Point cloud segmentation is a fundamental task in 3D. Despite recent progress on point cloud segmentation with the power of deep networks, current deep learning methods based on the clean label assumptions may fail with noisy labels. Yet, object class labels are often mislabeled in real-world point cloud datasets. In this work, we take the lead in solving this issue by proposing a novel Point Noise-Adaptive Learning (PNAL) framework. Compared to existing noise-robust methods on image tasks, our PNAL is noise-rate blind, to cope with the spatially variant noise rate problem specific to point clouds. Specifically, we propose a novel point-wise confidence selection to obtain reliable labels based on the historical predictions of each point. A novel cluster-wise label correction is proposed with a voting strategy to generate the best possible label taking the neighbor point correlations into consideration. We conduct extensive experiments to demonstrate the effectiveness of PNAL on both synthetic and real-world noisy datasets. In particular, even with $60\%$ symmetric noisy labels, our proposed method produces much better results than its baseline counterpart without PNAL and is comparable to the ideal upper bound trained on a completely clean dataset. Moreover, we fully re-labeled the validation set of a popular but noisy real-world scene dataset ScanNetV2 to make it clean, for rigorous experiment and future research. Our code and data are available at \url{https://shuquanye.com/PNAL_website/}.

翻译：3D 中, 云层分割是一项基本任务 。 尽管最近以深网络的力量在点云分割方面取得进展, 目前基于清洁标签假设的深层次学习方法可能因杂音标签而失败。 然而, 对象类标签往往在现实世界点云数据集中被误贴。 在这项工作中, 我们率先解决这一问题, 提出一个新的“ 噪音- 适应性学习( PNAAL) 框架 ” 。 与现有关于图像任务的噪音- robust 方法相比, 我们的 PNAL 是噪音率盲点, 以应对空间变异噪声率问题, 具体针对点云。 具体地说, 我们提议了一个新的点向点选择点点选择, 以获得基于每个点的历史预测的可靠标签。 提出了新的组类标签更正, 以投票策略生成最佳可能的标签, 将相邻点相关性考虑在内。 我们进行了广泛的实验, 以展示 PNAL 在合成和真实世界的噪音数据集上的有效性。 特别是, 我们提出的方法比 http://qumnational blogy mold blogy cal claud cal claud cal 还要好 。