  Reconstruction of endoscopic scenes is an important asset for various medical
applications, from post-surgery analysis to educational training. Neural
rendering has recently shown promising results in endoscopic reconstruction
with deforming tissue. However, the setup has been restricted to a static
endoscope, limited deformation, or required an external tracking device to
retrieve camera pose information of the endoscopic camera. With FLex we adress
the challenging setup of a moving endoscope within a highly dynamic environment
of deforming tissue. We propose an implicit scene separation into multiple
overlapping 4D neural radiance fields (NeRFs) and a progressive optimization
scheme jointly optimizing for reconstruction and camera poses from scratch.
This improves the ease-of-use and allows to scale reconstruction capabilities
in time to process surgical videos of 5,000 frames and more; an improvement of
more than ten times compared to the state of the art while being agnostic to
external tracking information. Extensive evaluations on the StereoMIS dataset
show that FLex significantly improves the quality of novel view synthesis while
maintaining competitive pose accuracy.
