Hybrid NeRF-Stereo Vision: Pioneering Depth Estimation and 3D Reconstruction in Endoscopy
<|reference_start|>Hybrid NeRF-Stereo Vision: Pioneering Depth Estimation and 3D Reconstruction in Endoscopy: The 3D reconstruction of the surgical field in minimally invasive endoscopic surgery has posed a formidable challenge when using conventional monocular endoscopes. Existing 3D reconstruction methodologies are frequently encumbered by suboptimal accuracy and limited generalization capabilities. In this study, we introduce an innovative pipeline using Neural Radiance Fields (NeRF) for 3D reconstruction. Our approach utilizes a preliminary NeRF reconstruction that yields a coarse model, then creates a binocular scene within the reconstructed environment, which derives an initial depth map via stereo vision. This initial depth map serves as depth supervision for subsequent NeRF iterations, progressively refining the 3D reconstruction with enhanced accuracy. The binocular depth is iteratively recalculated, with the refinement process continuing until the depth map converges, and exhibits negligible variations. Through this recursive process, high-fidelity depth maps are generated from monocular endoscopic video of a realistic cranial phantom. By repeated measures of the final 3D reconstruction compared to X-ray computed tomography, all differences of relevant clinical distances result in sub-millimeter accuracy.<|reference_end|>
@article{chen2024hybrid,
    title={Hybrid NeRF-Stereo Vision: Pioneering Depth Estimation and 3D
  Reconstruction in Endoscopy},
    author={Pengcheng Chen, Wenhao Li, Nicole Gunderson, Jeremy Ruthberg, Randall
  Bly, Waleed M. Abuzeid, Zhenglong Sun, Eric J. Seibel},
    journal={arXiv preprint arXiv:2410.04041},
    year={2024},
    archivePrefix={arXiv},
    eprint={2410.04041},
    primaryClass={eess.IV cs.CV}
}