Decouple the object: Component-level semantic recognizer for point clouds classification

Rui Hu, Bing Yang, Hailiang Ye, Feilong Cao, Chenglin Wen, Qinghua Zhang

Abstract: 3D point clouds classification is fundamental but always challenging in point clouds analysis, of which the key is efficiently extracting their distinguishing features. At present, several studies based on deep learning perform well in 3D point clouds classification task but are still weak in the capability of resemblance categories recognition. This paper presents a component semantic recognizer to decrease misclassification between the resemblance categories, which can better identify the different structural blocks of point clouds. Specifically, a point energy measurement unit is first built to pre-divide point clouds into non-overlapping blocks. Then, considering the fact that the closer the distance between 3D points, the stronger the feature interaction should be, an affinity bias is proposed for preserving the spatial properties when points features interact within the structural block. Besides validating the method on both synthetic and real-world point clouds benchmarks, the performance of the components is also tested and visualized. Comparing with other state-of-the-art methods, the proposed approach shows superiority.