부산대학교 도서관

Point cloud analysis is challenging due to its data structure. To capture the 3-D geometries, prior works mainly rely on exploring local geometric extractors. However, the human visual system suggested that both global and local features should be considered. In this article, we introduce a novel framework for point cloud classification, called ChainFrame, which takes the pair-wise global-local correlations into consideration within the intermediate scales hierarchically. The ChainFrame captures the global features that characterize the entire outline of the object. Simultaneously, the ChainFrame organizes the local features that incorporate the point itself and its neighboring region. With such a framework, our practical implementations (ChainMLP and ChainGraph) perform on par or even better than other methods. Evaluations on two popular datasets show the effectiveness and efficiency of our ChainFrame. ChainMLP and ChainGraph achieve $ 87.2{\%}$ and $87.6{\%}$ overall point-wise accuracy scores, respectively, on the real-world ScanObjectNN benchmark. Besides, ChainMLP delivers comparable performance on ModelNet40 with only 0.47 M parameters and 0.33 G floating point operations (FLOPs), which are much smaller than the prior methods.