Self-supervised Edge Structure Learning for Multi-view Stereo and Parallel Optimization

Recent studies have witnessed that many self-supervised methods obtain clear progress on the multi-view stereo (MVS). However, existing methods ignore the edge structure information of the reconstructed target, which includes the outer silhouette and the edge information of the internal structure. This may lead to less satisfactory edges and completeness of the reconstruction result. To solve this problem, we propose an extractor for extracting edge structure maps, and we innovatively design an edge structure Loss to constrain the network to pay more attention to edge structure features of the reference view to improve the texture details of the reconstruction results. Specially, we utilize the idea of constructing cost volume in multi-view stereo and warp the edge structure map of the source view to the reference view to provide reliable self-supervision. In addition, we design a masking mechanism that combines local and global properties, which ensures robustness and improves the reconstruction completeness of the model for complex samples. Furthermore, we adopt an effective parallel acceleration approach to improve the training speed and reconstruction efficiency. Extensive experiments on the DTU and Tanks &Temples benchmarks demonstrate that our method improves both accuracy and completeness in comparison with other unsupervised work. In addition, our parallel method improves efficiency while ensuring accuracy. The code will be published.