Joint-Learning: A Robust Segmentation Method for 3D Point Clouds Under Label Noise

Abstract

Most of point cloud segmentation methods are based on clean datasets and are easily affected by label noise. We present a novel method called Joint-learning, which is the first attempt to apply a dual-network framework to point cloud segmentation with noisy labels. Two networks are trained simultaneously, and each network selects clean samples to update its peer network. The communication between two networks is able to exchange the knowledge they learned, possessing good robustness and generalization ability. Subsequently, adaptive sample selection is proposed to maximize the learning capacity. When the accuracies of both networks are no longer improving, the selection rate is reduced, which results in cleaner selected samples. To further reduce the impact of noisy labels, for unselected samples, we provide a joint label correction algorithm to rectify their labels via two networks' predictions. We conduct various experiments on S3DIS and ScanNet-v2 datasets under different types and rates of noises. Both quantitative and qualitative results verify the reasonableness and effectiveness of the proposed method. By comparison, our method is substantially superior to the state-of-the-art methods and achieves the best results in all noise settings. The average performance improvement is more than 7.43%, with a maximum of 11.42%.