168093 20260126155509.0 doi 10.1007/978-3-031-43999-5_48 sideral 147676 ART-2023-147676 eng Batlle, Víctor M. Universidad de Zaragoza (orcid)0000-0002-6837-934X LightNeuS: Neural Surface Reconstruction in Endoscopy Using Illumination Decline 2023 We propose a new approach to 3D reconstruction from sequences of images acquired by monocular endoscopes. It is based on two key insights. First, endoluminal cavities are watertight, a property naturally enforced by modeling them in terms of a signed distance function. Second, the scene illumination is variable. It comes from the endoscope’s light sources and decays with the inverse of the squared distance to the surface. To exploit these insights, we build on NeuS [25], a neural implicit surface reconstruction technique with an outstanding capability to learn appearance and a SDF surface model from multiple views, but currently limited to scenes with static illumination. To remove this limitation and exploit the relation between pixel brightness and depth, we modify the NeuS architecture to explicitly account for it and introduce a calibrated photometric model of the endoscope’s camera and light source. Our method is the first one to produce watertight reconstructions of whole colon sections. We demonstrate excellent accuracy on phantom imagery. Remarkably, the watertight prior combined with illumination decline, allows to complete the reconstruction of unseen portions of the surface with acceptable accuracy, paving the way to automatic quality assessment of cancer screening explorations, measuring the global percentage of observed mucosa. info:eu-repo/semantics/closedAccess All rights reserved http://www.europeana.eu/rights/rr-f/ 0.606 2023 Theoretical Computer Science 2023 Q2 Computer Science (miscellaneous) 2023 Q2 2.6 2023 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Montiel, José M. M. Universidad de Zaragoza (orcid)0000-0002-3627-7306 Fua, Pascal Tardós, Juan D. Universidad de Zaragoza (orcid)0000-0002-4518-5876 5007 520 Universidad de Zaragoza Dpto. Informát.Ingenie.Sistms. Área Ingen.Sistemas y Automát. (2023), 502-512 Lect. notes comput. sci. Lecture Notes in Computer Science 0302-9743 661143 http://zaguan.unizar.es/record/168093/files/texto_completo.pdf Versión publicada 1517359 http://zaguan.unizar.es/record/168093/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:168093 articulos driver 2026-01-26-14:50:21 ARTICLE