.03 91.1

Improved real

Entangled (Shift inv.)

4.03 98.3

4.39 97.7

4.48 97.8

3.85 98.2 1.47 100

20.2 73.8 9.84 89.8

4.75 96.5

21.3 69.5

3.74

99.0

7.80 92.1

Improved real

Decoupled (Conv)

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

Synthetic only Decoupled (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 87.3

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

Decoupled (MLP)

4.03 98.3

4.45 97.5

4.11 98.1

3.74 98.2 1.37 100

19.8 75.4 9.71 90.1

4.79 96.2

20.0 72.5

3.30

99.3

7.53 92.6

Peer reviews:
{"IS_META_REVIEW": false, "comments": "SUMMARY:\nIn 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths:\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.\n\nQUESTIONS:\nPlease refer to the weakness part.\n\nLIMITATIONS:\nYes.", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 3, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-26"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nMoGe2 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.\n\nSTRENGTHS AND WEAKNESSES:\n- 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.\n\nQUESTIONS:\n1. **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?\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 4, "REVIEWER_CONFIDENCE": 4, "CLARITY": 3, "IMPACT": 3, "ORIGINALITY": 3, "DATE": "2025-06-29"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\nThis 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.\n\nSTRENGTHS AND WEAKNESSES:\nStrengths 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.\n\nQUESTIONS:\nSee above\n\nLIMITATIONS:\nyes", "TITLE": "Official Review", "RECOMMENDATION": 3, "REVIEWER_CONFIDENCE": 5, "CLARITY": 3, "IMPACT": 2, "ORIGINALITY": 2, "DATE": "2025-07-01"}

{"IS_META_REVIEW": false, "comments": "SUMMARY:\n## 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.\n\nSTRENGTHS AND WEAKNESSES:\n## 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).\n\nQUESTIONS:\n1. 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.\n\nLIMITATIONS:\nYes", "TITLE": "Official Review", "RECOMMENDATION": 5, "REVIEWER_CONFIDENCE": 3, "CLARITY": 4, "IMPACT": 4, "ORIGINALITY": 4, "DATE": "2025-07-06"}