# Hyperspectral Neural Radiance Fields

arXiv: https://arxiv.org/abs/2403.14839 · HTML: https://mlsanity.com/paper/2403.14839/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 30 references that are in the map.
- First posted on arXiv 21 Mar 2024.

## Builds on (most cited first, 16 of 16)

- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://mlsanity.com/paper/2008.02268/) (2020)
- [Nerfstudio: A Modular Framework for Neural Radiance Field Development](https://mlsanity.com/paper/2302.04264/) (2023)
- [Dynamic View Synthesis from Dynamic Monocular Video](https://mlsanity.com/paper/2105.06468/) (2021)
- [In-Place Scene Labelling and Understanding with Implicit Scene Representation](https://mlsanity.com/paper/2103.15875/) (2021)
- [Space-time Neural Irradiance Fields for Free-Viewpoint Video](https://mlsanity.com/paper/2011.12950/) (2020)
- [NeRF in the Dark: High Dynamic Range View Synthesis from Noisy Raw Images](https://mlsanity.com/paper/2111.13679/) (2021)
- [Neural Radiance Flow for 4D View Synthesis and Video Processing](https://mlsanity.com/paper/2012.09790/) (2020)
- [Deblur-NeRF: Neural Radiance Fields from Blurry Images](https://mlsanity.com/paper/2111.14292/) (2021)
- [NAN: Noise-Aware NeRFs for Burst-Denoising](https://mlsanity.com/paper/2204.04668/) (2022)
- [Cross-Spectral Neural Radiance Fields](https://mlsanity.com/paper/2209.00648/) (2022)
- [Implicit Neural Representation Learning for Hyperspectral Image Super-Resolution](https://mlsanity.com/paper/2112.10541/) (2021)
- [Spec-NeRF: Multi-spectral Neural Radiance Fields](https://mlsanity.com/paper/2310.12987/) (2023)
- [SpectralNeRF: Physically Based Spectral Rendering with Neural Radiance Field](https://mlsanity.com/paper/2312.08692/) (2023)
- [AR-NeRF: Unsupervised Learning of Depth and Defocus Effects from Natural Images with Aperture Rendering Neural Radiance Fields](https://mlsanity.com/paper/2206.06100/) (2022)

## Built on by (most cited first, 12 of 12)

- [HyperGS: Hyperspectral 3D Gaussian Splatting](https://mlsanity.com/paper/2412.12849/) (2024)
- [A Survey on 3D Reconstruction Techniques in Plant Phenotyping: From Classical Methods to Neural Radiance Fields (NeRF), 3D Gaussian Splatting (3DGS), and Beyond](https://mlsanity.com/paper/2505.00737/) (2025)
- [UnMix-NeRF: Spectral Unmixing Meets Neural Radiance Fields](https://mlsanity.com/paper/2506.21884/) (2025)
- [Towards Integrating Multi-Spectral Imaging with Gaussian Splatting](https://mlsanity.com/paper/2509.00989/) (2025)
- [GANESH: Generalizable NeRF for Lensless Imaging](https://mlsanity.com/paper/2411.04810/) (2024)
- [SC-NeRF: NeRF-based Point Cloud Reconstruction using a Stationary Camera for Agricultural Applications](https://mlsanity.com/paper/2503.21958/) (2025)
- [Diffusion-Denoised Hyperspectral Gaussian Splatting](https://mlsanity.com/paper/2505.21890/) (2025)
- [HS-3D-NeRF: 3D Surface and Hyperspectral Reconstruction From Stationary Hyperspectral Images Using Multi-Channel NeRFs](https://mlsanity.com/paper/2602.16950/) (2026)
- [LGDWT-GS: Local and Global Discrete Wavelet-Regularized 3D Gaussian Splatting for Sparse-View Scene Reconstruction](https://mlsanity.com/paper/2601.17185/) (2026)
- [Towards 3D Scene Understanding of Gas Plumes in LWIR Hyperspectral Images Using Neural Radiance Fields](https://mlsanity.com/paper/2603.05473/) (2026)
- [3-D Representations for Hyperspectral Flame Tomography](https://mlsanity.com/paper/2603.27832/) (2026)
- [Spectral World Renderer: Unified 3D Hyperspectral Unmixing](https://mlsanity.com/paper/2610.19520/) (2026)

## Abstract (arXiv)

Hyperspectral Imagery (HSI) has been used in many applications to non-destructively determine the material and/or chemical compositions of samples. There is growing interest in creating 3D hyperspectral reconstructions, which could provide both spatial and spectral information while also mitigating common HSI challenges such as non-Lambertian surfaces and translucent objects. However, traditional 3D reconstruction with HSI is difficult due to technological limitations of hyperspectral cameras. In recent years, Neural Radiance Fields (NeRFs) have seen widespread success in creating high quality volumetric 3D representations of scenes captured by a variety of camera models. Leveraging recent advances in NeRFs, we propose computing a hyperspectral 3D reconstruction in which every point in space and view direction is characterized by wavelength-dependent radiance and transmittance spectra. To evaluate our approach, a dataset containing nearly 2000 hyperspectral images across 8 scenes and 2 cameras was collected. We perform comparisons against traditional RGB NeRF baselines and apply ablation testing with alternative spectra representations. Finally, we demonstrate the potential of hyperspectral NeRFs for hyperspectral super-resolution and imaging sensor simulation. We show that our hyperspectral NeRF approach enables creating fast, accurate volumetric 3D hyperspectral scenes and enables several new applications and areas for future study.