{"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/radarocc-robust-3d-occupancy-prediction-with","title":"RadarOcc: Robust 3D Occupancy Prediction with 4D Imaging Radar","arxiv_id":"2405.14014","date":"2024-05-22","proceeding":null,"authors":["Fangqiang Ding","Xiangyu Wen","Yunzhou Zhu","Yiming Li","Chris Xiaoxuan Lu"],"abstract":"3D occupancy-based perception pipeline has significantly advanced autonomous driving by capturing detailed scene descriptions and demonstrating strong generalizability across various object categories and shapes. Current methods predominantly rely on LiDAR or camera inputs for 3D occupancy prediction. These methods are susceptible to adverse weather conditions, limiting the all-weather deployment of self-driving cars. To improve perception robustness, we leverage the recent advances in automotive radars and introduce a novel approach that utilizes 4D imaging radar sensors for 3D occupancy prediction. Our method, RadarOcc, circumvents the limitations of sparse radar point clouds by directly processing the 4D radar tensor, thus preserving essential scene details. RadarOcc innovatively addresses the challenges associated with the voluminous and noisy 4D radar data by employing Doppler bins descriptors, sidelobe-aware spatial sparsification, and range-wise self-attention mechanisms. To minimize the interpolation errors associated with direct coordinate transformations, we also devise a spherical-based feature encoding followed by spherical-to-Cartesian feature aggregation. We benchmark various baseline methods based on distinct modalities on the public K-Radar dataset. The results demonstrate RadarOcc's state-of-the-art performance in radar-based 3D occupancy prediction and promising results even when compared with LiDAR- or camera-based methods. Additionally, we present qualitative evidence of the superior performance of 4D radar in adverse weather conditions and explore the impact of key pipeline components through ablation studies.","url_abs":"https://arxiv.org/abs/2405.14014v4","url_pdf":"https://arxiv.org/pdf/2405.14014v4.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":"radarocc-robust-3d-occupancy-prediction-with","repo_url":"https://github.com/toytiny/radarocc","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"self-driving-cars","task_name":"Self-Driving Cars"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2405.14014","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.14014"}},"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":"deterministic:regex_extraction","url":"https://github.com/Toytiny/RadarOcc","reach":{"status":"ok","spdx":"Apache-2.0"}},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/toytiny/radarocc","reach":{"status":"ok","spdx":"Apache-2.0"}}],"summary":{"ran":3,"unverified":3},"by_repo_kind":{"official":{"samples":6,"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":"06e19c1d21f057c6","entry":"is_number","repo":"toytiny/radarocc","repo_kind":"official","path":"generate_4d_polar_percentil.py","file_url":"https://github.com/toytiny/radarocc/blob/HEAD/generate_4d_polar_percentil.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"06e19c1d21f057c6"}},{"code_sha256_prefix":"58f05ca94a1f16ad","entry":"process_file","repo":"toytiny/radarocc","repo_kind":"official","path":"generate_3d_polar.py","file_url":"https://github.com/toytiny/radarocc/blob/HEAD/generate_3d_polar.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"58f05ca94a1f16ad"}},{"code_sha256_prefix":"cccc647437ae78d0","entry":"process_file","repo":"toytiny/radarocc","repo_kind":"official","path":"generate_4d_polar.py","file_url":"https://github.com/toytiny/radarocc/blob/HEAD/generate_4d_polar.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"cccc647437ae78d0"}},{"code_sha256_prefix":"7ca7350ddb4ca251","entry":"process_file","repo":"Toytiny/RadarOcc","repo_kind":"official","path":"generate_4d_polar_doppler.py","file_url":"https://github.com/Toytiny/RadarOcc/blob/HEAD/generate_4d_polar_doppler.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":"7ca7350ddb4ca251"}},{"code_sha256_prefix":"bca17c9a1a27be2b","entry":"process_file","repo":"toytiny/radarocc","repo_kind":"official","path":"generate_4d_polar_doppler.py","file_url":"https://github.com/toytiny/radarocc/blob/HEAD/generate_4d_polar_doppler.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":"bca17c9a1a27be2b"}},{"code_sha256_prefix":"c69c41474090b153","entry":"process_file","repo":"toytiny/radarocc","repo_kind":"official","path":"generate_4d_polar_percentil.py","file_url":"https://github.com/toytiny/radarocc/blob/HEAD/generate_4d_polar_percentil.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":"c69c41474090b153"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}