DetailPoint: detailed feature learning on point clouds with attention mechanism

Abstract

Point cloud analysis is an important part of 3D geometric processing. It has been widely used in many fields, such as automatic driving and robots. Although great progress has been made in recent years, there are still some unresolved problems. For example, current methods devote employing MLP to extract local features after search k neighbor points, they cannot effectively model the dependency relationship between the anchor point and k neighboring points. In addition, the prevailing models may not exploit the inherent structural similarities present in the global scope. To solve these issues, we propose a feature extraction model named DetailPoint to get detailed local information and long-range global dependency of point clouds. DetailPoint possess three units: the shallow local learning unit, the deep local learning unit and the deep global learning unit. We first use the SLL to extract shallow local features, and then use the DLL to learn deep local features. In these two units, we design a dual-path extraction method to acquire detail local features with dependencies. Finally, the DGL unit is employed to improve the generalization ability of local features and establish global interaction. These three units are connected in series to form our DetailPoint. We evaluated the performance of our model on four datasets, ScanObjectNN and ModelNet40 for shape classification, the ShapeNet dataset for part segmentation, and the S3DIS dataset for sementatic segmentations. The experimental results demonstrate that DetailPoint is capable of expressing point clouds more effectively, resulting in superior performance compared to existing methods.