Self-Constructing Stereo Correspondences for Unsupervised Multi-View Stereo

Existing unsupervised Multi-View Stereo (MVS) methods generally construct supervision on the basis of the photometric consistency loss, which suffers from unreliable supervision and limited scalability. In this paper, a novel unsupervised MVS framework with Self-constructed Stereo Correspondences, termed SSC-MVS, is proposed to provide reliable supervision for the network and improve scalability of unsupervised MVS. Specifically, a pseudo depth-based learning strategy is first presented to supervise the MVS network with a pseudo depth, which is used to characterize the accurate stereo correspondences. Additionally, a consistency-based training mechanism is designed, where the depth consistency between two differently-augmented inputs is constrained to further improve the robustness of the network in real MVS scenes. Experimental results on widely-used MVS datasets demonstrate that the proposed SSC-MVS obtains the state-of-the-art performance among the unsupervised methods and has the potential to outperform the fully-supervised methods. The code is available at https://github.com/jzhu98/ssc-mvs. © 2024 IEEE.