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Related Experiment Video

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Topographical Estimation of Visual Population Receptive Fields by fMRI

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UC-NeRF: Uncertainty-Aware Conditional Neural Radiance Fields From Endoscopic Sparse Views.

Jiaxin Guo, Jiangliu Wang, Ruofeng Wei

IEEE Transactions on Medical Imaging

|November 12, 2024

Summary

This summary is machine-generated.

This study introduces UC-NeRF, a novel method for synthesizing new views of surgical scenes from sparse endoscopic images. It effectively addresses shape-radiance ambiguity and photometric inconsistencies, improving visualization for minimally invasive procedures.

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Area of Science:

Computer Vision
Medical Imaging
Surgical Technology

Background:

Minimally invasive surgery requires clear visualization of internal anatomy.
Novel View Synthesis (NVS) techniques like Neural Radiance Fields (NeRF) reconstruct 3D scenes but struggle with sparse endoscopic views and lighting variations common in surgery.
Existing NeRF methods fail to adequately address the challenges of shape-radiance ambiguity and photometric inconsistencies in surgical data.

Purpose of the Study:

To develop an uncertainty-aware conditional NeRF (UC-NeRF) for improved novel view synthesis in surgical scenes.
To tackle the severe shape-radiance ambiguity arising from sparse surgical views.
To adaptively model and mitigate significant photometric inconsistencies inherent in endoscopic data.

Main Methods:

Proposed UC-NeRF incorporates multi-view uncertainty estimation to condition the neural radiance field.
A consistency learner (multi-view stereo network) establishes geometric correspondence and generates uncertainty/feature priors from sparse views.
A base-adaptive NeRF network leverages uncertainty estimation for handling photometric inconsistencies, complemented by uncertainty-guided geometry distillation.

Main Results:

UC-NeRF demonstrates superior performance in both appearance and geometry rendering compared to state-of-the-art methods.
Experiments on the SCARED and Hamlyn datasets validate the effectiveness of the proposed approach.
The method successfully addresses shape-radiance ambiguity and photometric inconsistencies in surgical NVS.

Conclusions:

UC-NeRF offers a robust solution for novel view synthesis in challenging surgical environments.
The uncertainty-aware approach significantly enhances the quality and reliability of reconstructed surgical scenes.
This work advances the potential of NVS for improving surgical planning and decision-making through better visualization.