IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo

Fangjinhua Wang, Silvano Galliani, Christoph Vogel, Marc Pollefeys; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022, pp. 8606-8615

Abstract


We present IterMVS, a new data-driven method for high-resolution multi-view stereo. We propose a novel GRU-based estimator that encodes pixel-wise probability distributions of depth in its hidden state. Ingesting multi-scale matching information, our model refines these distributions over multiple iterations and infers depth and confidence. To extract the depth maps, we combine traditional classification and regression in a novel manner. We verify the efficiency and effectiveness of our method on DTU, Tanks&Temples and ETH3D. While being the most efficient method in both memory and run-time, our model achieves competitive performance on DTU and better generalization ability on Tanks&Temples as well as ETH3D than most state-of-the-art methods. Code is available at https://github.com/FangjinhuaWang/IterMVS.

Related Material


[pdf] [supp] [arXiv]
[bibtex]
@InProceedings{Wang_2022_CVPR, author = {Wang, Fangjinhua and Galliani, Silvano and Vogel, Christoph and Pollefeys, Marc}, title = {IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo}, booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)}, month = {June}, year = {2022}, pages = {8606-8615} }