Point cloud part segmentation combined with adaptive sampling and channel attention mechanism

To address the problem that the geometric connection of the channels was ignored and the segmentation accuracy was not enough in existing point cloud part segmentation, a point cloud part segmentation model based on channel attention mechanism was proposed. First, adaptive furthest point sampling was used to determine the sampling points so that the determined local area is better representative, so that the local area could be represented better. In order to suppress the interference of useless information and capture more useful local feature details, three kinds of channel compression operators were used to perform attention pooling in the local area, so as to calculate the attention distribution of channel features. At the sametime, feature interpolation was used to classify each point to achieve the purpose of overall point cloud segmentation. Finally, the ability of the experimental model to capture channel geometric features was verified by comparing the segmentation results of two point cloud datasets with different channel dimensions. The results show that the proposed model can effectively improve the segmentation accuracy of 3D point cloud classes and parts. It can maintain the robustness of the segmentation effect in the case of different number of channels.