{"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/once-detected-never-lost-surpassing-human","title":"Once Detected, Never Lost: Surpassing Human Performance in Offline LiDAR based 3D Object Detection","arxiv_id":"2304.12315","date":"2023-04-24","proceeding":"ICCV 2023 1","authors":["Lue Fan","Yuxue Yang","Yiming Mao","Feng Wang","Yuntao Chen","Naiyan Wang","Zhaoxiang Zhang"],"abstract":"This paper aims for high-performance offline LiDAR-based 3D object detection. We first observe that experienced human annotators annotate objects from a track-centric perspective. They first label the objects with clear shapes in a track, and then leverage the temporal coherence to infer the annotations of obscure objects. Drawing inspiration from this, we propose a high-performance offline detector in a track-centric perspective instead of the conventional object-centric perspective. Our method features a bidirectional tracking module and a track-centric learning module. Such a design allows our detector to infer and refine a complete track once the object is detected at a certain moment. We refer to this characteristic as \"onCe detecTed, neveR Lost\" and name the proposed system CTRL. Extensive experiments demonstrate the remarkable performance of our method, surpassing the human-level annotating accuracy and the previous state-of-the-art methods in the highly competitive Waymo Open Dataset without model ensemble. The code will be made publicly available at https://github.com/tusen-ai/SST.","url_abs":"https://arxiv.org/abs/2304.12315v1","url_pdf":"https://arxiv.org/pdf/2304.12315v1.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":"once-detected-never-lost-surpassing-human","repo_url":"https://github.com/tusen-ai/sst","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"once-detected-never-lost-surpassing-human","repo_url":"https://github.com/tusimple/sst","is_official":0,"mentioned_in_paper":0,"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":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"object-detection-1","task_name":"object-detection"}],"methods":[{"method_slug":"adagrad","method_name":"AdaGrad"},{"method_slug":"attention","method_name":"Attention"},{"method_slug":"bpe","method_name":"BPE"},{"method_slug":"ctrl","method_name":"CTRL"},{"method_slug":"dense-connections","method_name":"Dense Connections"},{"method_slug":"dropout","method_name":"Dropout"},{"method_slug":"gradient-clipping","method_name":"Gradient Clipping"},{"method_slug":"layer-normalization","method_name":"Layer Normalization"},{"method_slug":"linear-layer","method_name":"Linear Layer"},{"method_slug":"linear-warmup","method_name":"Linear Warmup"},{"method_slug":"multi-head-attention","method_name":"Multi-Head Attention"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"softmax","method_name":"Softmax"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/2304.12315","atlas_url":"https://app.syntology.ai/?focus=2304.12315","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.12315"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/tusen-ai/sst","reach":{"status":"ok","spdx":"Apache-2.0"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/tusimple/sst","reach":{"status":"ok","spdx":"Apache-2.0"}},{"provenance":"deterministic:regex_extraction","url":"https://github.com/tusen-ai/SST","reach":{"status":"ok","spdx":"Apache-2.0"}}],"summary":{"ran_draft_wrong":2,"ran_fixture":1,"unverified":2},"by_repo_kind":{"official":{"samples":5,"ran":3,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":0,"samples":[{"code_sha256_prefix":"0df15159eda818b9","entry":"_in_projection","repo":"tusen-ai/SST","repo_kind":"official","path":"mmdet3d/models/sst/cosine_msa.py","file_url":"https://github.com/tusen-ai/SST/blob/HEAD/mmdet3d/models/sst/cosine_msa.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"0df15159eda818b9"}},{"code_sha256_prefix":"02a694ccb83fcce7","entry":"_in_projection_packed","repo":"tusen-ai/SST","repo_kind":"official","path":"mmdet3d/models/sst/cosine_msa.py","file_url":"https://github.com/tusen-ai/SST/blob/HEAD/mmdet3d/models/sst/cosine_msa.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"02a694ccb83fcce7"}},{"code_sha256_prefix":"13ea48f023c8a417","entry":"_scaled_cosine_attention","repo":"tusen-ai/SST","repo_kind":"official","path":"mmdet3d/models/sst/cosine_msa.py","file_url":"https://github.com/tusen-ai/SST/blob/HEAD/mmdet3d/models/sst/cosine_msa.py","link_basis":"first_harvest_node","language":"python","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"13ea48f023c8a417"}},{"code_sha256_prefix":"6798a13b4573e117","entry":"CosineMultiheadAttention","repo":"tusen-ai/SST","repo_kind":"official","path":"mmdet3d/models/sst/cosine_msa.py","file_url":"https://github.com/tusen-ai/SST/blob/HEAD/mmdet3d/models/sst/cosine_msa.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"6798a13b4573e117"}},{"code_sha256_prefix":"30bd5537682bdbad","entry":"cosine_multi_head_attention_forward","repo":"tusen-ai/SST","repo_kind":"official","path":"mmdet3d/models/sst/cosine_msa.py","file_url":"https://github.com/tusen-ai/SST/blob/HEAD/mmdet3d/models/sst/cosine_msa.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"30bd5537682bdbad"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}