Box-Supervised 3D Instance Segmentation with Level Set Evolution and Cross-View Consistency

Weakly supervised 3D instance segmentation aims to reduce the high cost of point-wise annotations while maintaining competitive accuracy compared with fully supervised methods. Among various weak annotations, box annotation offers an ideal trade-off between labeling efficiency and supervision strength. However, most box-supervised methods rely on a two-stage training pipeline: 1) generating pseudo-labels, 2) training the segmentation model with pseudo-labels, which is iterative and sensitive to pseudo-label quality. To address this issue, we propose an end-to-end framework that directly learns instance masks from box annotations without an explicit pseudo-label generation and iterative relabeling and retraining stage. Specifically, we introduce a boundary-aware refinement module that adaptively learns instance boundaries from boxes through level set evolution. Furthermore, we propose a multi-scale geometric augmentation module to alleviate semantic ambiguity in overlapping regions by applying cross-view consistency constraints on predictions. Finally, we construct a multi-objective optimization framework, which improves both the training stability of level-set-evolution-based boundary refinement and the overall segmentation performance. Extensive experimental results on both indoor and outdoor datasets demonstrate that our method achieves the SOTA performance across multiple benchmark datasets with different backbones and closely approaches fully supervised counterparts.