Weakly Supervised Large-Scale Point Cloud Semantic Segmentation Based on Dual Consistency Constraints and Uncertainty-Aware Fusion

Weakly supervised point cloud semantic segmentation has attracted more and more attention due to its capability to circumvent the time-consuming and expensive full labeling annotation process, which is required by fully supervised learning. However, existing weakly supervised methods typically rely solely on the sparsely labeled points for network training and cannot fully exploit the vast amount of unlabeled data. Moreover, recent weakly supervised segmentation methods are not efficient in enhancing network generalization and extracting discriminative features. To resolve these issues, we propose a novel framework (DCUF-Net) for weakly supervised point cloud semantic segmentation based on dual consistency constraints and uncertainty-aware fusion. Specifically, we first design dual perturbations in the data and feature domains to improve the network generalization and employ prediction consistency constraints between the two perturbed branches and the original branch. Additionally, we propose to jointly represent the prediction reliability of unlabeled points according to distribution confidence and uncertainty, which enables us to introduce an uncertainty-aware loss for all unlabeled points, providing additional supervised signals to optimize the network. To further extract more discriminative features, we propose an efficient regional context adaptive aggregation (RCAA) module to enhance information interaction between points. Extensive experiments on three large-scale datasets, including S3DIS, Toronto3D, and STPLS3D, demonstrate the superior performance of our method against some state-of-the-art methods.