Point HorNet: Higher-Order Spatial Interaction Network for Point Clouds

The progression of 3D scanning technology has amplified the importance of point cloud data in computer vision, robotics navigation, and virtual reality. Point clouds, consisting of discrete points in space, harbor rich geometric and structural information. The rise of deep learning has ushered in innovative methods for point cloud processing, improving the efficiency and precision of tasks such as feature extraction, classification, segmentation, and reconstruction. The introduction of HorNet and Recursive Gated Convolution has facilitated spatial interactions of any order, addressing issues of feature loss inherent in low-order interactions. This has resulted in significant advancements in image analysis tasks, including image classification, object detection, and semantic segmentation. Drawing inspiration from this success, we have investigated the application of HorNet and Recursive Gated Convolution to point clouds, constructing a network specifically designed for semantic segmentation tasks. Point HorNet has achieved commendable results in semantic segmentation, attaining a mIoU of 68.1% in Area 5 and 73.6% in a six-fold cross-validation on the S3DIS dataset.