Bridging geometry and semantics for 3D point cloud instance segmentation

3D instance segmentation is a pivotal yet demanding problem in scene understanding and perception. Existing approaches still fall short of fully exploiting geometric and semantic cues, and the weak coupling between these modalities often leads to over- and under-segmentation artifacts. In this paper, we present a novel framework that couples structural perception with semantic learning to exploit their complementary strengths for 3D instance segmentation. Our framework centers on instance mask prediction and augments it with semantic classification and bounding-box regression as auxiliary objectives. First, a knowledge embedding module initializes instance queries alongside point-level structural features, stabilizing training and accelerating convergence. Second, a two-stage refinement module iteratively updates the instance queries and their associated point features, strengthening the network's ability to align each instance with its constituent points. Finally, a joint mask module fuses geometric and semantic cues, capitalizing on their synergy to improve instance mask accuracy. Extensive evaluation on ScanNetV2, ScanNet200, and S3DIS benchmarks demonstrate that our method achieves state-of-the-art performance for 3D instance segmentation. Furthermore, our underlying architecture generalizes naturally to 3D object detection, where it achieves competitive performance. Source code is available at https://github.com/tianhuixiao12138/BGS.