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We introduce VA-Point-MVSNet, a novel visibility-aware point-based deep framework for multi-view stereo (MVS). Distinct from existing cost volume approaches, our method directly processes the target scene as point clouds. More specifically, our method predicts the depth in a coarse-to-fine manner. We first generate a coarse depth map, convert it into a point cloud and refine the point cloud iteratively by estimating the residual between the depth of the current iteration and that of the ground truth. Our network leverages 3D geometry priors and 2D texture information jointly and effectively by fusing them into a feature-augmented point cloud, and processes the point cloud to estimate the 3D flow for each point. This point-based architecture allows higher accuracy, more computational efficiency and more flexibility than cost-volume-based counterparts. Furthermore, our visibility-aware multi-view feature aggregation allows the network to aggregate multi-view appearance cues while taking into account visibility. Experimental results show that our approach achieves a significant improvement in reconstruction quality compared with state-of-the-art methods on the DTU and the Tanks and Temples dataset. The code of VA-Point-MVSNet proposed in this work will be released at https://github.com/callmeray/PointMVSNet.
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Rui Chen
Shihezi University
Songfang Han
Zhoukou Normal University
Jing Xu
Chinese Academy of Sciences
IEEE Transactions on Pattern Analysis and Machine Intelligence
University of California, San Diego
Tsinghua University
Hong Kong University of Science and Technology
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Chen et al. (Wed,) studied this question.
synapsesocial.com/papers/6a09235115fb758097d25a0c — DOI: https://doi.org/10.1109/tpami.2020.2988729
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