  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
