The present invention relates to an object classification and object segmentation method based on 3D
point cloud information, which belongs to the field of 3D
point cloud processing, and particularly to the field of difficult
processing of disordered point clouds and difficult extraction of
point cloud features. In order to extract more effective information in extracting point cloud features, the present invention proposes and uses a new joint global feature and local
feature structure. When extracting high-dimensional features, a new extraction mechanism is proposed to replace the traditional symmetric function. A large number of experiments have shown that the neural network we designed can solve a variety of point cloud tasks, including but not limited to object classification, object segmentation,
scene segmentation, etc. The beneficial effects of the present invention: the designed point cloud recognition and segmentation
algorithm mainly designs a new Mix‑Net dedicated
neural network design, and innovates from multiple modules such as downsampling
algorithm, feature value extraction and
fusion mechanism, which significantly improves the accuracy of point cloud recognition and segmentation.