{"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/dense-reppoints-representing-visual-objects","title":"Dense RepPoints: Representing Visual Objects with Dense Point Sets","arxiv_id":"1912.11473","date":"2019-12-24","proceeding":"ECCV 2020 8","authors":["Ze Yang","Yinghao Xu","Han Xue","Zheng Zhang","Raquel Urtasun","Li-Wei Wang","Stephen Lin","Han Hu"],"abstract":"We present a new object representation, called Dense RepPoints, that utilizes a large set of points to describe an object at multiple levels, including both box level and pixel level. Techniques are proposed to efficiently process these dense points, maintaining near-constant complexity with increasing point numbers. Dense RepPoints is shown to represent and learn object segments well, with the use of a novel distance transform sampling method combined with set-to-set supervision. The distance transform sampling combines the strengths of contour and grid representations, leading to performance that surpasses counterparts based on contours or grids. Code is available at \\url{https://github.com/justimyhxu/Dense-RepPoints}.","url_abs":"https://arxiv.org/abs/1912.11473v3","url_pdf":"https://arxiv.org/pdf/1912.11473v3.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":"dense-reppoints-representing-visual-objects","repo_url":"https://github.com/justimyhxu/Dense-RepPoints","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"dense-reppoints-representing-visual-objects","repo_url":"https://github.com/Scalsol/RepPointsV2","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"object","task_name":"Object"},{"task_slug":"object-detection","task_name":"Object Detection"}],"methods":[{"method_slug":"reppoints","method_name":"RepPoints"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1912.11473","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}