  Point cloud segmentation is fundamental in understanding 3D environments.
However, current 3D point cloud segmentation methods usually perform poorly on
scene boundaries, which degenerates the overall segmentation performance. In
this paper, we focus on the segmentation of scene boundaries. Accordingly, we
first explore metrics to evaluate the segmentation performance on scene
boundaries. To address the unsatisfactory performance on boundaries, we then
propose a novel contrastive boundary learning (CBL) framework for point cloud
segmentation. Specifically, the proposed CBL enhances feature discrimination
between points across boundaries by contrasting their representations with the
assistance of scene contexts at multiple scales. By applying CBL on three
different baseline methods, we experimentally show that CBL consistently
improves different baselines and assists them to achieve compelling performance
on boundaries, as well as the overall performance, eg in mIoU. The experimental
results demonstrate the effectiveness of our method and the importance of
boundaries for 3D point cloud segmentation. Code and model will be made
publicly available at https://github.com/LiyaoTang/contrastBoundary.
