{"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/pedx-benchmark-dataset-for-metric-3d-pose","title":"PedX: Benchmark Dataset for Metric 3D Pose Estimation of Pedestrians in Complex Urban Intersections","arxiv_id":"1809.03605","date":"2018-09-10","proceeding":null,"authors":["Wonhui Kim","Manikandasriram Srinivasan Ramanagopal","Charles Barto","Ming-Yuan Yu","Karl Rosaen","Nick Goumas","Ram Vasudevan","Matthew Johnson-Roberson"],"abstract":"This paper presents a novel dataset titled PedX, a large-scale multimodal\ncollection of pedestrians at complex urban intersections. PedX consists of more\nthan 5,000 pairs of high-resolution (12MP) stereo images and LiDAR data along\nwith providing 2D and 3D labels of pedestrians. We also present a novel 3D\nmodel fitting algorithm for automatic 3D labeling harnessing constraints across\ndifferent modalities and novel shape and temporal priors. All annotated 3D\npedestrians are localized into the real-world metric space, and the generated\n3D models are validated using a mocap system configured in a controlled outdoor\nenvironment to simulate pedestrians in urban intersections. We also show that\nthe manual 2D labels can be replaced by state-of-the-art automated labeling\napproaches, thereby facilitating automatic generation of large scale datasets.","url_abs":"http://arxiv.org/abs/1809.03605v1","url_pdf":"http://arxiv.org/pdf/1809.03605v1.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":[],"tasks":[{"task_slug":"3d-pose-estimation","task_name":"3D Pose Estimation"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[{"slug":"pedx","name":"PedX","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1809.03605","atlas_url":"https://app.syntology.ai/?focus=1809.03605","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}