Papers › EPP-MVSNet: Epipolar-Assembling Based Depth Prediction for Multi-View Stereo
EPP-MVSNet: Epipolar-Assembling Based Depth Prediction for Multi-View Stereo
Xinjun Ma, Yue Gong, Qirui Wang, Jingwei Huang, Lei Chen, Fan Yu
In this paper, we proposed EPP-MVSNet, a novel deep learning network for 3D reconstruction from multi-view stereo (MVS). EPP-MVSNet can accurately aggregate features at high resolution to a limited cost volume with an optimal depth range, thus, leads to effective and efficient 3D construction. Distinct from existing works which measure feature cost at discrete positions which affects the 3D reconstruction accuracy, EPP-MVSNet introduces an epipolar assembling-based kernel that operates on adaptive intervals along epipolar lines for making full use of the image resolution. Further, we introduce an entropy-based refining strategy where the cost volume describes the space geometry with the little redundancy. Moreover, we design a light-weighted network with Pseudo-3D convolutions integrated to achieve high accuracy and efficiency. We have conducted extensive experiments on challenging datasets Tanks & Temples(TNT), ETH3D and DTU. As a result, we achieve promising results on all datasets and the highest F-Score on the online TNT intermediate benchmark. Code is available at https://gitee.com/mindspore/mindspore/tree/master/model_zoo/research/cv/eppmvsnet.
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 | EPP-MVSNet | Acc | 0.413 | #18 of 24 | Archive leaderboard | report |
| 3D Reconstruction | DTU | EPP-MVSNet | Comp | 0.296 | #18 of 24 | Archive leaderboard | report |
| 3D Reconstruction | DTU | EPP-MVSNet | Overall | 0.355 | #18 of 24 | Archive leaderboard | report |
| Point Clouds | Tanks and Temples | EPP-MVSNet | Mean F1 (Advanced) | 35.72 | #9 of 21 | Archive leaderboard | report |
| Point Clouds | Tanks and Temples | EPP-MVSNet | Mean F1 (Intermediate) | 61.68 | #9 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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