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:点云的语义分解通常依赖于耗尽和昂贵的密语说明,因此它吸引了广泛的注意力,调查薄弱监管机制的解决方案,只有少点附加说明。现有的作品从给定标签开始,传播到高度相关但没有标签的点,并受数据指导,例如点内部关系。然而,它受到以下因素的影响:(一) 数据信息利用效率低下,以及(二) 大量依赖标签,因此,如果给出的注释少得多,很容易被抑制。因此,我们提议了一个新颖的框架,即点Match,它既存在于数据和标签上,同时又采用一致性规范,充分探测数据本身的信息,同时利用薄弱标签作为协助。通过这样做,可以从数据和标签中学习有意义的信息,以更好地进行代表性学习,这也使模型更加稳健。简单而有效,拟议的点Match在ScranNet-v2和S3DIS数据集集中都实现了最先进的状态,特别是在SBARNet2和S21%的设置上,在SBIS2和21%的SBAR2和21%的设置上,在SBAR2和21%的SBIS-Q上分别设置。