Towards Abdominal 3-D Scene Rendering from Laparoscopy Surgical Videos
  using NeRFs
Khoa Tuan Nguyen Francesca Tozzi Nikdokht Rashidian Wouter Willaert Joris Vankerschaver Wesley De Neve
Given that a conventional laparoscope only provides a two-dimensional (2-D)
view, the detection and diagnosis of medical ailments can be challenging. To
overcome the visual constraints associated with laparoscopy, the use of
laparoscopic images and videos to reconstruct the three-dimensional (3-D)
anatomical structure of the abdomen has proven to be a promising approach.
Neural Radiance Fields (NeRFs) have recently gained attention thanks to their
ability to generate photorealistic images from a 3-D static scene, thus
facilitating a more comprehensive exploration of the abdomen through the
synthesis of new views. This distinguishes NeRFs from alternative methods such
as Simultaneous Localization and Mapping (SLAM) and depth estimation. In this
paper, we present a comprehensive examination of NeRFs in the context of
laparoscopy surgical videos, with the goal of rendering abdominal scenes in
3-D. Although our experimental results are promising, the proposed approach
encounters substantial challenges, which require further exploration in future
research.