{"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/ea-bev-edge-aware-bird-s-eye-view-projector","title":"EA-LSS: Edge-aware Lift-splat-shot Framework for 3D BEV Object Detection","arxiv_id":"2303.17895","date":"2023-03-31","proceeding":null,"authors":["Haotian Hu","Fanyi Wang","Jingwen Su","Yaonong Wang","Laifeng Hu","Weiye Fang","Jingwei Xu","Zhiwang Zhang"],"abstract":"In recent years, great progress has been made in the Lift-Splat-Shot-based (LSS-based) 3D object detection method. However, inaccurate depth estimation remains an important constraint to the accuracy of camera-only and multi-model 3D object detection models, especially in regions where the depth changes significantly (i.e., the \"depth jump\" problem). In this paper, we proposed a novel Edge-aware Lift-splat-shot (EA-LSS) framework. Specifically, edge-aware depth fusion (EADF) module is proposed to alleviate the \"depth jump\" problem and fine-grained depth (FGD) module to further enforce refined supervision on depth. Our EA-LSS framework is compatible for any LSS-based 3D object detection models, and effectively boosts their performances with negligible increment of inference time. Experiments on nuScenes benchmarks demonstrate that EA-LSS is effective in either camera-only or multi-model models. It is worth mentioning that EA-LSS achieved the state-of-the-art performance on nuScenes test benchmarks with mAP and NDS of 76.5% and 77.6%, respectively.","url_abs":"https://arxiv.org/abs/2303.17895v4","url_pdf":"https://arxiv.org/pdf/2303.17895v4.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":"ea-bev-edge-aware-bird-s-eye-view-projector","repo_url":"https://github.com/hht1996ok/ea-bev","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"3d-object-detection","task_name":"3D Object Detection"},{"task_slug":"depth-estimation","task_name":"Depth Estimation"},{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-object-detection-on-nuscenes","task":"3D Object Detection","dataset":"nuScenes","model":"EA-LSS","rank_in_archive_order":1,"of":372,"metrics":{"NDS":"0.78","mAAE":"0.12","mAOE":"0.28","mAP":"0.77","mASE":"0.21","mATE":"0.23","mAVE":"0.20"},"uses_additional_data":false}],"syntology":{"syntology_url":"https://syntology.ai/paper/2303.17895","atlas_url":"https://app.syntology.ai/?focus=2303.17895","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2303.17895"}},"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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