[1]王兴涛,代梦丽,于嘉阔,等.基于深度学习的点云去噪方法综述[J].智能系统学报,2026,21(4):849-863.[doi:10.11992/tis.202511005]
WANG Xingtao,DAI Mengli,YU Jiakuo,et al.A review of deep learning-based point cloud denoising methods[J].CAAI Transactions on Intelligent Systems,2026,21(4):849-863.[doi:10.11992/tis.202511005]
点击复制
WANG Xingtao,DAI Mengli,YU Jiakuo,et al.A review of deep learning-based point cloud denoising methods[J].CAAI Transactions on Intelligent Systems,2026,21(4):849-863.[doi:10.11992/tis.202511005]
基于深度学习的点云去噪方法综述
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
21
期数:
2026年第4期
页码:
849-863
栏目:
综述
出版日期:
2026-07-05
- Title:
- A review of deep learning-based point cloud denoising methods
- Keywords:
- 3D data; point clouds; denoising; deep learning; neural networks; feature extraction; supervised learning; graph neural networks
- 分类号:
- TP391
- 摘要:
- 点云作为重要的三维数据载体，广泛应用于自动驾驶、具身智能及虚拟现实等领域。然而，受传感器误差、环境干扰与重建算法缺陷等因素影响，原始点云常含有噪声，导致几何结构失真并降低下游任务性能。针对该问题，大量基于深度学习的点云去噪方法不断涌现。本文系统梳理了近8年该领域的最新进展，构建了基于深度学习的点云去噪五大分类体系，涵盖PointNet主干网络、卷积神经网络（convolutional neural network，CNN)、Transformer网络、非传统网络以及无监督学习策略，并深入剖析了各类算法的核心机制与演进脉络；对比了主流算法在合成与真实数据集上的性能；总结了当前技术面临的泛化能力弱与数据稀缺等挑战，并对全能模型研发、自适应噪声建模等未来发展趋势进行了展望。本文分析结果可为深度学习点云去噪方法的分类理解、性能评估及后续算法改进提供参考。
- Abstract:
- Point clouds, as an important carrier of 3D data, are widely applied in autonomous driving, embodied intelligence, virtual reality, and other fields. However, raw point clouds are often corrupted by sensor errors, environmental interference, and reconstruction algorithm defects, resulting in noise, geometric structure distortion, and degraded performance in downstream tasks. To address these issues, numerous deep learning-based point cloud denoising methods have been proposed. This paper systematically reviews developments in this field over the past eight years. First, a five-category taxonomy for deep learning-based point cloud denoising is established, encompassing PointNet-based backbones, convolutional neural network(CNN), Transformer networks, nontraditional networks, and unsupervised learning strategies. The core mechanisms and evolutionary trajectories of these algorithms are analyzed in depth. Second, the performance of mainstream algorithms is evaluated and compared on both synthetic and real-world datasets. Finally, current challenges, such as limited generalization ability and data scarcity, are summarized, and future research directions, including the development of all-in-one models and adaptive noise modeling, are discussed. The analysis presented in this paper provides a reference for the taxonomy, performance evaluation, and future improvement of deep learning-based point cloud denoising methods.
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备注/Memo
收稿日期:2025-11-4。
基金项目:国家自然科学基金项目（62402138，62502116，624B2049）.
作者简介:王兴涛，副研究员，博士，主要研究方向为三维空间计算、沉浸式媒体、具身智能。主持国家自然科学基金青年项目1项，参与包括国家重点研发计划、国自然重点联合基金、国自然面上项目在内的多项国家级项目。发表学术论文30余篇。E-mail：xtwang@ hit.edu.cn。;代梦丽，硕士研究生，主要研究方向为三维点云去噪。E-mail：24s136078@stu.hit.edu.cn。;范晓鹏，教授，博士生导师，国家级高层次人才，国家重点研发计划项目首席科学家，哈工大计算学部智能接口与人机交互研究中心主任，苏州研究院数字孪生与具身智能团队负责人，曾任香港科技大学研究助理教授、哈工大人工智能专业负责人等。主要研究方向为三维视觉、视频编码、多模态、具身智能。担任中国人工智能学会（CAAI）教育工作委员会副主任、黑龙江省计算机学会学术工作委员会主任、脑机接口技术应用应急管理部重点实验室学术委员会主任等。入选教育部人才计划（2011年）。2013年获IEEE标准杰出贡献奖。2023年获电子学会创新团队奖。发表学术论文200余篇。E-mail：fxp@hit.edu.cn。
通讯作者:范晓鹏. E-mail：fxp@hit.edu.cn
更新日期/Last Update:
1900-01-01