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Found 3 papers, 1 papers with code
GANeRF: Leveraging Discriminators to Optimize Neural Radiance Fields
Neural Radiance Fields (NeRF) have shown impressive novel view synthesis results; nonetheless, even thorough recordings yield imperfections in reconstructions, for instance due to poorly observed areas or minor lighting changes.
End2End Multi-View Feature Matching with Differentiable Pose Optimization
Training feature matching with gradients from pose optimization naturally learns to down-weight outliers and boosts pose estimation on image pairs compared to SuperGlue by 6. 7% on ScanNet.
Dense Depth Priors for Neural Radiance Fields from Sparse Input Views
To this end, we leverage dense depth priors in order to constrain the NeRF optimization.
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