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Computer Science > Computer Vision and Pattern Recognition

arXiv:2403.12198 (cs)
[Submitted on 18 Mar 2024]

Title:FLex: Joint Pose and Dynamic Radiance Fields Optimization for Stereo Endoscopic Videos

Authors:Florian Philipp Stilz, Mert Asim Karaoglu, Felix Tristram, Nassir Navab, Benjamin Busam, Alexander Ladikos
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Abstract: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.
Subjects: Computer Vision and Pattern Recognition (cs.CV); Graphics (cs.GR); Machine Learning (cs.LG)
Cite as: arXiv:2403.12198 [cs.CV]
  (or arXiv:2403.12198v1 [cs.CV] for this version)
  https://doi.org/10.48550/arXiv.2403.12198

Submission history

From: Felix Tristram [view email]
[v1] Mon, 18 Mar 2024 19:13:02 UTC (13,200 KB)
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