Multi-Stage Pseudo-Label Guided Weakly Supervised Point Cloud Semantic Segmentation for Building Scenes

Point cloud semantic segmentation is a fundamental task for 3D scene understanding. However, acquiring dense point-wise annotations is labor-intensive and expensive. To alleviate the annotation burden, weakly supervised methods using extremely sparse labels (e.g., 0.1%) have attracted increasing attention. While existing methods have demonstrated promising performance, they struggle to generate sufficiently reliable pseudo-labels from unlabeled points, which limits further improvement in segmentation performance under extremely sparse supervision. To address this challenge, we propose a Multi-stage Pseudo-label Guided (MPG) training framework, which mines high-quality pseudo-labels through a two-stage training strategy. In the first stage, we introduce a Prototype-guided Pseudo-label Filter (PPF) to capture latent semantic information by comparing unlabeled features with class prototypes and removes unreliable pseudo-labels using a predefined threshold. In the second stage, we employ a teacher-student architecture and introduce a Pseudo-Label Fusion (PLF) strategy. PLF applies consistency and confidence filtering on two data-augmentation branches of the frozen teacher model to produce refined pseudo-labels, which are then combined with PPF outputs to train the student network. This design allows the student model to inherit distilled knowledge while exploring additional semantic information. Extensive experiments on indoor and outdoor building scenes demonstrate the effectiveness of MPG. Under extremely sparse supervision, MPG achieves 69.5% mIoU on S3DIS and 59.5% on XIAMEN-Buildings, achieving state-of-the-art performance among weakly supervised methods and performance comparable to some fully supervised methods.