Journal
2022 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR)
Volume -, Issue -, Pages 8596-8605Publisher
IEEE COMPUTER SOC
DOI: 10.1109/CVPR52688.2022.00841
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IterMVS is a new data-driven method for high-resolution multi-view stereo. It encodes and refines the pixel-wise probability distributions of depth using a GRU-based estimator, and combines traditional classification and regression for depth map extraction. The method has been validated for efficiency and effectiveness, and compared with state-of-the-art methods.
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.
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