{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/cenet-toward-concise-and-efficient-lidar","title":"CENet: Toward Concise and Efficient LiDAR Semantic Segmentation for Autonomous Driving","arxiv_id":"2207.12691","date":"2022-07-26","proceeding":null,"authors":["Hui-Xian Cheng","Xian-Feng Han","Guo-Qiang Xiao"],"abstract":"Accurate and fast scene understanding is one of the challenging task for autonomous driving, which requires to take full advantage of LiDAR point clouds for semantic segmentation. In this paper, we present a \\textbf{concise} and \\textbf{efficient} image-based semantic segmentation network, named \\textbf{CENet}. In order to improve the descriptive power of learned features and reduce the computational as well as time complexity, our CENet integrates the convolution with larger kernel size instead of MLP, carefully-selected activation functions, and multiple auxiliary segmentation heads with corresponding loss functions into architecture. Quantitative and qualitative experiments conducted on publicly available benchmarks, SemanticKITTI and SemanticPOSS, demonstrate that our pipeline achieves much better mIoU and inference performance compared with state-of-the-art models. The code will be available at https://github.com/huixiancheng/CENet.","url_abs":"https://arxiv.org/abs/2207.12691v1","url_pdf":"https://arxiv.org/pdf/2207.12691v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"cenet-toward-concise-and-efficient-lidar","repo_url":"https://github.com/huixiancheng/cenet","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"cenet-toward-concise-and-efficient-lidar","repo_url":"https://github.com/happytianhao/tade","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"cenet-toward-concise-and-efficient-lidar","repo_url":"https://github.com/open-mmlab/mmdetection3d","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"3d-semantic-segmentation","task_name":"3D Semantic Segmentation"},{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"descriptive","task_name":"Descriptive"},{"task_slug":"lidar-semantic-segmentation","task_name":"LIDAR Semantic Segmentation"},{"task_slug":"robust-3d-semantic-segmentation","task_name":"Robust 3D Semantic Segmentation"},{"task_slug":"scene-understanding","task_name":"Scene Understanding"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[{"method_slug":"convolution","method_name":"Convolution"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/robust-3d-semantic-segmentation-on","task":"Robust 3D Semantic Segmentation","dataset":"SemanticKITTI-C","model":"CENet (64x2048)","rank_in_archive_order":9,"of":22,"metrics":{"mean Corruption Error (mCE)":"103.41%"},"uses_additional_data":false},{"leaderboard":"/sota/robust-3d-semantic-segmentation-on-nuscenes-c","task":"Robust 3D Semantic Segmentation","dataset":"nuScenes-C","model":"CENet","rank_in_archive_order":10,"of":12,"metrics":{"mean Corruption Error (mCE)":"112.79%"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2207.12691","atlas_url":"https://app.syntology.ai/?focus=2207.12691","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.12691"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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