1 Introduction
Deep neural networks (DNNs) have witnessed considerable success in 3D point cloud segmentation in recent years. Owing to their powerful learning ability, once high-quality annotations are provided, DNNs-based point segmentation methods can achieve remarkable performance. However, such strong learning capacity is a double-edged sword, as it can also over-fit label noise and degrade performance if annotations are inaccurate.