{"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/fiery-future-instance-prediction-in-bird-s","title":"FIERY: Future Instance Prediction in Bird's-Eye View from Surround Monocular Cameras","arxiv_id":"2104.10490","date":"2021-04-21","proceeding":"ICCV 2021 10","authors":["Anthony Hu","Zak Murez","Nikhil Mohan","Sofía Dudas","Jeffrey Hawke","Vijay Badrinarayanan","Roberto Cipolla","Alex Kendall"],"abstract":"Driving requires interacting with road agents and predicting their future behaviour in order to navigate safely. We present FIERY: a probabilistic future prediction model in bird's-eye view from monocular cameras. Our model predicts future instance segmentation and motion of dynamic agents that can be transformed into non-parametric future trajectories. Our approach combines the perception, sensor fusion and prediction components of a traditional autonomous driving stack by estimating bird's-eye-view prediction directly from surround RGB monocular camera inputs. FIERY learns to model the inherent stochastic nature of the future solely from camera driving data in an end-to-end manner, without relying on HD maps, and predicts multimodal future trajectories. We show that our model outperforms previous prediction baselines on the NuScenes and Lyft datasets. The code and trained models are available at https://github.com/wayveai/fiery.","url_abs":"https://arxiv.org/abs/2104.10490v3","url_pdf":"https://arxiv.org/pdf/2104.10490v3.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":"fiery-future-instance-prediction-in-bird-s","repo_url":"https://github.com/wayveai/fiery","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"autonomous-driving","task_name":"Autonomous Driving"},{"task_slug":"bird-s-eye-view-semantic-segmentation","task_name":"Bird's-Eye View Semantic Segmentation"},{"task_slug":"future-prediction","task_name":"Future prediction"},{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"task_slug":"navigate","task_name":"Navigate"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"sensor-fusion","task_name":"Sensor Fusion"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/bird-s-eye-view-semantic-segmentation-on-lyft","task":"Bird's-Eye View Semantic Segmentation","dataset":"Lyft Level 5","model":"FIERY","rank_in_archive_order":7,"of":7,"metrics":{"IoU vehicle - 224x480 - Long":"36.7","IoU vehicle - 224x480 - Short":"59.4"},"uses_additional_data":false},{"leaderboard":"/sota/bird-s-eye-view-semantic-segmentation-on","task":"Bird's-Eye View Semantic Segmentation","dataset":"nuScenes","model":"FIERY (static)","rank_in_archive_order":5,"of":17,"metrics":{"IoU ped - 224x480 - Vis filter. - 100x100 at 0.5":"17.2","IoU veh - 224x480 - No vis filter - 100x100 at 0.5":"35.8","IoU veh - 224x480 - Vis filter. - 100x100 at 0.5":"39.8"},"uses_additional_data":false},{"leaderboard":"/sota/bird-s-eye-view-semantic-segmentation-on","task":"Bird's-Eye View Semantic Segmentation","dataset":"nuScenes","model":"FIERY","rank_in_archive_order":9,"of":17,"metrics":{"IoU veh - 224x480 - No vis filter - 100x100 at 0.5":"38.2","IoU veh - 224x480 - No vis filter - 100x50 at 0.25":"41.1","IoU vehicle - Setting 3":"58.5"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2104.10490","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2104.10490"}},"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. 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