Guided Point Contrastive Learning for Semi-supervised Point Cloud Semantic Segmentation

Abstract

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.

arXiv commentICCV 2021

References in corpus (5)

• PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
• Learning deep representations by mutual information estimation and maximization
• Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
• Semi-Supervised Learning with Ladder Networks
• ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes