An Efficient and Dynamical Way for Local Feature Extraction on Point Cloud

This paper presents a flexible module that utilizes the 3D position attention mechanism to extract contextual features from local regions of point cloud. The key point is to create an effective representation of local features. Due to the irregularity of point cloud, previous algorithms for point cloud processing have not fully explored how to enhance the extraction of local features. Inspired by the position attention mechanism in the 2D image segmentation algorithm, we propose a Point Attention Graph (PAG) module that can be used to improve the fusion of local features and make it better and faster. The PAG module uses the point attention mechanism to adaptively calculate the interaction between all nodes of the local graph. It can efficiently definite the relations of local points to enhance the performance of feature extraction both in accuracy and time efficiency, especially compared with some related models like PointWEB. Experiments show that our method can be effectively applied to semantic segmentation datasets.

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