Mutual Positive and Negative Learning for Weakly-supervised Point Cloud Semantic Segmentation

Point cloud semantic segmentation heavily relies on the large-scale point-level annotated dataset, which encourages the weakly-supervised method to prevail gradually. Previous weakly-supervised self-training methods only adopted positive labels, which would be under-performed due to too much noise and lack of supervision. We are the first to present negative labels into the 3D segmentation area, providing extra supervision for hard samples to mitigate the drawbacks induced by noisy labels. Together with the positive labels, we formulate a Mutual Positive-Negative Bi-branch Learning framework to generate positive and negative labels iteratively. Based on that positive branch and negative branch learn complementary knowledge, we build a Mutual Positive-Negative Knowledge Distillation within the bi-branch to further encourage the two branches to learn from each other. Finally, we propose a novel dynamic fusion strategy to fuse predictions from the positive and negative branches, generating more robust predictions. Results on three large-scale datasets show that our method outperforms state-of-the-art weakly-supervised methods by a large margin.