Papers › Visibility-aware Multi-view Stereo Network
Visibility-aware Multi-view Stereo Network
Jingyang Zhang, Yao Yao, Shiwei Li, Zixin Luo, Tian Fang
Learning-based multi-view stereo (MVS) methods have demonstrated promising results. However, very few existing networks explicitly take the pixel-wise visibility into consideration, resulting in erroneous cost aggregation from occluded pixels. In this paper, we explicitly infer and integrate the pixel-wise occlusion information in the MVS network via the matching uncertainty estimation. The pair-wise uncertainty map is jointly inferred with the pair-wise depth map, which is further used as weighting guidance during the multi-view cost volume fusion. As such, the adverse influence of occluded pixels is suppressed in the cost fusion. The proposed framework Vis-MVSNet significantly improves depth accuracies in the scenes with severe occlusion. Extensive experiments are performed on DTU, BlendedMVS, and Tanks and Temples datasets to justify the effectiveness of the proposed framework.
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Code
Syntology Ran 3 of 14 code samples harvested from 1 repository linked to this paper; 11 have no recorded run. Of those that ran: 1 ran · honoured contract; 2 ran · our draft was wrong.
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Code Syntology ran Syntology
14 samples harvested; 3 ran; 1 honoured the contract we drafted; 11 have no recorded run. Read from Syntology's graph 2026-09-24; that is when this build read the record, not when the samples ran.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| 3D Reconstruction | DTU | Vis-MVSNet | Acc | 0.369 | #20 of 24 | Archive leaderboard | report |
| 3D Reconstruction | DTU | Vis-MVSNet | Comp | 0.361 | #20 of 24 | Archive leaderboard | report |
| 3D Reconstruction | DTU | Vis-MVSNet | Overall | 0.365 | #20 of 24 | Archive leaderboard | report |
| Point Clouds | DTU | Vis-MVSNet | Overall | 0.365 | #1 of 1 | Archive leaderboard | report |
| Point Clouds | Tanks and Temples | Vis-MVSNet | Mean F1 (Intermediate) | 60.03 | #18 of 21 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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