Prototype Optimal Transport for Box-Supervised 3D Instance Segmentation

3D Instance segmentation (3DIS) on point clouds is a fundamental task in the field of 3D scene understanding. Existing fully-supervised networks have achieved promising results but remain heavily reliant on point-wise annotated data. Using the instance bounding boxes as annotations for weakly-supervised learning is a feasible way to solve the label-efficiency problem. In this paper, we propose POTNet, a novel training paradigm designed to generate point-wise pseudo-labels using only bounding box annotations, which effectively considering both local and global information. We leverage prototype learning method to extract local features from the non-overlapping regions indicated by the bounding boxes as instance prototypes. We employ an optimal transport algorithm to assign points in overlapping regions to their corresponding instances based on the similarity matrix between prototypes and these points. Our approach enables the generation of point-wise pseudo-labels that fully account for local and global correlations. We demonstrate the effectiveness of our method by achieving performance comparable to state-of-the-art approaches on multiple datasets, without requiring any additional supplementary data or retraining processes.