# Real-Time Neural Light Field on Mobile Devices

arXiv: https://arxiv.org/abs/2212.08057 · HTML: https://mlsanity.com/paper/2212.08057/

## Key facts

- Cited by 48 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 46 references that are in the map.
- First posted on arXiv 15 Dec 2022.

## Builds on (most cited first, 30 of 31)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Mip-NeRF 360: Unbounded Anti-Aliased Neural Radiance Fields](https://mlsanity.com/paper/2111.12077/) (2021)
- [Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2103.13415/) (2021)
- [Plenoxels: Radiance Fields without Neural Networks](https://mlsanity.com/paper/2112.05131/) (2021)
- [Efficient Geometry-aware 3D Generative Adversarial Networks](https://mlsanity.com/paper/2112.07945/) (2021)
- [Neural Sparse Voxel Fields](https://mlsanity.com/paper/2007.11571/) (2020)
- [PlenOctrees for Real-time Rendering of Neural Radiance Fields](https://mlsanity.com/paper/2103.14024/) (2021)
- [KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs](https://mlsanity.com/paper/2103.13744/) (2021)
- [Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines](https://mlsanity.com/paper/1905.00889/) (2019)
- [Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields](https://mlsanity.com/paper/2112.03907/) (2021)
- [FastNeRF: High-Fidelity Neural Rendering at 200FPS](https://mlsanity.com/paper/2103.10380/) (2021)
- [StyleNeRF: A Style-based 3D-Aware Generator for High-resolution Image Synthesis](https://mlsanity.com/paper/2110.08985/) (2021)
- [Baking Neural Radiance Fields for Real-Time View Synthesis](https://mlsanity.com/paper/2103.14645/) (2021)
- [Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering](https://mlsanity.com/paper/2106.02634/) (2021)
- [MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures](https://mlsanity.com/paper/2208.00277/) (2022)
- [EfficientFormer: Vision Transformers at MobileNet Speed](https://mlsanity.com/paper/2206.01191/) (2022)
- [DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks](https://mlsanity.com/paper/2103.03231/) (2021)
- [HeadNeRF: A Real-time NeRF-based Parametric Head Model](https://mlsanity.com/paper/2112.05637/) (2021)
- [NeX: Real-time View Synthesis with Neural Basis Expansion](https://mlsanity.com/paper/2103.05606/) (2021)
- [AutoInt: Automatic Integration for Fast Neural Volume Rendering](https://mlsanity.com/paper/2012.01714/) (2020)
- [DeRF: Decomposed Radiance Fields](https://mlsanity.com/paper/2011.12490/) (2020)
- [R2L: Distilling Neural Radiance Field to Neural Light Field for Efficient Novel View Synthesis](https://mlsanity.com/paper/2203.17261/) (2022)
- [Learning Neural Light Fields with Ray-Space Embedding Networks](https://mlsanity.com/paper/2112.01523/) (2021)
- [NeuLF: Efficient Novel View Synthesis with Neural 4D Light Field](https://mlsanity.com/paper/2105.07112/) (2021)

## Built on by (most cited first, 30 of 48)

- [MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes](https://mlsanity.com/paper/2302.12249/) (2023)
- [VastGaussian: Vast 3D Gaussians for Large Scene Reconstruction](https://mlsanity.com/paper/2402.17427/) (2024)
- [Delicate Textured Mesh Recovery from NeRF via Adaptive Surface Refinement](https://mlsanity.com/paper/2303.02091/) (2023)
- [Adaptive Shells for Efficient Neural Radiance Field Rendering](https://mlsanity.com/paper/2311.10091/) (2023)
- [SMERF: Streamable Memory Efficient Radiance Fields for Real-Time Large-Scene Exploration](https://mlsanity.com/paper/2312.07541/) (2023)
- [SPAD : Spatially Aware Multiview Diffusers](https://mlsanity.com/paper/2402.05235/) (2024)
- [Masked Space-Time Hash Encoding for Efficient Dynamic Scene Reconstruction](https://mlsanity.com/paper/2310.17527/) (2023)
- [Vidu4D: Single Generated Video to High-Fidelity 4D Reconstruction with Dynamic Gaussian Surfels](https://mlsanity.com/paper/2405.16822/) (2024)
- [CoGS: Controllable Gaussian Splatting](https://mlsanity.com/paper/2312.05664/) (2023)
- [V^3: Viewing Volumetric Videos on Mobiles via Streamable 2D Dynamic Gaussians](https://mlsanity.com/paper/2409.13648/) (2024)
- [MirrorGaussian: Reflecting 3D Gaussians for Reconstructing Mirror Reflections](https://mlsanity.com/paper/2405.11921/) (2024)
- [LiteVGGT: Boosting Vanilla VGGT via Geometry-aware Cached Token Merging](https://mlsanity.com/paper/2512.04939/) (2025)
- [City-on-Web: Real-time Neural Rendering of Large-scale Scenes on the Web](https://mlsanity.com/paper/2312.16457/) (2023)
- [RayDF: Neural Ray-surface Distance Fields with Multi-view Consistency](https://mlsanity.com/paper/2310.19629/) (2023)
- [FastSR-NeRF: Improving NeRF Efficiency on Consumer Devices with A Simple Super-Resolution Pipeline](https://mlsanity.com/paper/2312.11537/) (2023)
- [Efficient Ray Sampling for Radiance Fields Reconstruction](https://mlsanity.com/paper/2308.15547/) (2023)
- [LightSpeed: Light and Fast Neural Light Fields on Mobile Devices](https://mlsanity.com/paper/2310.16832/) (2023)
- [LiveNVS: Neural View Synthesis on Live RGB-D Streams](https://mlsanity.com/paper/2311.16668/) (2023)
- [Lightweight Predictive 3D Gaussian Splats](https://mlsanity.com/paper/2406.19434/) (2024)
- [ULSR-GS: Ultra Large-scale Surface Reconstruction Gaussian Splatting with Multi-View Geometric Consistency](https://mlsanity.com/paper/2412.01402/) (2024)
- [A Real-time Method for Inserting Virtual Objects into Neural Radiance Fields](https://mlsanity.com/paper/2310.05837/) (2023)
- [Expansive Supervision for Neural Radiance Field](https://mlsanity.com/paper/2409.08056/) (2024)
- [Video4DGen: Enhancing Video and 4D Generation through Mutual Optimization](https://mlsanity.com/paper/2504.04153/) (2025)
- [Neural Rendering and Its Hardware Acceleration: A Review](https://mlsanity.com/paper/2402.00028/) (2024)
- [DecentNeRFs: Decentralized Neural Radiance Fields from Crowdsourced Images](https://mlsanity.com/paper/2403.13199/) (2024)
- [Niagara: Normal-Integrated Geometric Affine Field for Scene Reconstruction from a Single View](https://mlsanity.com/paper/2503.12553/) (2025)
- [Progressive Volume Distillation with Active Learning for Efficient NeRF Architecture Conversion](https://mlsanity.com/paper/2304.04012/) (2023)
- [MixRT: Mixed Neural Representations For Real-Time NeRF Rendering](https://mlsanity.com/paper/2312.11841/) (2023)
- [EG-HumanNeRF: Efficient Generalizable Human NeRF Utilizing Human Prior for Sparse View](https://mlsanity.com/paper/2410.12242/) (2024)
- [A General Implicit Framework for Fast NeRF Composition and Rendering](https://mlsanity.com/paper/2308.04669/) (2023)

## Abstract (arXiv)

Recent efforts in Neural Rendering Fields (NeRF) have shown impressive results on novel view synthesis by utilizing implicit neural representation to represent 3D scenes. Due to the process of volumetric rendering, the inference speed for NeRF is extremely slow, limiting the application scenarios of utilizing NeRF on resource-constrained hardware, such as mobile devices. Many works have been conducted to reduce the latency of running NeRF models. However, most of them still require high-end GPU for acceleration or extra storage memory, which is all unavailable on mobile devices. Another emerging direction utilizes the neural light field (NeLF) for speedup, as only one forward pass is performed on a ray to predict the pixel color. Nevertheless, to reach a similar rendering quality as NeRF, the network in NeLF is designed with intensive computation, which is not mobile-friendly. In this work, we propose an efficient network that runs in real-time on mobile devices for neural rendering. We follow the setting of NeLF to train our network. Unlike existing works, we introduce a novel network architecture that runs efficiently on mobile devices with low latency and small size, i.e., saving 15× ~ 24× storage compared with MobileNeRF. Our model achieves high-resolution generation while maintaining real-time inference for both synthetic and real-world scenes on mobile devices, e.g., 18.04ms (iPhone 13) for rendering one 1008×756 image of real 3D scenes. Additionally, we achieve similar image quality as NeRF and better quality than MobileNeRF (PSNR 26.15 vs. 25.91 on the real-world forward-facing dataset).