Adaptive aggregation and dynamic self-learning for multi-view stereo network

For the problem of poor accuracy and integrity of multi-view stereo (MVS) reconstruction, we propose an efficient multi-view stereo network, which mainly studies feature extraction module and depth map optimization module. At first, we introduce adaptive aggregation module to use context-aware convolution and multi-scale aggregation to adaptively extract image features, which effectively improves the extraction accuracy of weak texture surface. Then, we introduce dynamic self-learning optimization module (DSL) to solve the problem of depth map over-smoothing. Experiments show that, compared with MVSNet, our network greatly improves the integrity of reconstruction, and does not increase memory and time overhead.