Supervised Contrastive Learning for Indoor Point Cloud Oversegmentation

Point cloud oversegmentation method can obtain a series of superpoints by grouping points that are semantically and geometrically consistent. The generated superpoints can be treated as the basic processing units in various downstream tasks to improve task performance and processing efficiency. However, due to the high semantic and geometric complexity of point cloud scenes, obtaining high-quality superpoints is still challenging. Aiming to generate high-quality indoor superpoints, we propose an end-to-end supervised contrastive learning framework SCL-OverSeg for indoor point cloud oversegmentation. Firstly, to solve the challenge of balancing the importance of geometric similarity and spatial proximity constraint between points and superpoints in indoor scenes, we integrate the geometric similarity and spatial proximity constraint into the supervision signal by generating the superpoint ground truth. To solve the challenge of superpoints crossing objects, we propose to utilize instance labels rather than semantic labels to generate the ideal superpoint ground truth as the object-level supervision signal. Secondly, to construct the distinguishable embedding space facilitating to the assignments of points to superpoints, we propose point-superpoint contrastive learning to compel the network to project each point to be closer to the reasonable superpoint in embedding space. Besides, with the instance labels, to improve the superpoint performance on object boundaries, we propose the object boundary contrastive learning to enhance the feature distinguishability between tough points across the object boundaries. Extensive experiments demonstrate that SCL-OverSeg can effectively improve indoor oversegmentation performance, especially on object boundaries. The relevant codes will be available on https://github.com/sssssyf/SCL-OverSeg.