{"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/quantitative-depth-quality-assessment-of-rgbd","title":"Quantitative Depth Quality Assessment of RGBD Cameras At Close Range Using 3D Printed Fixtures","arxiv_id":"1903.09169","date":"2019-03-21","proceeding":null,"authors":["Michele Pratusevich","Jason Chrisos","Shreyas Aditya"],"abstract":"Mobile robots that manipulate their environments require high-accuracy scene\nunderstanding at close range. Typically this understanding is achieved with\nRGBD cameras, but the evaluation process for selecting an appropriate RGBD\ncamera for the application is minimally quantitative. Limited\nmanufacturer-published metrics do not translate to observed quality in\nreal-world cluttered environments, since quality is application-specific. To\nbridge the gap, we present a method for quantitatively measuring depth quality\nusing a set of extendable 3D printed fixtures that approximate real-world\nconditions. By framing depth quality as point cloud density and root mean\nsquare error (RMSE) from a known geometry, we present a method that is\nextendable by other system integrators for custom environments. We show a\ncomparison of 3 cameras and present a case study for camera selection, provide\nreference meshes and analysis code, and discuss further extensions.","url_abs":"http://arxiv.org/abs/1903.09169v1","url_pdf":"http://arxiv.org/pdf/1903.09169v1.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":"quantitative-depth-quality-assessment-of-rgbd","repo_url":"https://github.com/root-ai/depth-quality","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"scene-understanding","task_name":"Scene Understanding"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}