Hybrid Attention Cascade Multi-View Stereo Network
Weiqiang Liu, Rongshang Chen, Huarong Xu, Lifen Weng · 2022
The multi-view stereo reconstruction method based on deep learning is usually affected by the weak-textured area or occlusion in the real scene. Therefore we propose a multi-view stereo reconstruction network method with a hybrid attention mechanism. A hybrid attention module is added to the feature extractor to improve the performance in weak-textured regions. In order to reduce occlusion effects a module is used to adjust the view weights. We find adding depth-adaptive partitioning will improve the performance of our method. Our method is trained and tested on the DTU and Tanks and Temples datasets, the results show that our method has good results in terms of reconstruction accuracy and completeness.