Learning-based structure-from-motion methods such as ACE-Zero have demonstrated strong performance in estimating cameraposes and scene coordinates from unordered image collections without requiring ground truth supervision. However, the lack of global and multi-view consistency constraints in ACE-Zero can lead to pose drift and misalignment, particularly in complex or ambiguous scenes. In this work, we propose a hybrid framework that integrates pose graph optimization (PGO) into ACE-Zero to refine camera poses and suppress incorrect refinements. We construct pose graphs directly from ACE-Zero outputs by extracting relative pose constraints from predicted scene coordinates. Furthermore, we introduce an uncertainty-aware optimization strategy by estimating confidence scores using geometric priors, including epipolar and optical flow consistencies across views. Our approach improves the robustness and accuracy of pose estimation, demonstrating that global geometric reasoning can effectively complement learning-based inference in structure-from-motion.
Figures & tables
Figure 1: Despite being initialized from (a) COLMAP generated camera poses, local refinements in (b) often introduce global inconsistencies. In contrast, (c) our hybrid-framework, which integrates pose graph optimization into ACE-Zero, enforces multi-view consistency and suppress incorrect refinements.
Figure 2: Qualitative example of the epipolar-based uncertainty metric. We overlay the reprojected points and their corresponding epipolar lines which are color-coded by error (green=low, red=high), indicating geometric alignment quality under perturbed pose. In (a), errors remain low with no perturbation applied. Introducing pose perturbations in (b) and (c) naturally increases the error. Interestingly, adding translation perturbations in (d) and (e) can sometimes reduce the error, indicating that epipolar error is sensitive to interactions between pose components and does not always correlate directly with pose accuracy. Consequently, we interpret it as a heuristic confidence indicator rather than a strict geometric validator.
Figure 3: Qualitative example of optical flow predictions. (a) One image from the input pair. (b) Predicted flow visualization where similar colours indicate similar motion directions. (c) UV correspondences overlaid on the image pair, demonstrating that optical flow provides robust pixel-level matches between views.
Figure 4: Qualitative comparison on Mip-NeRF 360 (top two rows) and Tanks and Temples datasets (bottom two rows) with VGGT-SLAM [ 27 ] and ACE-Zero [ 8 ] . While ACE-Zero performs well on small-scale scenes such as Mip-NeRF 360, it struggles with large, complex environments like Tanks and Temples, where our method maintains high rendering quality across diverse scenes.
Figure 5: Ablations on (a) design components, and sensitivity to (b) hyperparameter and (c) sparse training data.
Appendix figures & tables9 assets
Supplementary material from the paper’s appendix.
Appendix
Figure 6: Despite being initialized from (a) COLMAP generated camera poses, local refinements in (b) often introduce global inconsistencies. In contrast, (c) our hybrid-framework, which integrates pose graph optimization into ACE-Zero, enforces multi-view consistency and suppress incorrect refinements. Furthermore, we demonstrate that (d) ACE-Zero fails without COLMAP initialization, and (e) the low PSNR indicates that VGGT-SLAM lacks accuracy despite its ability to perform loop closure.
Figure 7: We provide several sample image frames from the office loop video that were used for the teaser example.
Figure 8: Qualitative comparison from the office loop video that were used for the teaser example. Although ACE-Zero, even when initialized with COLMAP-generated camera poses, tends to exhibit artifacts and blurriness in synthesized novel views, our proposed method consistently delivers high-fidelity renderings. This improvement reflects a more globally consistent and structurally coherent scene reconstruction.
α1:α2
7-Scenes
Mip-NeRF 360
0.4 : 0.6 (Best)
21.7
24.3
0.5 : 0.5
21.5
24.1
0.3 : 0.7
21.6
24.2
0.6 : 0.4
21.3
23.8
0.7 : 0.3
21.0
23.5
1.0 : 0.0
20.5
22.8
Appendix
Table 9: Ablations on different α ratios on 7-Scenes and Mip-NeRF 360 datasets. We report the pose accuracy via view synthesis as PSNR in dB.
Configuration
7-Scenes
Mip 360
Vanilla PGO (with construction)
18.5
21.0
+ Epipolar metric
19.5
22.0
+ Optical flow metric
20.1
22.3
+ Both metrics
20.8
23.2
Appendix
Table 10: Ablations on different uncertainty-aware configurations. We report the pose accuracy via view synthesis as PSNR in dB.
Figure 9: Ablations on sparse training data for both Mip-NeRF 360 and Tanks and Temples (T2) datasets. We show that our method consistently demonstrates better robustness to limited training data.
Figure 10: Qualitative example of the epipolar-based uncertainty metric (top row: 7-Scenes dataset; bottom row: Tanks and Temples dataset). We overlay reprojected points with their corresponding epipolar lines, color-coded by error (green = low, red = high), to indicate geometric alignment quality under perturbed poses. In the top row, the highest error occurs when no perturbation is applied, suggesting the original camera pose is inaccurate; introducing rotation perturbations reduces the error. In the bottom row, from left to right, we observe a similar trend as in Fig. 2 of the main text: errors remain low without perturbation, increase with pose perturbations, and occasionally decrease when translation perturbations are added. This behavior highlights that epipolar error is sensitive to interactions between pose components and does not always correlate directly with pose accuracy. Consequently, we treat it as a heuristic confidence indicator rather than a strict geometric validator.
Figure 11: Qualitative examples of optical flow predictions are shown (top row: 7-Scenes dataset; bottom row: Tanks and Temples dataset). From left to right: (i) an image from the input pair, (ii) predicted flow visualization where similar colors indicate similar motion directions, and (iii) UV correspondences overlaid on the image pair, illustrating that optical flow provides robust pixel-level matches across views.
Figure 12: Qualitative comparison on Mip-NeRF 360 (top two rows) and Tanks and Temples datasets (bottom two rows) with VGGT-SLAM [ 27 ] and ACE-Zero [ 8 ] . While ACE-Zero performs well on small-scale scenes such as Mip-NeRF 360, it struggles with large, complex environments like Tanks and Temples, where our method maintains high rendering quality across diverse scenes.
Camera pose estimation is a key step in 3D reconstruction and view-synthesis pipelines. We present a deep, global Structure-from-Motion framework based on learned view-graph aggregation. Our method employs a permutation-equivariant, edge-conditioned graph neural network that takes noisy pairwise relative poses as input and outputs globally consistent camera extrinsics. The network is trained without ground-truth supervision, relying solely on a relative-pose consistency objective. This is followed by 3D point triangulation and robust bundle adjustment. Our approach is efficient, scalable to more than a thousand images, and robust to graph density. We evaluate our method on MegaDepth, 1DSfM, Strecha, and BlendedMVS. These experiments demonstrate that our method achieves superior rotation and translation accuracy compared to deep track-centric methods while registering more images across many scenes, and competitive results compared to state-of-the-art classical pipelines, while being much faster.
Structure-from-Motion (SfM) aims to estimate camera poses and reconstruct 3D structures from a collection of unordered images. Compared with incremental SfM, global SfM achieves better scalability by jointly estimating camera poses based on a view graph constructed from pairwise correspondences. However, its performance is highly sensitive to erroneous edges caused by visually ambiguous matches, which may lead to incorrect camera registration and reconstruction artifacts. In this work, we propose a subgraph-guided view graph pruning framework for robust global SfM. Our key idea is to exploit the internal consistency of reliable subgraphs to identify and remove unreliable connections. Specifically, we first partition the view graph into locally consistent subgraphs and perform global SfM within each subgraph to obtain reliable camera poses. We then apply RANSAC-based edge pruning across subgraphs to remove inconsistent edges, and finally perform global SfM on the refined view graph. Extensive experiments on ambiguous, sequential, and unordered image datasets demonstrate that our method improves the robustness of global SfM under challenging conditions. Further evaluation with neural rendering shows that the improved camera estimation leads to higher-quality novel view synthesis results.
Jiamin Xu, Lixing Yao, Weichen Dai +4
Hangzhou Dianzi University, Hangzhou, 310018, China. · State Key Lab of CAD&CG, Zhejiang University, Hangzhou, 310058, China.
Structure from Motion (SfM) is essential for multi-view 3D reconstruction, however, its accuracy heavily relies on the accuracy of image matching. While the recent correspondence matching method, MASt3R, enables robust matching even under challenging conditions, it tends to generate incorrect correspondences for non-overlapping image pairs. Consequently, existing SfM methods using MASt3R, such as MASt3R-SfM, suffer from significant degradation in pose estimation accuracy as they incorporate these unreliable matches directly into optimization. To address this issue, we propose G-MASt3R-SfM, a novel SfM pipeline that enhances robustness through two key modules. First, the Graph-based View Pruning (GVP) module constructs a scene graph from matching confidence and geometrically prunes outlier views. Second, the Multi-Stage Optimization (MSO) module progressively refines camera parameters by expanding the optimization scope from local consistency to the global consistency. Experiments on the ETH3D dataset demonstrate that our method achieves state-of-the-art accuracy in both camera pose estimation and 3D reconstruction, effectively suppressing noise caused by outliers.
Toshiki Watanabe, Shintaro Ito, Natsuki Takama +2
Graduate School of Information Sciences, Tohoku University, Japan