{"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/blvd-building-a-large-scale-5d-semantics","title":"BLVD: Building A Large-scale 5D Semantics Benchmark for Autonomous Driving","arxiv_id":"1903.06405","date":"2019-03-15","proceeding":null,"authors":["Jianru Xue","Jianwu Fang","Tao Li","Bohua Zhang","Pu Zhang","Zhen Ye","Jian Dou"],"abstract":"In autonomous driving community, numerous benchmarks have been established to\nassist the tasks of 3D/2D object detection, stereo vision, semantic/instance\nsegmentation. However, the more meaningful dynamic evolution of the surrounding\nobjects of ego-vehicle is rarely exploited, and lacks a large-scale dataset\nplatform. To address this, we introduce BLVD, a large-scale 5D semantics\nbenchmark which does not concentrate on the static detection or\nsemantic/instance segmentation tasks tackled adequately before. Instead, BLVD\naims to provide a platform for the tasks of dynamic 4D (3D+temporal) tracking,\n5D (4D+interactive) interactive event recognition and intention prediction.\nThis benchmark will boost the deeper understanding of traffic scenes than ever\nbefore. We totally yield 249,129 3D annotations, 4,902 independent individuals\nfor tracking with the length of overall 214,922 points, 6,004 valid fragments\nfor 5D interactive event recognition, and 4,900 individuals for 5D intention\nprediction. These tasks are contained in four kinds of scenarios depending on\nthe object density (low and high) and light conditions (daytime and nighttime).\nThe benchmark can be downloaded from our project site\nhttps://github.com/VCCIV/BLVD/.","url_abs":"http://arxiv.org/abs/1903.06405v1","url_pdf":"http://arxiv.org/pdf/1903.06405v1.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":"blvd-building-a-large-scale-5d-semantics","repo_url":"https://github.com/VCCIV/BLVD","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"2d-object-detection","task_name":"2D Object Detection"},{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"object-detection","task_name":"Object Detection"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"object-detection-1","task_name":"object-detection"},{"task_slug":null,"task_name":"valid"}],"methods":[],"datasets_introduced":[{"slug":"blvd","name":"BLVD","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1903.06405","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1903.06405"}},"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/VCCIV/BLVD","reach":null}],"summary":{"ran_honours":1,"ran_draft_wrong":2},"by_repo_kind":{"official":{"samples":3,"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":3,"samples":[{"code_sha256_prefix":"716c663876bbf4ba","entry":"compute_box_corners_3d","repo":"VCCIV/BLVD","repo_kind":"official","path":"utils.py","file_url":"https://github.com/VCCIV/BLVD/blob/HEAD/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":"well_formed","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"716c663876bbf4ba"}},{"code_sha256_prefix":"d070e0ebc13c8b39","entry":"get_lidar_in_image_fov","repo":"VCCIV/BLVD","repo_kind":"official","path":"utils.py","file_url":"https://github.com/VCCIV/BLVD/blob/HEAD/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"d070e0ebc13c8b39"}},{"code_sha256_prefix":"16da97b8bddad35f","entry":"read_label","repo":"VCCIV/BLVD","repo_kind":"official","path":"utils.py","file_url":"https://github.com/VCCIV/BLVD/blob/HEAD/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"16da97b8bddad35f"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}