Mixed Feature Prediction on Boundary Learning for Point Cloud Semantic Segmentation

Existing point cloud semantic segmentation approaches do not perform well on details, especially for the boundary regions. However, supervised-learning-based methods depend on costly artificial annotations for performance improvement. In this paper, we bridge this gap by designing a self-supervised pretext task applicable to point clouds. Our main innovation lies in the mixed feature prediction strategy during the pretraining stage, which facilitates point cloud feature learning with boundary-aware foundations. Meanwhile, a dynamic feature aggregation module is proposed to regulate the range of receptive field according to the neighboring pattern of each point. In this way, more spatial details are preserved for discriminative high-level representations. Extensive experiments across several point cloud segmentation datasets verify the superiority of our proposed method, including ShapeNet-part, ScanNet v2, and S3DIS. Furthermore, transfer learning on point cloud classification and object detection tasks demonstrates the generalization ability of our method.