# fNeRF: High Quality Radiance Fields from Practical Cameras

arXiv: https://arxiv.org/abs/2406.10633 · HTML: https://mlsanity.com/paper/2406.10633/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 25 references that are in the map.
- First posted on arXiv 15 Jun 2024.

## Builds on (most cited first, 11 of 11)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction](https://mlsanity.com/paper/2106.10689/) (2021)
- [BARF: Bundle-Adjusting Neural Radiance Fields](https://mlsanity.com/paper/2104.06405/) (2021)
- [Self-Calibrating Neural Radiance Fields](https://mlsanity.com/paper/2108.13826/) (2021)
- [Deblur-NeRF: Neural Radiance Fields from Blurry Images](https://mlsanity.com/paper/2111.14292/) (2021)
- [DP-NeRF: Deblurred Neural Radiance Field with Physical Scene Priors](https://mlsanity.com/paper/2211.12046/) (2022)
- [Sharp-NeRF: Grid-based Fast Deblurring Neural Radiance Fields Using Sharpness Prior](https://mlsanity.com/paper/2401.00825/) (2024)
- [Inverting the Imaging Process by Learning an Implicit Camera Model](https://mlsanity.com/paper/2304.12748/) (2023)
- [Neural Assets: Volumetric Object Capture and Rendering for Interactive Environments](https://mlsanity.com/paper/2212.06125/) (2022)

## Built on by (most cited first, 1 of 1)

- [Revisiting 3D Reconstruction Kernels as Low-Pass Filters](https://mlsanity.com/paper/2601.17900/) (2026)

## Abstract (arXiv)

In recent years, the development of Neural Radiance Fields has enabled a previously unseen level of photo-realistic 3D reconstruction of scenes and objects from multi-view camera data. However, previous methods use an oversimplified pinhole camera model resulting in defocus blur being `baked' into the reconstructed radiance field. We propose a modification to the ray casting that leverages the optics of lenses to enhance scene reconstruction in the presence of defocus blur. This allows us to improve the quality of radiance field reconstructions from the measurements of a practical camera with finite aperture. We show that the proposed model matches the defocus blur behavior of practical cameras more closely than pinhole models and other approximations of defocus blur models, particularly in the presence of partial occlusions. This allows us to achieve sharper reconstructions, improving the PSNR on validation of all-in-focus images, on both synthetic and real datasets, by up to 3 dB.