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To overcome these challenges, we propose pillar\u2010wise attention and semantic enhancement network (PASENet), an end\u2010to\u2010end network specifically designed for snowy scene. PASENet first employs pillar\u2010wise attention during feature extraction, utilizing L2\u2010normalized feature aggregation to compute query\u2010key differences, thereby enhancing relevant object features while suppressing snow noise. Secondly, we introduce a semantic enhancement branch where a lightweight U\u2010Net predicts pixel\u2010wise foreground segmentation in bird's\u2010eye\u2010view map and a segmentation head generates feature maps according to classes, supervised by free\u2010of\u2010charge semantic segmentation labels. The resulting semantically enhanced features are fused with pillar features to boost discriminability. Extensive experiments on the real\u2010world snowy STF dataset demonstrate an average performance improvement of 1.3% across varying snowfall intensities, while maintaining robustness in clear weather with 2.2% gain on the KITTI validation set.<\/jats:p>","DOI":"10.1049\/ipr2.70257","type":"journal-article","created":{"date-parts":[[2025,12,30]],"date-time":"2025-12-30T11:22:34Z","timestamp":1767093754000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["PASENet: Snowy Scene 3D Object Detection With Pillar\u2010Wise Attention and Semantic Enhancement"],"prefix":"10.1049","volume":"20","author":[{"given":"Yutian","family":"Wu","sequence":"first","affiliation":[{"name":"School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China"},{"name":"Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education of the People's Republic of China Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1986-7515","authenticated-orcid":false,"given":"Wenwei","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zuodong","family":"Zhong","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qing","family":"Li","sequence":"additional","affiliation":[{"name":"School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China"},{"name":"Key Laboratory of Knowledge Automation for Industrial Processes Ministry of Education of the People's Republic of China Beijing China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"265","published-online":{"date-parts":[[2025,12,30]]},"reference":[{"key":"e_1_2_10_2_1","doi-asserted-by":"publisher","DOI":"10.1049\/ipr2.13185"},{"issue":"5","key":"e_1_2_10_3_1","first-page":"1081","article-title":"Overview of Object Detection Methods Based on Lidar Point Cloud Under Adverse Weather Conditions","volume":"47","author":"Wu Y.","year":"2025","journal-title":"Chinese Journal of Engineering"},{"key":"e_1_2_10_4_1","doi-asserted-by":"publisher","DOI":"10.1109\/JSEN.2020.3020626"},{"key":"e_1_2_10_5_1","unstructured":"C. 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