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Related Experiment Video

Updated: Sep 14, 2025

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FLex: joint pose and dynamic radiance fields optimization for stereo endoscopic videos.

Florian Stilz1, Mert Karaoglu2,3, Felix Tristram2

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|July 21, 2025
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Summary

This study introduces flow-optimized local hexplanes (FLex) for reconstructing moving stereo endoscope scenes. FLex enables accurate surgical scene reconstruction and camera pose estimation without external tracking, improving medical applications.

Keywords:
3D ReconstructionCamera pose optimizationNeural renderingRobotic surgery

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Area of Science:

  • Medical imaging
  • Computer vision
  • Surgical technology

Background:

  • Endoscopic scene reconstruction is vital for medical training and analysis.
  • Current methods struggle with dynamic environments, limited deformation, and reliance on external tracking.

Purpose of the Study:

  • To develop a novel approach for reconstructing moving stereo endoscope scenes in dynamic environments.
  • To overcome limitations of existing methods by enabling accurate reconstruction and camera pose estimation without external tracking.

Main Methods:

  • Introduced flow-optimized local hexplanes (FLex).
  • Implicitly separates scenes into overlapping 4D neural radiance fields (NeRFs).
  • Employs progressive optimization for joint reconstruction and camera pose estimation from scratch.

Main Results:

  • Tested on sequences up to 5000 frames, five times longer than previous methods.
  • Achieved highly detailed reconstruction for significantly longer surgical videos.
  • Eliminated the need for external tracking information.

Conclusions:

  • FLex enables accurate reconstruction and camera pose estimation for moving stereo endoscopes in challenging surgical scenes.
  • Advances applicability of neural rendering for improved surgical scene understanding.
  • Code and data will be released.