BGPSeg: Boundary-Guided Primitive Instance Segmentation of Point Clouds

Abstract

Point cloud primitive instance segmentation is critical for understanding the geometric shapes of man-made objects. Existing learning-based methods mainly focus on learning high-dimensional feature representations of points and further perform clustering or region growing to obtain corresponding primitive instances. However, these features generally cannot accurately represent the discriminability between instances, especially near the boundaries or in regions with small differences in geometric properties. This limitation often leads to over- or under-segmentation of geometric primitives. On the other hand, the boundaries of different primitives are the direct features that distinguish them and thus utilizing boundary information to guide feature learning and clustering is crucial for this task. In this paper, we propose a novel framework BGPSeg for point cloud primitive instance segmentation that utilizes boundary-guided feature extraction and clustering. Specifically, we first introduce a boundary-guided feature extractor with the additional input of a boundary probability map, which utilizes boundary-guided sampling and a boundary transformer to enhance feature discrimination among points crossing geometric boundaries. Furthermore, we propose a boundary-guided primitive clustering module, which combines boundary clues and geometric feature discrimination for clustering to further improve the segmentation performance. Finally, we demonstrate the effectiveness of our BGPSeg with a series of comparison and ablation experiments while achieving the state-of-the-art primitive instance segmentation. Our code is available at https://github.com/fz-20/BGPSeg.