面向弱监督点云分割的可靠性自适应一致性正则化
Reliability-Adaptive Consistency Regularization for Weakly-Supervised Point Cloud Segmentation
AI总结：
针对弱监督点云分割中直接应用一致性正则化存在的伪标签含噪、约束不足问题，提出结合置信度与不确定性的RAC-Net，实现自适应一致性训练，在基准数据集上取得更优性能。
摘要：
为缓解采集密集标注3D点的高昂成本，采用标签极少的弱监督点云分割方法极具价值。本文探索将弱监督学习中常用的一致性正则化应用于点云场景，结合多种数据特定增强策略，而这一方向此前尚未得到充分研究。我们发现，将一致性约束直接应用于弱监督点云分割存在两大主要局限：一是传统基于置信度的筛选会产生含噪伪标签，二是丢弃不可靠伪标签会导致一致性约束不足。为此，我们提出一种新型可靠性自适应一致性网络（Reliability-Adaptive Consistency Network，RAC-Net），同时利用预测置信度和模型不确定性衡量伪标签的可靠性，对所有未标记点开展一致性训练，并根据对应伪标签的可靠性为不同点施加差异化的一致性约束。在S3DIS和ScanNet-v2基准数据集上的实验结果表明，我们的模型在弱监督点云分割任务中取得了更优的性能。代码将公开发布于https://github.com/wu-zhonghua/RAC-Net。
英文摘要：
Weakly-supervised point cloud segmentation with extremely limited labels is highly desirable to alleviate the expensive costs of collecting densely annotated 3D points. This paper explores applying the consistency regularization that is commonly used in weakly-supervised learning, for its point cloud counterpart with multiple data-specific augmentations, which has not been well studied. We observe that the straightforward way of applying consistency constraints to weakly-supervised point cloud segmentation has two major limitations: noisy pseudo labels due to the conventional confidence-based selection and insufficient consistency constraints due to discarding unreliable pseudo labels. Therefore, we propose a novel Reliability-Adaptive Consistency Network (RAC-Net) to use both prediction confidence and model uncertainty to measure the reliability of pseudo labels and apply consistency training on all unlabeled points while with different consistency constraints for different points based on the reliability of corresponding pseudo labels. Experimental results on the S3DIS and ScanNet-v2 benchmark datasets show that our model achieves superior performance in weakly-supervised point cloud segmentation. The code will be released publicly at https://github.com/wu-zhonghua/RAC-Net.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
