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

Abstract

Geometrical structures and the internal local region relationship, such as symmetry, regular array, junction, etc., are essential for understanding a 3D shape. This paper proposes a point cloud feature extraction network named PointSCNet, to capture the geometrical structure information and local region correlation information of a point cloud. The PointSCNet consists of three main modules: the space-filling curve-guided sampling module, the information fusion module, and the channel-spatial attention module. The space-filling curve-guided sampling module uses Z-order curve coding to sample points that contain geometrical correlation. The information fusion module uses a correlation tensor and a set of skip connections to fuse the structure and correlation information. The channel-spatial attention module enhances the representation of key points and crucial feature channels to refine the network. The proposed PointSCNet is evaluated on shape classification and part segmentation tasks. The experimental results demonstrate that the PointSCNet outperforms or is on par with state-of-the-art methods by learning the structure and correlation of point clouds effectively.

A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation

“…PointSCNet [177] captures the geometrical structure and local region correlation of a point cloud using three key components: a space-filling curve-guided sampling module, an information fusion module, and a channel-spatial attention module. The sampling module selects points with geometrical correlation using Z-order curve coding.…”

Revisiting the Effectiveness of 3D Object Recognition Benchmarks

“…are 3D 'Voxel', 2D 'Image', or un-ordered array of 'XYZ' 3D point cloud. [60], [61], [62], [63] use normals or mesh information. [64] is pre-trained on ImageNet.…”