{"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/automatic-labeled-lidar-data-generation-based","title":"Automatic Labeled LiDAR Data Generation based on Precise Human Model","arxiv_id":"1902.05341","date":"2019-02-14","proceeding":null,"authors":["Wonjik Kim","Masayuki Tanaka","Masatoshi Okutomi","Yoko SASAKI"],"abstract":"Following improvements in deep neural networks, state-of-the-art networks\nhave been proposed for human recognition using point clouds captured by LiDAR.\nHowever, the performance of these networks strongly depends on the training\ndata. An issue with collecting training data is labeling. Labeling by humans is\nnecessary to obtain the ground truth label; however, labeling requires huge\ncosts. Therefore, we propose an automatic labeled data generation pipeline, for\nwhich we can change any parameters or data generation environments. Our\napproach uses a human model named Dhaiba and a background of Miraikan and\nconsequently generated realistic artificial data. We present 500k+ data\ngenerated by the proposed pipeline. This paper also describes the specification\nof the pipeline and data details with evaluations of various approaches.","url_abs":"http://arxiv.org/abs/1902.05341v1","url_pdf":"http://arxiv.org/pdf/1902.05341v1.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":"automatic-labeled-lidar-data-generation-based","repo_url":"https://github.com/Likarian/AutomaticLabeledLiDARData","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}