# Learning with Noisy Labels for Robust Point Cloud Segmentation

arXiv: https://arxiv.org/abs/2107.14230 · HTML: https://mlsanity.com/paper/2107.14230/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 36 references that are in the map.
- First posted on arXiv 29 Jul 2021.

## Builds on (most cited first, 24 of 24)

- [PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation](https://mlsanity.com/paper/1612.00593/) (2016)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [Point Transformer](https://mlsanity.com/paper/2011.00931/) (2020)
- [Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels](https://mlsanity.com/paper/1804.06872/) (2018)
- [Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels](https://mlsanity.com/paper/1805.07836/) (2018)
- [A Closer Look at Memorization in Deep Networks](https://mlsanity.com/paper/1706.05394/) (2017)
- [3D Semantic Segmentation with Submanifold Sparse Convolutional Networks](https://mlsanity.com/paper/1711.10275/) (2017)
- [Symmetric Cross Entropy for Robust Learning with Noisy Labels](https://mlsanity.com/paper/1908.06112/) (2019)
- [Robust Loss Functions under Label Noise for Deep Neural Networks](https://mlsanity.com/paper/1712.09482/) (2017)
- [Early-Learning Regularization Prevents Memorization of Noisy Labels](https://mlsanity.com/paper/2007.00151/) (2020)
- [How does Disagreement Help Generalization against Label Corruption?](https://mlsanity.com/paper/1901.04215/) (2019)
- [Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs](https://mlsanity.com/paper/1711.09869/) (2017)
- [Unsupervised Label Noise Modeling and Loss Correction](https://mlsanity.com/paper/1904.11238/) (2019)
- [Training Deep Neural Networks on Noisy Labels with Bootstrapping](https://mlsanity.com/paper/1412.6596/) (2014)
- [Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise](https://mlsanity.com/paper/1802.05300/) (2018)
- [Octree Generating Networks: Efficient Convolutional Architectures for High-resolution 3D Outputs](https://mlsanity.com/paper/1703.09438/) (2017)
- [Active Bias: Training More Accurate Neural Networks by Emphasizing High Variance Samples](https://mlsanity.com/paper/1704.07433/) (2017)
- [Escape from Cells: Deep Kd-Networks for the Recognition of 3D Point Cloud Models](https://mlsanity.com/paper/1704.01222/) (2017)
- [Curriculum Loss: Robust Learning and Generalization against Label Corruption](https://mlsanity.com/paper/1905.10045/) (2019)
- [JSIS3D: Joint Semantic-Instance Segmentation of 3D Point Clouds with Multi-Task Pointwise Networks and Multi-Value Conditional Random Fields](https://mlsanity.com/paper/1904.00699/) (2019)
- [Meta-PU: An Arbitrary-Scale Upsampling Network for Point Cloud](https://mlsanity.com/paper/2102.04317/) (2021)
- [Learning and Memorizing Representative Prototypes for 3D Point Cloud Semantic and Instance Segmentation](https://mlsanity.com/paper/2001.01349/) (2020)

## Built on by (most cited first, 22 of 22)

- [PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty Awareness](https://mlsanity.com/paper/2312.02158/) (2023)
- [3D Question Answering](https://mlsanity.com/paper/2112.08359/) (2021)
- [An End-to-End Robust Point Cloud Semantic Segmentation Network with Single-Step Conditional Diffusion Models](https://mlsanity.com/paper/2411.16308/) (2024)
- [A Survey of Label-Efficient Deep Learning for 3D Point Clouds](https://mlsanity.com/paper/2305.19812/) (2023)
- [All Points Matter: Entropy-Regularized Distribution Alignment for Weakly-supervised 3D Segmentation](https://mlsanity.com/paper/2305.15832/) (2023)
- [Contour-Aware Equipotential Learning for Semantic Segmentation](https://mlsanity.com/paper/2210.00223/) (2022)
- [Robust Point Cloud Segmentation with Noisy Annotations](https://mlsanity.com/paper/2212.03242/) (2022)
- [Reliability-Adaptive Consistency Regularization for Weakly-Supervised Point Cloud Segmentation](https://mlsanity.com/paper/2303.05164/) (2023)
- [UFO: Uncertainty-aware LiDAR-image Fusion for Off-road Semantic Terrain Map Estimation](https://mlsanity.com/paper/2403.02642/) (2024)
- [LiDAR Remote Sensing Meets Weak Supervision: Concepts, Methods, and Perspectives](https://mlsanity.com/paper/2503.18384/) (2025)
- [Towards Robust Few-shot Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2309.11228/) (2023)
- [AutoInst: Automatic Instance-Based Segmentation of LiDAR 3D Scans](https://mlsanity.com/paper/2403.16318/) (2024)
- [Neighborhood Spatial Aggregation MC Dropout for Efficient Uncertainty-aware Semantic Segmentation in Point Clouds](https://mlsanity.com/paper/2201.07676/) (2021)
- [Gradient-based Point Cloud Denoising with Uniformity](https://mlsanity.com/paper/2207.10279/) (2022)
- [UniDA3D: Unified Domain Adaptive 3D Semantic Segmentation Pipeline](https://mlsanity.com/paper/2212.10390/) (2022)
- [Towards Modality-agnostic Label-efficient Segmentation with Entropy-Regularized Distribution Alignment](https://mlsanity.com/paper/2408.16520/) (2024)
- [UPLME: Uncertainty-Aware Probabilistic Language Modelling for Robust Empathy Regression](https://mlsanity.com/paper/2508.03520/) (2025)
- [Geometric Digital Twinning of Industrial Facilities: Retrieval of Industrial Shapes](https://mlsanity.com/paper/2202.04834/) (2022)
- [Global Hierarchical Attention for 3D Point Cloud Analysis](https://mlsanity.com/paper/2208.03791/) (2022)
- [A Review and A Robust Framework of Data-Efficient 3D Scene Parsing with Traditional/Learned 3D Descriptors](https://mlsanity.com/paper/2312.01262/) (2023)
- [Robust Unsupervised Domain Adaptation for 3D Point Cloud Segmentation Under Source Adversarial Attacks](https://mlsanity.com/paper/2504.01659/) (2025)
- [Integrating SAM Supervision for 3D Weakly Supervised Point Cloud Segmentation](https://mlsanity.com/paper/2508.19909/) (2025)

## Abstract (arXiv)

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 urlhttps://shuquanye.com/PNALwebsite/.