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We propose a new approach to 3D reconstruction from sequences of imagesacquired by monocular endoscopes. It is based on two key insights. First,endoluminal cavities are watertight, a property naturally enforced by modelingthem in terms of a signed distance function. Second, the scene illumination isvariable. It comes from the endoscope's light sources and decays with theinverse of the squared distance to the surface. To exploit these insights, webuild on NeuS, a neural implicit surface reconstruction technique with anoutstanding capability to learn appearance and a SDF surface model frommultiple views, but currently limited to scenes with static illumination. Toremove this limitation and exploit the relation between pixel brightness anddepth, we modify the NeuS architecture to explicitly account for it andintroduce a calibrated photometric model of the endoscope's camera and lightsource. Our method is the first one to produce watertight reconstructions ofwhole colon sections. We demonstrate excellent accuracy on phantom imagery.Remarkably, the watertight prior combined with illumination decline, allows tocomplete the reconstruction of unseen portions of the surface with acceptableaccuracy, paving the way to automatic quality assessment of cancer screeningexplorations, measuring the global percentage of observed mucosa.

LightNeuS: Neural Surface Reconstruction in Endoscopy using Illumination Decline