Self-supervised Multi-view Stereo via Inter and Intra Network Pseudo Depth

Recent self-supervised learning-based multi-view stereo (MVS) approaches have shown promising results. However, previous methods primarily utilize view synthesis as the replacement for costly ground-truth depth data to guide network learning, still maintaining a performance gap with recent supervised methods. In this paper, we propose a self-supervised dual network MVS framework with inter and intra network pseudo depth labels for more powerful supervision guidance. Specifically, the inter network pseudo depth labels are estimated by an unsupervised network, filtered by multi-view geometry consistency, updated iteratively by a pseudo depth supervised network, and finally refined by our efficient geometry priority sampling strategy. And we dynamically generate multi-scale intra network pseudo labels inside our cascade unsupervised network during training to provide additional reliable supervision. Experimental results on the DTU and Tanks & Temples datasets demonstrate that our proposed methods achieve state-of-the-art performance among unsupervised methods and even achieve comparable performance and generalization ability with supervised adversaries.