{"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/structured-bird-s-eye-view-traffic-scene-1","title":"Structured Bird's-Eye-View Traffic Scene Understanding from Onboard Images","arxiv_id":"2110.01997","date":"2021-10-05","proceeding":"ICCV 2021 10","authors":["Yigit Baran Can","Alexander Liniger","Danda Pani Paudel","Luc van Gool"],"abstract":"Autonomous navigation requires structured representation of the road network and instance-wise identification of the other traffic agents. Since the traffic scene is defined on the ground plane, this corresponds to scene understanding in the bird's-eye-view (BEV). However, the onboard cameras of autonomous cars are customarily mounted horizontally for a better view of the surrounding, making this task very challenging. In this work, we study the problem of extracting a directed graph representing the local road network in BEV coordinates, from a single onboard camera image. Moreover, we show that the method can be extended to detect dynamic objects on the BEV plane. The semantics, locations, and orientations of the detected objects together with the road graph facilitates a comprehensive understanding of the scene. Such understanding becomes fundamental for the downstream tasks, such as path planning and navigation. We validate our approach against powerful baselines and show that our network achieves superior performance. We also demonstrate the effects of various design choices through ablation studies. Code: https://github.com/ybarancan/STSU","url_abs":"https://arxiv.org/abs/2110.01997v1","url_pdf":"https://arxiv.org/pdf/2110.01997v1.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":"structured-bird-s-eye-view-traffic-scene-1","repo_url":"https://github.com/ybarancan/stsu","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"structured-bird-s-eye-view-traffic-scene-1","repo_url":"https://github.com/robin-karlsson0/dslp","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"autonomous-navigation","task_name":"Autonomous Navigation"},{"task_slug":"lane-detection","task_name":"Lane Detection"},{"task_slug":"scene-understanding","task_name":"Scene Understanding"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/lane-detection-on-nuscenes","task":"Lane Detection","dataset":"nuScenes","model":"STSU","rank_in_archive_order":3,"of":3,"metrics":{"F1 score":"0.560","IoU":"0.389"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2110.01997","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2110.01997"}},"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/ybarancan/stsu","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/robin-karlsson0/dslp","reach":{"status":"ok","spdx":"GPL-3.0"}}],"summary":{"ran_honours":2},"by_repo_kind":{"official":{"samples":2,"ran":2,"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":"533dd8b6be8afb96","entry":"load_checkpoint","repo":"ybarancan/stsu","repo_kind":"official","path":"train_tr.py","file_url":"https://github.com/ybarancan/stsu/blob/HEAD/train_tr.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":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"533dd8b6be8afb96"}},{"code_sha256_prefix":"363e4dd61dae7923","entry":"load_checkpoint","repo":"ybarancan/stsu","repo_kind":"official","path":"train_prnn.py","file_url":"https://github.com/ybarancan/stsu/blob/HEAD/train_prnn.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":"Apache-2.0","inline_ok":true,"mcp_get_code":{"code_sha256":"363e4dd61dae7923"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}