Papers › Cost Volume Pyramid Network with Multi-strategies Range Searching for Multi-view Stereo
Cost Volume Pyramid Network with Multi-strategies Range Searching for Multi-view Stereo
Shiyu Gao, Zhaoxin Li, Zhaoqi Wang
Multi-view stereo is an important research task in computer vision while still keeping challenging. In recent years, deep learning-based methods have shown superior performance on this task. Cost volume pyramid network-based methods which progressively refine depth map in coarse-to-fine manner, have yielded promising results while consuming less memory. However, these methods fail to take fully consideration of the characteristics of the cost volumes in each stage, leading to adopt similar range search strategies for each cost volume stage. In this work, we present a novel cost volume pyramid based network with different searching strategies for multi-view stereo. By choosing different depth range sampling strategies and applying adaptive unimodal filtering, we are able to obtain more accurate depth estimation in low resolution stages and iteratively upsample depth map to arbitrary resolution. We conducted extensive experiments on both DTU and BlendedMVS datasets, and results show that our method outperforms most state-of-the-art methods.
Code
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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 | MSCVP-MVSNet | Acc | 0.379 | #13 of 24 | Archive leaderboard | report |
| 3D Reconstruction | DTU | MSCVP-MVSNet | Comp | 0.278 | #13 of 24 | Archive leaderboard | report |
| 3D Reconstruction | DTU | MSCVP-MVSNet | Overall | 0.328 | #13 of 24 | 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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