Semantic segmentation of point cloud usually relies on dense annotation that is exhausting and costly, so it attracts wide attention to investigate solutions for the weakly supervised scheme with only sparse points annotated. Existing works start from the given labels and propagate them to highly-related but unlabeled points, with the guidance of data, e.g. intra-point relation. However, it suffers from (i) the inefficient exploitation of data information, and (ii) the strong reliance on labels thus is easily suppressed when given much fewer annotations. Therefore, we propose a novel framework, PointMatch, that stands on both data and label, by applying consistency regularization to sufficiently probe information from data itself and leveraging weak labels as assistance at the same time. By doing so, meaningful information can be learned from both data and label for better representation learning, which also enables the model more robust to the extent of label sparsity. Simple yet effective, the proposed PointMatch achieves the state-of-the-art performance under various weakly-supervised schemes on both ScanNet-v2 and S3DIS datasets, especially on the settings with extremely sparse labels, e.g. surpassing SQN by 21.2% and 17.2% on the 0.01% and 0.1% setting of ScanNet-v2, respectively.

翻译：点云的语义分割通常依赖于密集标注，这一过程既耗时又成本高昂。因此，探索仅基于稀疏点标注的弱监督方案引起了广泛关注。现有方法从给定标签出发，借助数据（如点间关系）的引导，将标签传播至高度相关但未标注的点。然而，这类方法存在两方面不足：(i) 数据信息利用不充分；(ii) 过度依赖标签，因此在标注极为稀疏时易受抑制。为此，我们提出一种新颖框架——PointMatch，该框架兼顾数据与标签：通过应用一致性正则化充分挖掘数据自身的信息，同时利用弱标签作为辅助。如此，模型可从数据和标签中学习到有价值的信息，实现更好的表征学习，并使其对标签稀疏程度具有更强的鲁棒性。简单而高效的PointMatch在ScanNet-v2和S3DIS数据集上的多种弱监督方案中均取得了最先进的性能，尤其在标签极度稀疏的场景下表现突出：例如，在ScanNet-v2的0.01%和0.1%标注设置下，其性能分别超过SQN达21.2%和17.2%。