Multi-view Stereo Reconstruction with Context-guided Cost Volume and Depth Refinemen

In response to the challenges in deep learning-based multi-view stereo(MVS) reconstruction algorithms,which include incomplete image feature extraction,ambiguous cost volume matching,and the accumulation of depth errors leading to poor reconstruction results in textureless and repetitive texture regions,a cascaded MVS network based on context-guided cost volume construction and depth refinement is proposed.First,the feature fusion module based on non-reference attention is used to filter out irrelevant features and address the inconsistency in multi-scale features through feature fusion.Then,the context-guided cost vo-lume module is used to fuse global information to enhance the accuracy and robustness of cost volume matching.Finally,the depth refinement module is employed to learn and reduce depth errors,to improve the accuracy of the low-resolution depth maps.The experimental results show that compared with MVSNet,the integrity error of the network on the DTU dataset is reduced by 24.4%,the accuracy error is reduced by 4.1 %,and the overall error is reduced by 14.3 %.The performance on the Tanks and Temples dataset is also better than most algorithms,showing strong competitiveness.