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Geometrical structures and the internal local region relationship, such assymmetry, regular array, junction, etc., are essential for understanding a 3Dshape. This paper proposes a point cloud feature extraction network namedPointSCNet, to capture the geometrical structure information and local regioncorrelation information of a point cloud. The PointSCNet consists of three mainmodules: the space-filling curve-guided sampling module, the information fusionmodule, and the channel-spatial attention module. The space-fillingcurve-guided sampling module uses Z-order curve coding to sample points thatcontain geometrical correlation. The information fusion module uses acorrelation tensor and a set of skip connections to fuse the structure andcorrelation information. The channel-spatial attention module enhances therepresentation of key points and crucial feature channels to refine thenetwork. The proposed PointSCNet is evaluated on shape classification and partsegmentation tasks. The experimental results demonstrate that the PointSCNetoutperforms or is on par with state-of-the-art methods by learning thestructure and correlation of point clouds effectively.

PointSCNet: Point Cloud Structure and Correlation Learning Based on Space Filling Curve-Guided Sampling