{"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/glenet-boosting-3d-object-detectors-with","title":"GLENet: Boosting 3D Object Detectors with Generative Label Uncertainty Estimation","arxiv_id":"2207.02466","date":"2022-07-06","proceeding":null,"authors":["Yifan Zhang","Qijian Zhang","Zhiyu Zhu","Junhui Hou","Yixuan Yuan"],"abstract":"The inherent ambiguity in ground-truth annotations of 3D bounding boxes, caused by occlusions, signal missing, or manual annotation errors, can confuse deep 3D object detectors during training, thus deteriorating detection accuracy. However, existing methods overlook such issues to some extent and treat the labels as deterministic. In this paper, we formulate the label uncertainty problem as the diversity of potentially plausible bounding boxes of objects. Then, we propose GLENet, a generative framework adapted from conditional variational autoencoders, to model the one-to-many relationship between a typical 3D object and its potential ground-truth bounding boxes with latent variables. The label uncertainty generated by GLENet is a plug-and-play module and can be conveniently integrated into existing deep 3D detectors to build probabilistic detectors and supervise the learning of the localization uncertainty. Besides, we propose an uncertainty-aware quality estimator architecture in probabilistic detectors to guide the training of the IoU-branch with predicted localization uncertainty. We incorporate the proposed methods into various popular base 3D detectors and demonstrate significant and consistent performance gains on both KITTI and Waymo benchmark datasets. Especially, the proposed GLENet-VR outperforms all published LiDAR-based approaches by a large margin and achieves the top rank among single-modal methods on the challenging KITTI test set. The source code and pre-trained models are publicly available at \\url{https://github.com/Eaphan/GLENet}.","url_abs":"https://arxiv.org/abs/2207.02466v5","url_pdf":"https://arxiv.org/pdf/2207.02466v5.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":"glenet-boosting-3d-object-detectors-with","repo_url":"https://github.com/Eaphan/GLENet","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"}],"methods":[{"method_slug":"base","method_name":"BASE"},{"method_slug":"test","method_name":"Test"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-object-detection-on-kitti-cars-easy","task":"3D Object Detection","dataset":"KITTI Cars Easy","model":"GLENet-VR","rank_in_archive_order":2,"of":26,"metrics":{"AP":"91.67%"},"uses_additional_data":false},{"leaderboard":"/sota/3d-object-detection-on-kitti-cars-hard","task":"3D Object Detection","dataset":"KITTI Cars Hard","model":"GLENet-VR","rank_in_archive_order":3,"of":25,"metrics":{"AP":"78.43%"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2207.02466","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2207.02466"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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/Eaphan/GLENet","reach":{"status":"ok","spdx":"Apache-2.0"}}],"summary":{"ran_honours":1,"unverified":4},"by_repo_kind":{"official":{"samples":5,"ran":1,"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":"c23b05634625d281","entry":"l2_regularisation","repo":"Eaphan/GLENet","repo_kind":"official","path":"cvae_uncertainty/model.py","file_url":"https://github.com/Eaphan/GLENet/blob/HEAD/cvae_uncertainty/model.py","link_basis":"harvester_set","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"c23b05634625d281"}},{"code_sha256_prefix":"687c256752aa973e","entry":"get_coor_colors","repo":"Eaphan/GLENet","repo_kind":"official","path":"cvae_uncertainty/open3d_vis_utils.py","file_url":"https://github.com/Eaphan/GLENet/blob/HEAD/cvae_uncertainty/open3d_vis_utils.py","link_basis":"harvester_set","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":"687c256752aa973e"}},{"code_sha256_prefix":"e44c0fe71663160d","entry":"rotate_points_along_z","repo":"Eaphan/GLENet","repo_kind":"official","path":"cvae_uncertainty/dataset.py","file_url":"https://github.com/Eaphan/GLENet/blob/HEAD/cvae_uncertainty/dataset.py","link_basis":"harvester_set","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":"e44c0fe71663160d"}},{"code_sha256_prefix":"a047a351d772c575","entry":"scan_to_rv","repo":"Eaphan/GLENet","repo_kind":"official","path":"cvae_uncertainty/dataset.py","file_url":"https://github.com/Eaphan/GLENet/blob/HEAD/cvae_uncertainty/dataset.py","link_basis":"harvester_set","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":"a047a351d772c575"}},{"code_sha256_prefix":"f96f27ef1af8e78a","entry":"scan_to_rv_waymo","repo":"Eaphan/GLENet","repo_kind":"official","path":"cvae_uncertainty/dataset.py","file_url":"https://github.com/Eaphan/GLENet/blob/HEAD/cvae_uncertainty/dataset.py","link_basis":"harvester_set","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":"f96f27ef1af8e78a"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}