000126502 001__ 126502 000126502 005__ 20230706095712.0 000126502 0247_ $$2doi$$a10.1109/IROS47612.2022.9981742 000126502 0248_ $$2sideral$$a132467 000126502 037__ $$aART-2022-132467 000126502 041__ $$aeng 000126502 100__ $$0(orcid)0000-0002-6837-934X$$aMartínez Batlle, V.$$uUniversidad de Zaragoza 000126502 245__ $$aPhotometric single-view dense 3D reconstruction in endoscopy 000126502 260__ $$c2022 000126502 5060_ $$aAccess copy available to the general public$$fUnrestricted 000126502 5203_ $$aVisual SLAM inside the human body will open the way to computer-assisted navigation in endoscopy. However, due to space limitations, medical endoscopes only provide monocular images, leading to systems lacking true scale. In this paper, we exploit the controlled lighting in colonoscopy to achieve the first in-vivo 3D reconstruction of the human colon using photometric stereo on a calibrated monocular endoscope. Our method works in a real medical environment, providing both a suitable in-place calibration procedure and a depth estimation technique adapted to the colon's tubular geometry. We validate our method on simulated colonoscopies, obtaining a mean error of 7% on depth estimation, which is below 3 mm on average. Our qualitative results on the EndoMapper dataset show that the method is able to correctly estimate the colon shape in real human colonoscopies, paving the ground for truescale monocular SLAM in endoscopy. 000126502 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T45-17R$$9info:eu-repo/grantAgreement/EC/H2020/863146/EU/EndoMapper: Real-time mapping from endoscopic video/EndoMapper$$9This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No H2020 863146-EndoMapper$$9info:eu-repo/grantAgreement/ES/MCIU-AEI-FEDER/PGC2018-096367-B-I00 000126502 540__ $$9info:eu-repo/semantics/openAccess$$aAll rights reserved$$uhttp://www.europeana.eu/rights/rr-f/ 000126502 592__ $$a0.853$$b2022 000126502 593__ $$aComputer Science Applications$$c2022 000126502 593__ $$aSoftware$$c2022 000126502 593__ $$aControl and Systems Engineering$$c2022 000126502 593__ $$aComputer Vision and Pattern Recognition$$c2022 000126502 655_4 $$ainfo:eu-repo/semantics/conferenceObject$$vinfo:eu-repo/semantics/acceptedVersion 000126502 700__ $$0(orcid)0000-0002-3627-7306$$aMartínez Montiel, J. M.$$uUniversidad de Zaragoza 000126502 700__ $$0(orcid)0000-0002-4518-5876$$aTardos, J. D.$$uUniversidad de Zaragoza 000126502 7102_ $$15007$$2520$$aUniversidad de Zaragoza$$bDpto. Informát.Ingenie.Sistms.$$cÁrea Ingen.Sistemas y Automát. 000126502 773__ $$g2022 (2022), 4904-4910$$pProc. IEEE/RSJ Int. Conf. Intell. Rob. Syst.$$tProceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems$$x2153-0858 000126502 8564_ $$s2520496$$uhttps://zaguan.unizar.es/record/126502/files/texto_completo.pdf$$yPostprint 000126502 8564_ $$s2909528$$uhttps://zaguan.unizar.es/record/126502/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint 000126502 909CO $$ooai:zaguan.unizar.es:126502$$particulos$$pdriver 000126502 951__ $$a2023-07-06-07:58:16 000126502 980__ $$aARTICLE