Uncertainty-Guided Contrastive Learning for Weakly Supervised Point Cloud Segmentation

Three-dimensional point cloud data are widely used in many fields, as they can be easily obtained and contain rich semantic information. Recently, weakly supervised segmentation has attracted lots of attention, because it only requires very few labels, thus reducing time-consuming and expensive data annotation efforts for huge amounts of point cloud data. The existing approaches typically adopt softmax scores from the last layer as the confidence for selecting high-confident point predictions. However, such approaches can ignore the potential value of a large number of low-confidence point predictions under traditional metrics. In this work, we propose an uncertainty-guided contrastive learning (UCL) framework for weakly supervised point cloud segmentation. A novel uncertainty metric based on prototype entropy (PE) is presented to estimate the reliability of model predictions. With this metric, we propose a negative contrastive learning module exploiting negative pseudo-labels of predictions with low reliability and an active contrastive learning module enhancing feature learning of segmentation models by predictions with high reliability. We also propose a generic multiscale feature perturbation method to expand a wider perturbation space. Extensive experimental results on indoor and outdoor point cloud datasets demonstrate that the proposed method achieves competitive performance.