# Complex-Valued Holographic Radiance Fields

arXiv: https://arxiv.org/abs/2506.08350 · HTML: https://mlsanity.com/paper/2506.08350/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 64 references that are in the map.
- First posted on arXiv 10 Jun 2025.

## Builds on (most cited first, 26 of 26)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [SGDR: Stochastic Gradient Descent with Warm Restarts](https://mlsanity.com/paper/1608.03983/) (2016)
- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [2D Gaussian Splatting for Geometrically Accurate Radiance Fields](https://mlsanity.com/paper/2403.17888/) (2024)
- [Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines](https://mlsanity.com/paper/1905.00889/) (2019)
- [Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets](https://mlsanity.com/paper/1903.05662/) (2019)
- [Free View Synthesis](https://mlsanity.com/paper/2008.05511/) (2020)
- [Taming 3DGS: High-Quality Radiance Fields with Limited Resources](https://mlsanity.com/paper/2406.15643/) (2024)
- [Adan: Adaptive Nesterov Momentum Algorithm for Faster Optimizing Deep Models](https://mlsanity.com/paper/2208.06677/) (2022)
- [DeRF: Decomposed Radiance Fields](https://mlsanity.com/paper/2011.12490/) (2020)
- [RadSplat: Radiance Field-Informed Gaussian Splatting for Robust Real-Time Rendering with 900+ FPS](https://mlsanity.com/paper/2403.13806/) (2024)
- [GaussianImage: 1000 FPS Image Representation and Compression by 2D Gaussian Splatting](https://mlsanity.com/paper/2403.08551/) (2024)
- [SMERF: Streamable Memory Efficient Radiance Fields for Real-Time Large-Scene Exploration](https://mlsanity.com/paper/2312.07541/) (2023)
- [FreeTimeGS: Free Gaussian Primitives at Anytime and Anywhere for Dynamic Scene Reconstruction](https://mlsanity.com/paper/2506.05348/) (2025)
- [3D Scene Generation: A Survey](https://mlsanity.com/paper/2505.05474/) (2025)
- [Neural Spectro-polarimetric Fields](https://mlsanity.com/paper/2306.12562/) (2023)
- [Multi-color Holograms Improve Brightness in Holographic Displays](https://mlsanity.com/paper/2301.09950/) (2023)
- [Holographic Parallax Improves 3D Perceptual Realism](https://mlsanity.com/paper/2404.11810/) (2024)
- [Pupil-aware Holography](https://mlsanity.com/paper/2203.14939/) (2022)
- [Gaussian Wave Splatting for Computer-Generated Holography](https://mlsanity.com/paper/2505.06582/) (2025)
- [Stochastic Light Field Holography](https://mlsanity.com/paper/2307.06277/) (2023)
- [Large 'Etendue 3D Holographic Display with Content-adaptive Dynamic Fourier Modulation](https://mlsanity.com/paper/2409.03143/) (2024)
- [Pupil-Adaptive 3D Holography Beyond Coherent Depth-of-Field](https://mlsanity.com/paper/2409.00028/) (2024)
- [Focal Surface Holographic Light Transport using Learned Spatially Adaptive Convolutions](https://mlsanity.com/paper/2410.06854/) (2024)

## Built on by (most cited first, 2 of 2)

- [Complex-Valued 2D Gaussian Representation for Computer-Generated Holography](https://mlsanity.com/paper/2511.15022/) (2025)
- [A Large-Depth-Range Layer-Based Hologram Dataset for Machine Learning-Based 3D Computer-Generated Holography](https://mlsanity.com/paper/2512.21040/) (2025)

## Abstract (arXiv)

Modeling wave properties of light is an important milestone for advancing physically-based rendering. In this paper, we propose complex-valued holographic radiance fields, a method that optimizes scenes without relying on intensity-based intermediaries. By leveraging multi-view images, our method directly optimizes a scene representation using complex-valued Gaussian primitives representing amplitude and phase values aligned with the scene geometry. Our approach eliminates the need for computationally expensive holographic rendering that typically utilizes a single view of a given scene. This accelerates holographic rendering speed by 30x-10,000x while achieving on-par image quality with state-of-the-art holography methods, representing a promising step towards bridging the representation gap between modeling wave properties of light and 3D geometry of scenes.