The pose graph is a core component of Structure-from-Motion (SfM), where images act as nodes and edges encode relative poses. Since geometric verification is expensive, SfM pipelines restrict the pose graph to a sparse set of candidate edges, making initialization critical. Existing methods rely on image retrieval to connect each image to its k nearest neighbors, treating pairs independently and ignoring global consistency. We address this limitation through the concept of edge prioritization, ranking candidate edges by their utility for SfM. Our approach has three components: (1) a GNN trained with SfM-derived supervision to predict globally consistent edge reliability; (2) multi-minimal-spanning-tree-based pose graph construction guided by these ranks; and (3) connectivity-aware score modulation that reinforces weak regions and reduces graph diameter. This globally informed initialization yields more reliable and compact pose graphs, improving reconstruction accuracy in sparse and high-speed settings and outperforming SOTA retrieval methods on ambiguous scenes. The ode and trained models are available at https://github.com/weitong8591/global_edge_prior.
Figures & tables
Figure 1: Given a set of input image pairs ( left ), our method ranks all candidate edges by global matchability ( middle ) and constructs a compact, well-connected pose graph via multi-MST selection ( right ). The resulting initialization enables accurate and stable 3D reconstruction, even under sparse or ambiguous settings.
Figure 2: Overall pipeline. Input images are first encoded using a fine-tuned image encoder (DINOv2 backbone with SALAD aggregation). A complete graph is then constructed over image embeddings and processed by our GNN–MLP model to predict global edge ranks. During training, these predictions are supervised using geometry-derived ranking signals via a differentiable ranking loss. At inference, the predicted ranks guide the construction of multiple minimum spanning trees, whose union forms the initial pose graph. Incremental SfM is finally applied on this graph to recover the sparse 3D reconstruction.
Figure 3: COLMAP reconstruction [ 42 ] performance using pose graphs constructed from multiple MSTs guided by baseline embedding similarities or our learned global edge ranks. Top row: Relative pose accuracy on IMC23-PhotoTourism [ 21 ] ([email protected] ∘ , left; AUC@5 ∘ , middle) and on MegaDepth [ 30 ] (AUC@5 ∘ , right) as the number of MSTs k increases. Bottom row: Percentage of registered cameras on PhotoTourism (left), AUC@5 ∘ versus COLMAP runtime on PhotoTourism (middle), and percentage of accurately reconstructed cameras on VisymScenes [ 50 ] (right). Across all benchmarks, our method consistently achieves the highest accuracy, particularly in the sparse regime ( k=1 – 2 ), while maintaining competitive or superior reconstruction efficiency.
Method
Config
AUC@5 ∘↑
Time (min) ↓
k MSTs
→
2
3
5
5
MegaLoc
w/o modul.
60.2
64.8
70.5
1 7.3
w/ o modul.
64.1
65.3
74.7
14.1
DG++
64.2
64.9
68.4
1 8.6
Ours
w/o modul.
61.9
65.8
71.7
1 5.7
w/ o modul.
66.0
71.5
75.6
13.3
Table 1: Ablation of connectivity-aware score modulation on VisymScenes [ 50 ] . We report AUC@5 ∘ for pose graphs built from MegaLoc similarities and from our predicted global edge ranks, with and without modulation, as well as with DoppelGanger++ filtering. Modulation consistently improves accuracy for both methods, especially in the sparse regime ( k=2 – 3 ). COLMAP mapping time at k=5 is shown in minutes.
Figure 4: Edge selection strategies for pose graph initialization. We report COLMAP [ 42 ] relative pose AUC@5 ∘ on IMC23-PhotoTourism [ 21 ] ( left ) and MegaDepth [ 30 ] ( middle ) when using either k NN or the proposed k minimal spanning tree approach (MSTs) for graph construction. Oracle ∗ curves correspond to ground-truth rankings from SfM-derived geometry. The percentage of successfully registered cameras on PhotoTourism is shown on the right. MST-based selection yields substantially stronger connectivity and higher accuracy than k NN, and closely follows the oracle behavior across all k s.
Variant
AUC@5 ∘↑
k MSTs →
1
2
3
5
Ours w/ SALAD backbone
61.2
71.0
72.4
73.4
Ours w/o GNN
55.4
70.4
72.3
72.3
Ours
64.2
71.7
72.6
73.5
Oracle-RANSAC inliers
65.7
72.4
73.0
74.1
Oracle-3D inliers
65.4
72.1
73.5
74.3
Table 2: Ablation study on PhotoTourism evaluating different components of our method. We evaluate by using the weaker SALAD backbone and the removal of the GNN. The last three rows report oracle rankings derived directly from geometric signals (RANSAC inliers, 3D point overlap, or their combination), illustrating the relative strength of each supervision source. AUC@5 ∘ scores reported.
Modul.
Top-5
Normalized
AUC@5 ∘↑
candidates
dist.
k→ 2
3
5
×
×
×
61.9
65.8
71.7
×
✓
×
61.4
66.9
64.8
✓
×
×
63.5
64.7
68.9
✓
✓
×
63.7
65.5
68.3
×
✓
✓
60.3
67.3
67.1
Table 3: Ablation of the proposed connectivity-aware score modulation on VisymScenes. We evaluate three components: (i) whether modulation is applied, (ii) whether only the top-5 candidate edges per image are updated after each MST, and (iii) whether graph distances are normalized before modulation. Reported metrics are AUC@5 ∘ for pose graph initialization using k=2,3,5 MSTs. The best configuration combines modulation with normalized distances, yielding the strongest performance across all settings.
Global Structure-from-Motion (SfM) is an efficient paradigm for recovering camera poses and sparse 3D structure from unordered images. However, its reliance on scale-ambiguous epipolar geometry makes global positioning sensitive to noisy baseline estimates and weak view-graph constraints, while false edges from visually ambiguous pairs can further degrade reconstruction. We propose DGSfM, a depth-aware global SfM pipeline that uses monocular depth maps as a scalable prior while preserving explicit multi-view optimization. For each image pair, we use a depth-aware relative pose solver to convert scale-ambiguous epipolar constraints into scale-aware relative pose constraints. We further improve robustness through view-graph filtering and depth-consistency-based correspondence pruning, which suppress false edges and matches that remain plausible under epipolar geometry alone. Finally, global scale averaging and depth-guided pose-point initialization align monocular depth maps into a common reconstruction scale and provide stable initialization for global positioning and bundle adjustment. Experiments on ETH3D and IMC2021 show that DGSfM consistently improves over strong global SfM baselines across sparse and dense matching front-ends, achieving substantial gains in pose accuracy. Code and project page: https://dgsfm.github.io/.
Sithu Aung, Viktor Kocur, Yaqing Ding +2
VRG, FEE, Czech Technical University in Prague · FMPH, Comenius University in Bratislava · Southeast University +1
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.
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.