Endo-NeRF++ augments EndoNeRF with multi-resolution hash grids, temporal feature blending, and Monte Carlo dropout uncertainty sampling, reporting up to 1.22 dB PSNR, 5.3% SSIM, and 55.1% LPIPS gains over the EndoNeRF baseline on two surgical video sequences.
In: CVPR (2024)
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Endo-NeRF++: Uncertainty-Aware Neural Rendering with Multi-Resolution Hash Encoding for Dynamic Surgical Scene Reconstruction
Endo-NeRF++ augments EndoNeRF with multi-resolution hash grids, temporal feature blending, and Monte Carlo dropout uncertainty sampling, reporting up to 1.22 dB PSNR, 5.3% SSIM, and 55.1% LPIPS gains over the EndoNeRF baseline on two surgical video sequences.