@inproceedings{wang2022itermvs,
author = {Wang, Fangjinhua and Galliani, Silvano and Vogel, Christoph and Pollefeys, Marc},
title = {IterMVS: Iterative Probability Estimation for Efficient Multi-View Stereo},
booktitle = {CVPR 2022},
year = {2022},
month = {May},
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.},
url = {http://approjects.co.za/?big=en-us/research/publication/itermvs-iterative-probability-estimation-for-efficient-multi-view-stereo/},
}