RC-MVSNet: Unsupervised Multi-View Stereo with Neural Rendering (ECCV'22)

Project: boese0601.gith...
Code: github.com/Boe...
Paper: arxiv.org/abs/...
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 approach achieves state-of-the-art performance over unsupervised MVS frameworks and competitive performance to many supervised methods. Our code will be released at RC-MVSNet.