Research on the Fast-GwcNet 3D Reconstruction Method for Crime Scenes

The 3D reconstruction of the case scene is a key technology for judicial evidence collection and physical evidence analysis, which restores the details of the 3D scene of the x scene through high-precision stereo matching, which significantly surpasses the clue mining ability of 2D images. However, traditional algorithms are prone to matching ambiguity in complex scene environments (such as weakly textured physical evidence, dynamic occlusion, and non-Lambert surfaces), and at the same time, to meet the needs of accuracy and efficiency of 3D reconstruction of case scenes, this paper designs a lightweight stereo matching algorithm based on the GwcNet basic model, in which the S2A attention mechanism is introduced to improve the feature extraction, the MPM module and a new 3D convolution fusion multi-level convolution fusion with cost filtering cost are used. At the same time, the SAFM module is used to fuse the multi-level disparity map of parallax prediction, so that the matching accuracy and running time of the experimental results of the KITTI2012 and KITTI2015 datasets can be improved, and the running time is reduced, which has significant judicial application value.