{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,7]],"date-time":"2026-04-07T11:32:19Z","timestamp":1775561539093,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2022,3,18]],"date-time":"2022-03-18T00:00:00Z","timestamp":1647561600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["Project No.42130112, 42171456 and 41801317"],"award-info":[{"award-number":["Project No.42130112, 42171456 and 41801317"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate and efficient environmental awareness is a fundamental capability of autonomous driving technology and the real-time data collected by sensors offer autonomous vehicles an intuitive impression of their environment. Unfortunately, the ambient noise caused by varying weather conditions immediately affects the ability of autonomous vehicles to accurately understand their environment and its expected impact. In recent years, researchers have improved the environmental perception capabilities of simultaneous localization and mapping (SLAM), object detection and tracking, semantic segmentation and panoptic segmentation, but relatively few studies have focused on enhancing environmental perception capabilities in adverse weather conditions, such as rain, snow and fog. To enhance the environmental perception of autonomous vehicles in adverse weather, we developed a dynamic filtering method called Dynamic Distance\u2013Intensity Outlier Removal (DDIOR), which integrates the distance and intensity of points based on the systematic and accurate analysis of LiDAR point cloud data characteristics in snowy weather. Experiments on the publicly available WADS dataset (Winter Adverse Driving dataSet) showed that our method can efficiently remove snow noise while fully preserving the detailed features of the environment.<\/jats:p>","DOI":"10.3390\/rs14061468","type":"journal-article","created":{"date-parts":[[2022,3,20]],"date-time":"2022-03-20T21:37:17Z","timestamp":1647812237000},"page":"1468","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":52,"title":["A Scalable and Accurate De-Snowing Algorithm for LiDAR Point Clouds in Winter"],"prefix":"10.3390","volume":"14","author":[{"given":"Weiqi","family":"Wang","sequence":"first","affiliation":[{"name":"Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450052, China"}]},{"given":"Xiong","family":"You","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450052, China"}]},{"given":"Lingyu","family":"Chen","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450052, China"}]},{"given":"Jiangpeng","family":"Tian","sequence":"additional","affiliation":[{"name":"Institute of Geospatial Information, PLA Strategic Support Force Information Engineering University, Zhengzhou 450052, China"}]},{"given":"Fen","family":"Tang","sequence":"additional","affiliation":[{"name":"Institute of Information and Communication, National University of Defense Technology, Wuhan 430014, China"}]},{"given":"Lantian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Remote Sensing Information, Beijing 100011, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.iatssr.2019.11.005","article-title":"Automated driving recognition technologies for adverse weather conditions","volume":"43","author":"Yoneda","year":"2019","journal-title":"IATSS Res."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Wang, H., Yue, Z., Xie, Q., Xie, Q., Zhao, Q., Zheng, Y., and Meng, D. (2021, January 20\u201325). From Rain Generation to Rain Removal. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Nashville, TN, USA.","DOI":"10.1109\/CVPR46437.2021.01455"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Lin, S.L., and Wu, B.H. (2021). Application of Kalman Filter to Improve 3D LiDAR Signals of Autonomous Vehicles in Adverse Weather. Appl. Sci., 11.","DOI":"10.3390\/app11073018"},{"key":"ref_4","unstructured":"Zhang, Y., Carballo, A., Yang, H., and Takeda, K. (2021). Autonomous Driving in Adverse Weather Conditions: A Survey. arXiv, 2112."},{"key":"ref_5","unstructured":"Kurup, A., and Bos, J. (2021). DSOR: A Scalable Statistical Filter for Removing Falling Snow from LiDAR Point Clouds in Severe Winter Weather. arXiv."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"160202","DOI":"10.1109\/ACCESS.2020.3020266","article-title":"Fast and Accurate Desnowing Algorithm for LiDAR Point Clouds","volume":"8","author":"Park","year":"2020","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Charron, N., Phillips, S., and Waslander, S.L. (2018, January 8\u201310). De-noising of lidar point clouds corrupted by snowfall. Proceedings of the 15th IEEE Conference on Computer and Robot Vision (CRV), Toronto, ON, Canada.","DOI":"10.1109\/CRV.2018.00043"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2514","DOI":"10.1109\/LRA.2020.2972865","article-title":"Cnn-based lidar point cloud de-noising in adverse weather","volume":"5","author":"Heinzler","year":"2020","journal-title":"IEEE Robot. Autom. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.image.2017.05.009","article-title":"review of algorithms for filtering the 3D point cloud","volume":"57","author":"Han","year":"2017","journal-title":"Signal Processing Image Commun."},{"key":"ref_10","unstructured":"Narv\u00e1ez, E.A.L., and Narv\u00e1ez, N.E.L. (2006, January 25\u201328). Point cloud de-noising using robust principal component analysis. Proceedings of the International Conference on Computer Graphics Theory and Applications, Set\u00fabal, Portugal."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1111\/j.1467-8659.2006.00957.x","article-title":"Bayesian point cloud reconstruction","volume":"Volume 25","author":"Jenke","year":"2006","journal-title":"Computer Graphics Forum"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Paris, S. (2001, January 12\u201317). A gentle introduction to bilateral filtering and its applications. Proceedings of the ACM SIGGRAPH 2007 Courses, Los Angeles, CA, USA. Available online: https:\/\/dl.acm.org\/doi\/proceedings\/10.1145\/12815003-es.","DOI":"10.1145\/1281500.1281604"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1145\/1276377.1276405","article-title":"Parameterization-free projection for geometry reconstruction","volume":"26","author":"Lipman","year":"2007","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/1618452.1618522","article-title":"Consolidation of unorganized point clouds for surface reconstruction","volume":"28","author":"Huang","year":"2009","journal-title":"ACM Trans. Graph. (TOG)"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Rusu, R.B., and Cousins, S. (2011, January 9\u201313). 3D is here: Point cloud library (PCL). Proceedings of the 2011 IEEE International Conference on Robotics and Automation, Shanghai, China.","DOI":"10.1109\/ICRA.2011.5980567"},{"key":"ref_16","first-page":"70","article-title":"Iterative consolidation of unorganized point clouds","volume":"32","author":"Liu","year":"2011","journal-title":"IEEE Comput. Graph. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"348","DOI":"10.1016\/j.ifacol.2018.11.566","article-title":"Fast statistical outlier removal based method for large 3D point clouds of outdoor environments","volume":"51","author":"Balta","year":"2018","journal-title":"IFAC-PapersOnLine"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"126567","DOI":"10.1016\/j.optcom.2020.126567","article-title":"Low-complexity point cloud de-noising for LiDAR by PCA-based dimension reduction","volume":"482","author":"Duan","year":"2021","journal-title":"Opt. Commun."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1621","DOI":"10.1109\/JSEN.2021.3133873","article-title":"\u201cDIOR: A Hardware-Assisted Weather De-noising Solution for LiDAR Point Clouds","volume":"22","author":"Roriz","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Piewak, F., Pinggera, P., Schafer, M., Peter, D., Schwarz, B., Schneider, N., Enzweiler, M., Pfeiffer, D., and Z\u00f6llner, M. (2018, January 8\u201314). Boosting lidar-based semantic labeling by cross-modal training data generation. Proceedings of the European Conference on Computer Vision (ECCV), Munich, Germany.","DOI":"10.1007\/978-3-030-11024-6_39"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1231","DOI":"10.1177\/0278364913491297","article-title":"Vision meets robotics: The kitti dataset","volume":"32","author":"Geiger","year":"2013","journal-title":"Int. J. Robot. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1177\/0278364916679498","article-title":"1 year, 1000 km: The Oxford robotcar dataset","volume":"36","author":"Maddern","year":"2017","journal-title":"Int. J. Robot. Res."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2702","DOI":"10.1109\/TPAMI.2019.2926463","article-title":"The apolloscape open dataset for autonomous driving and its application","volume":"42","author":"Huang","year":"2019","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Sun, P., Kretzschmar, H., Dotiwalla, X., Chouard, A., Patnaik, V., Tsui, P., Guo, J., Zhou, Y., Chai, Y., and Caine, B. (2020, January 13\u201319). Scalability in perception for autonomous driving: Waymo open dataset. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.00252"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Caesar, H., Bankiti, V., Lang, A.H., Vora, S., Liong, V.E., Xu, Q., Krishnan, A., Pan, Y., Baldan, G., and Beijbom, O. (2020, January 13\u201319). Nuscenes: A multimodal dataset for autonomous driving. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01164"},{"key":"ref_26","unstructured":"Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., and Gall, J. (November, January 27). Semantickitti: A dataset for semantic scene understanding of lidar sequences. Proceedings of the IEEE\/CVF International Conference on Computer Vision, Seoul, Korea."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Bijelic, M., Gruber, T., Mannan, F., Kraus, F., Ritter, W., Dietmayer, K., and Heide, F. (2020, January 15). Seeing through fog without seeing fog: Deep multimodal sensor fusion in unseen adverse weather. Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR42600.2020.01170"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"681","DOI":"10.1177\/0278364920979368","article-title":"Canadian adverse driving conditions dataset","volume":"40","author":"Pitropov","year":"2021","journal-title":"Int. J. Robot. Res."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Yan, Y., Mao, Y., and Li, B. (2018). SECOND: Sparsely Embedded Convolutional Detection. Sensors, 18.","DOI":"10.3390\/s18103337"},{"key":"ref_30","unstructured":"Pan, Y., Xiao, P., He, Y., Shao, Z., and Li, Z. (June, January 30). MULLS: Versatile LiDAR SLAM via Multi-metric Linear Least Square. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), Xi\u2019an, China."},{"key":"ref_31","unstructured":"Grupp, M. (2022, March 10). Evo: Python PackAge for the Evaluation of Odometry and Slam. Available online: https:\/\/github.com\/MichaelGrupp\/evo."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/6\/1468\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:39:06Z","timestamp":1760135946000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/14\/6\/1468"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,3,18]]},"references-count":31,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2022,3]]}},"alternative-id":["rs14061468"],"URL":"https:\/\/doi.org\/10.3390\/rs14061468","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,3,18]]}}}