Papers › GeoMVSNet: Learning Multi-View Stereo With Geometry Perception
GeoMVSNet: Learning Multi-View Stereo With Geometry Perception
Zhe Zhang, Rui Peng, Yuxi Hu, Ronggang Wang
Recent cascade Multi-View Stereo (MVS) methods can efficiently estimate high-resolution depth maps through narrowing hypothesis ranges. However, previous methods ignored the vital geometric information embedded in coarse stages, leading to vulnerable cost matching and sub-optimal reconstruction results. In this paper, we propose a geometry awareness model, termed GeoMVSNet, to explicitly integrate geometric clues implied in coarse stages for delicate depth estimation. In particular, we design a two-branch geometry fusion network to extract geometric priors from coarse estimations to enhance structural feature extraction at finer stages. Besides, we embed the coarse probability volumes, which encode valuable depth distribution attributes, into the lightweight regularization network to further strengthen depth-wise geometry intuition. Meanwhile, we apply the frequency domain filtering to mitigate the negative impact of the high-frequency regions and adopt the curriculum learning strategy to progressively boost the geometry integration of the model. To intensify the full-scene geometry perception of our model, we present the depth distribution similarity loss based on the Gaussian-Mixture Model assumption. Extensive experiments on DTU and Tanks and Temples (T&T) datasets demonstrate that our GeoMVSNet achieves state-of-the-art results and ranks first on the T&T-Advanced set. Code is available at https://github.com/doubleZ0108/GeoMVSNet.
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 | GeoMVSNet | Acc | 0.331 | #6 of 24 | Archive leaderboard | report |
| 3D Reconstruction | DTU | GeoMVSNet | Comp | 0.259 | #6 of 24 | Archive leaderboard | report |
| 3D Reconstruction | DTU | GeoMVSNet | Overall | 0.295 | #6 of 24 | Archive leaderboard | report |
| Point Clouds | Tanks and Temples | GeoMVSNet | Mean F1 (Advanced) | 41.52 | #2 of 21 | Archive leaderboard | report |
| Point Clouds | Tanks and Temples | GeoMVSNet | Mean F1 (Intermediate) | 65.89 | #2 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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