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, 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.

翻译：我们提出了一种基于单目内窥镜获取的图像序列进行三维重建的新方法。该方法基于两个关键洞察：首先，腔内腔体是水密的，这一特性自然地通过符号距离函数建模即可实现；其次，场景光照是变化的，它来自内窥镜光源，并随距离表面的平方倒数衰减。为利用这些洞察，我们以NeuS为基础——一种具备从多视角学习外观和SDF表面模型的卓越能力、但目前仅限于静态光照场景的神经隐式表面重建技术。为突破此限制并利用像素亮度与深度间的关联，我们修改了NeuS架构以显式处理这一关系，并引入内窥镜相机与光源的标定光度模型。本方法是首个能够对完整结肠段进行水密重建的技术。我们在体模图像上展示了卓越的准确性。值得注意的是，水密先验结合光照衰减，使得以可接受的精度完成对未观测表面部分的重建成为了可能，这为癌症筛查探索的自动质量评估铺平了道路，能够测量黏膜的全局观察百分比。