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
https://shuquanye.com/PNAL_website/.

本文提出了一种基于点噪声自适应学习（PNAL）框架的点云分割算法，包括一种基于历史预测的可靠标签点选择和一种投票策略的集群微调方法，有效解决了一些实际场景中存在的标签噪声问题。