  In surgical oncology, screening colonoscopy plays a pivotal role in providing
diagnostic assistance, such as biopsy, and facilitating surgical navigation,
particularly in polyp detection. Computer-assisted endoscopic surgery has
recently gained attention and amalgamated various 3D computer vision
techniques, including camera localization, depth estimation, surface
reconstruction, etc. Neural Radiance Fields (NeRFs) and Neural Implicit
Surfaces (NeuS) have emerged as promising methodologies for deriving accurate
3D surface models from sets of registered images, addressing the limitations of
existing colon reconstruction approaches stemming from constrained camera
movement.
  However, the inadequate tissue texture representation and confused scale
problem in monocular colonoscopic image reconstruction still impede the
progress of the final rendering results. In this paper, we introduce a novel
method for colon section reconstruction by leveraging NeuS applied to
endoscopic images, supplemented by a single frame of depth map. Notably, we
pioneered the exploration of utilizing only one frame depth map in
photorealistic reconstruction and neural rendering applications while this
single depth map can be easily obtainable from other monocular depth estimation
networks with an object scale. Through rigorous experimentation and validation
on phantom imagery, our approach demonstrates exceptional accuracy in
completely rendering colon sections, even capturing unseen portions of the
surface. This breakthrough opens avenues for achieving stable and consistently
scaled reconstructions, promising enhanced quality in cancer screening
procedures and treatment interventions.
