{"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/endo-depth-and-motion-localization-and","title":"Endo-Depth-and-Motion: Reconstruction and Tracking in Endoscopic Videos using Depth Networks and Photometric Constraints","arxiv_id":"2103.16525","date":"2021-03-30","proceeding":null,"authors":["David Recasens","José Lamarca","José M. Fácil","J. M. M. Montiel","Javier Civera"],"abstract":"Estimating a scene reconstruction and the camera motion from in-body videos is challenging due to several factors, e.g. the deformation of in-body cavities or the lack of texture. In this paper we present Endo-Depth-and-Motion, a pipeline that estimates the 6-degrees-of-freedom camera pose and dense 3D scene models from monocular endoscopic videos. Our approach leverages recent advances in self-supervised depth networks to generate pseudo-RGBD frames, then tracks the camera pose using photometric residuals and fuses the registered depth maps in a volumetric representation. We present an extensive experimental evaluation in the public dataset Hamlyn, showing high-quality results and comparisons against relevant baselines. We also release all models and code for future comparisons.","url_abs":"https://arxiv.org/abs/2103.16525v2","url_pdf":"https://arxiv.org/pdf/2103.16525v2.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":"endo-depth-and-motion-localization-and","repo_url":"https://github.com/UZ-SLAMLab/Endo-Depth-and-Motion","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=2103.16525","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}