Enhancing Point Cloud Robustness through Cylindrical Coordinate Masking in PointCut

Masked autoencoders (MAE) have demonstrated impressive performance in both natural language processing and computer vision, showcasing their potential across various applications. Building on this success, we have developed a novel self-supervised learning framework called PointCut, designed to address challenges in point cloud data processing, such as uneven grouping and data representation issues. The core innovation of PointCut lies in its cutting plane generation technique. By converting point cloud data into cylindrical coordinates and sorting it based on projection distances, we create optimal cutting planes for each point cloud cluster. This method enhances processing efficiency and model robustness through diverse occlusion schemes. Our masking strategy further strengthens the model by applying varying masking ratios to each data patch, improving recovery capability under different levels of information loss. Through extensive testing and experiments, PointCut has demonstrated promising performance in pre-training and exceptional generalization across various point cloud-related tasks. These results validate our approach’s effectiveness in point cloud self-supervised learning and provide valuable insights for overcoming challenges in point cloud data processing. The code for PointCut will be available at https://github.com/NWUzhouwei/PointCut.