When 3D Bounding-Box Meets SAM: Point Cloud Instance Segmentation With Weak-and-Noisy Supervision

Qingtao Yu, Heming Du, Chen Liu, Xin Yu; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 3719-3728

Learning from bounding-boxes annotations has shown great potential in weakly-supervised 3D point cloud in- stance segmentation. However, we observed that existing methods would suffer severe performance degradation with perturbed bounding box annotations. To tackle this is- sue, we propose a complementary image prompt-induced weakly-supervised point cloud instance segmentation (CIP- WPIS) method. CIP-WPIS leverages pretrained knowledge embedded in the 2D foundation model SAM and 3D geo- metric prior to achieve accurate point-wise instance labels from the bounding box annotations. Specifically, CIP-WPIS first selects image views in which 3D candidate points of an instance are fully visible. Then, we generate complemen- tary background and foreground prompts from projections to obtain SAM 2D instance mask predictions. According to these, we assign the confidence values to points indicating the likelihood of points belonging to the instance. Furthermore, we utilize 3D geometric homogeneity provided by superpoints to decide the final instance label assignments. In this fashion, we achieve high-quality 3D point-wise in- stance labels. Extensive experiments on both Scannet-v2 and S3DIS benchmarks proves that our method not only achieves state-of-the-art performance for bounding-boxes supervised point cloud instance segmentation, but also exhibits robustness against noisy 3D bounding-box annotations.