Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentation
This addresses the problem of expensive annotation for 3D semantic segmentation, offering an incremental improvement for researchers and practitioners in computer vision.
The paper tackles the high cost of 3D point-level labeling by proposing a semi-supervised method for point cloud semantic segmentation, using guided point contrastive loss and pseudo-label guidance to improve feature representation; experiments on ScanNet V2, S3DIS, and SemanticKITTI datasets show it effectively boosts prediction quality with unlabeled data.
Rapid progress in 3D semantic segmentation is inseparable from the advances of deep network models, which highly rely on large-scale annotated data for training. To address the high cost and challenges of 3D point-level labeling, we present a method for semi-supervised point cloud semantic segmentation to adopt unlabeled point clouds in training to boost the model performance. Inspired by the recent contrastive loss in self-supervised tasks, we propose the guided point contrastive loss to enhance the feature representation and model generalization ability in semi-supervised setting. Semantic predictions on unlabeled point clouds serve as pseudo-label guidance in our loss to avoid negative pairs in the same category. Also, we design the confidence guidance to ensure high-quality feature learning. Besides, a category-balanced sampling strategy is proposed to collect positive and negative samples to mitigate the class imbalance problem. Extensive experiments on three datasets (ScanNet V2, S3DIS, and SemanticKITTI) show the effectiveness of our semi-supervised method to improve the prediction quality with unlabeled data.