{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,12]],"date-time":"2026-05-12T08:34:05Z","timestamp":1778574845850,"version":"3.51.4"},"reference-count":50,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,22]],"date-time":"2024-08-22T00:00:00Z","timestamp":1724284800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62072076"],"award-info":[{"award-number":["62072076"]}]},{"name":"National Natural Science Foundation of China","award":["2022ZDZX0005"],"award-info":[{"award-number":["2022ZDZX0005"]}]},{"name":"Sichuan Provincial Research Plan Project","award":["62072076"],"award-info":[{"award-number":["62072076"]}]},{"name":"Sichuan Provincial Research Plan Project","award":["2022ZDZX0005"],"award-info":[{"award-number":["2022ZDZX0005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>LiDAR sensors have been shown to generate data with various common corruptions, which seriously affect their applications in 3D vision tasks, particularly object detection. At the same time, it has been demonstrated that traditional defense strategies, including adversarial training, are prone to suffering from gradient confusion during training. Moreover, they can only improve their robustness against specific types of data corruption. In this work, we propose LiDARPure, which leverages the powerful generation ability of diffusion models to purify corruption in the LiDAR scene data. By dividing the entire scene into voxels to facilitate the processes of diffusion and reverse diffusion, LiDARPure overcomes challenges induced from adversarial training, such as sparse point clouds in large-scale LiDAR data and gradient confusion. In addition, we utilize the latent geometric features of a scene as a condition to assist the generation of diffusion models. Detailed experiments show that LiDARPure can effectively purify 19 common types of LiDAR data corruption. Further evaluation results demonstrate that it can improve the average precision of 3D object detectors to an extent of 20% in the face of data corruption, much higher than existing defence strategies.<\/jats:p>","DOI":"10.3390\/s24165440","type":"journal-article","created":{"date-parts":[[2024,8,23]],"date-time":"2024-08-23T12:58:07Z","timestamp":1724417887000},"page":"5440","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Diffusion Models-Based Purification for Common Corruptions on Robust 3D Object Detection"],"prefix":"10.3390","volume":"24","author":[{"given":"Mumuxin","family":"Cai","sequence":"first","affiliation":[{"name":"School of Information and Software Engineering, The University of Electronic Science and Technology of China, Chengdu 610054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4160-8552","authenticated-orcid":false,"given":"Xupeng","family":"Wang","sequence":"additional","affiliation":[{"name":"Laboratory Of Intelligent Collaborative Computing, The University of Electronic Science and Technology of China, Chengdu 610054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1557-4907","authenticated-orcid":false,"given":"Ferdous","family":"Sohel","sequence":"additional","affiliation":[{"name":"School of Information Technology, Murdoch University, Perth, WA 6150, Australia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hang","family":"Lei","sequence":"additional","affiliation":[{"name":"School of Information and Software Engineering, The University of Electronic Science and Technology of China, Chengdu 610054, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.inffus.2020.11.002","article-title":"Point-cloud based 3D object detection and classification methods for self-driving applications: A survey and taxonomy","volume":"68","author":"Fernandes","year":"2021","journal-title":"Inf. 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