Don’t Starve the Boundaries: Boundary-Constrained Label Propagation for Weakly Supervised 3D Segmentation

Although fully supervised methods have substantially ad-vanced segmentation in boundary areas of point clouds, effective weaklysupervised approaches remain scarce. This is primarily because limitedsupervision rarely reaches boundary regions, leaving them lacking reli-able supervision. We propose a novel 2D-assisted pseudo-label propaga-tion paradigm that does not rely on the model’s own predictions or anyexternal foundation models, yet is able to generate high-purity pseudo-labels. Compared with SAM-based 2D-3D projection, our pseudo-labelsare purer and more uniformly distributed. Even under 1 pt/obj settingon S3DIS, our initial offline propagation achieves >94.4% accuracy (≈93k pts per scene). We decomposed the pseudo-labels generation pro-cess from the main network, and applied a divide-and-conquer strategy:supervision from interior pseudo-labels serves to stabilize the representa-tion of core class regions, while boundary pseudo-labels are leveraged toenhance boundary robustness. This design reduces the confirmation biasinherent in classic online labeling and alleviates the lack of boundary su-pervision in existing weakly supervised models. Experiments show thatour method outperforms existing state-of-the-art methods. Our codeswill be released at: https://github.com/paul-swu/Bound3D.