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.
Ying-Cong Chen
Assistant Professor
Ying-Cong Chen is an Assistant Professor at AI Thrust, Information Hub of Hong Kong University of Science and Technology (Guangzhou), and also an affliliated Assistant Professor at Department of Computer Science and Engineering, Hong Kong University of Science and Technology.