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arXiv · 2203.03949

RC-MVSNet: Unsupervised Multi-View Stereo with Neural Rendering

Di Chang·Aljaž Božič·Tong Zhang·Qingsong Yan·Yingcong Chen·Sabine Süsstrunk·Matthias Nießner
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Abstract

Finding accurate correspondences among different views is the Achilles' heel of unsupervised Multi-View Stereo (MVS). Existing methods are built upon the assumption that corresponding pixels share similar photometric features. However, multi-view images in real scenarios observe non-Lambertian surfaces and experience occlusions. In this work, we propose a novel approach with neural rendering (RC-MVSNet) to solve such ambiguity issues of correspondences among views. Specifically, we impose a depth rendering consistency loss to constrain the geometry features close to the object surface to alleviate occlusions. Concurrently, we introduce a reference view synthesis loss to generate consistent supervision, even for non-Lambertian surfaces. Extensive experiments on DTU and Tanks\&Temples benchmarks demonstrate that our RC-MVSNet approach achieves state-of-the-art performance over unsupervised MVS frameworks and competitive performance to many supervised methods.The code is released at https://github.com/Boese0601/RC-MVSNet

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Di Chang, Aljaž Božič, Tong Zhang, Qingsong Yan, Yingcong Chen, Sabine Süsstrunk, Matthias Nießner. 2022-03-08. RC-MVSNet: Unsupervised Multi-View Stereo with Neural Rendering. https://arxiv.org/abs/2203.03949

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