# Re-ReND: Real-time Rendering of NeRFs across Devices arXiv: https://arxiv.org/abs/2303.08717 ยท HTML: https://mlsanity.com/paper/2303.08717/ ## Key facts - Cited by 17 papers in the map (data through 14 Oct 2026). - Builds on 27 of its 40 references that are in the map. - First posted on arXiv 15 Mar 2023. ## Builds on (most cited first, 27 of 27) - [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015) - [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019) - [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018) - [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) - [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) - [TensoRF: Tensorial Radiance Fields](https://mlsanity.com/paper/2203.09517/) (2022) - [Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction](https://mlsanity.com/paper/2111.11215/) (2021) - [PlenOctrees for Real-time Rendering of Neural Radiance Fields](https://mlsanity.com/paper/2103.14024/) (2021) - [NeRF++: Analyzing and Improving Neural Radiance Fields](https://mlsanity.com/paper/2010.07492/) (2020) - [Depth-supervised NeRF: Fewer Views and Faster Training for Free](https://mlsanity.com/paper/2107.02791/) (2021) - [Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision](https://mlsanity.com/paper/1912.07372/) (2019) - [KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs](https://mlsanity.com/paper/2103.13744/) (2021) - [FastNeRF: High-Fidelity Neural Rendering at 200FPS](https://mlsanity.com/paper/2103.10380/) (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) - [State of the Art on Neural Rendering](https://mlsanity.com/paper/2004.03805/) (2020) - [DONeRF: Towards Real-Time Rendering of Compact Neural Radiance Fields using Depth Oracle Networks](https://mlsanity.com/paper/2103.03231/) (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) - [Differentiable Rendering: A Survey](https://mlsanity.com/paper/2006.12057/) (2020) - [Light Field Neural Rendering](https://mlsanity.com/paper/2112.09687/) (2021) - [R2L: Distilling Neural Radiance Field to Neural Light Field for Efficient Novel View Synthesis](https://mlsanity.com/paper/2203.17261/) (2022) - [Differentiable Surface Rendering via Non-Differentiable Sampling](https://mlsanity.com/paper/2108.04886/) (2021) ## Built on by (most cited first, 17 of 17) - [SMERF: Streamable Memory Efficient Radiance Fields for Real-Time Large-Scene Exploration](https://mlsanity.com/paper/2312.07541/) (2023) - [EVER: Exact Volumetric Ellipsoid Rendering for Real-time View Synthesis](https://mlsanity.com/paper/2410.01804/) (2024) - [VideoRF: Rendering Dynamic Radiance Fields as 2D Feature Video Streams](https://mlsanity.com/paper/2312.01407/) (2023) - [HPC: Hierarchical Progressive Coding Framework for Volumetric Video](https://mlsanity.com/paper/2407.09026/) (2024) - [Neural Radiance Field-based Visual Rendering: A Comprehensive Review](https://mlsanity.com/paper/2404.00714/) (2024) - [LightSpeed: Light and Fast Neural Light Fields on Mobile Devices](https://mlsanity.com/paper/2310.16832/) (2023) - [GS-Cache: A GS-Cache Inference Framework for Large-scale Gaussian Splatting Models](https://mlsanity.com/paper/2502.14938/) (2025) - [DATENeRF: Depth-Aware Text-based Editing of NeRFs](https://mlsanity.com/paper/2404.04526/) (2024) - [Gaussian Control with Hierarchical Semantic Graphs in 3D Human Recovery](https://mlsanity.com/paper/2405.12477/) (2024) - [NGP-RT: Fusing Multi-Level Hash Features with Lightweight Attention for Real-Time Novel View Synthesis](https://mlsanity.com/paper/2407.10482/) (2024) - [Plenoptic PNG: Real-Time Neural Radiance Fields in 150 KB](https://mlsanity.com/paper/2409.15689/) (2024) - [PocketGS: On-Device Training of 3D Gaussian Splatting for High Perceptual Modeling](https://mlsanity.com/paper/2601.17354/) (2026) - [SplitNeRF: Split Sum Approximation Neural Field for Joint Geometry, Illumination, and Material Estimation](https://mlsanity.com/paper/2311.16671/) (2023) - [fMPI: Fast Novel View Synthesis in the Wild with Layered Scene Representations](https://mlsanity.com/paper/2312.16109/) (2023) - [Baking Relightable NeRF for Real-time Direct/Indirect Illumination Rendering](https://mlsanity.com/paper/2409.10327/) (2024) - [NeRFlex: Resource-aware Real-time High-quality Rendering of Complex Scenes on Mobile Devices](https://mlsanity.com/paper/2504.03415/) (2025) - [EvaSurf: Efficient View-Aware Implicit Textured Surface Reconstruction](https://mlsanity.com/paper/2311.09806/) (2023) ## Abstract (arXiv) This paper proposes a novel approach for rendering a pre-trained Neural Radiance Field (NeRF) in real-time on resource-constrained devices. We introduce Re-ReND, a method enabling Real-time Rendering of NeRFs across Devices. Re-ReND is designed to achieve real-time performance by converting the NeRF into a representation that can be efficiently processed by standard graphics pipelines. The proposed method distills the NeRF by extracting the learned density into a mesh, while the learned color information is factorized into a set of matrices that represent the scene's light field. Factorization implies the field is queried via inexpensive MLP-free matrix multiplications, while using a light field allows rendering a pixel by querying the field a single time-as opposed to hundreds of queries when employing a radiance field. Since the proposed representation can be implemented using a fragment shader, it can be directly integrated with standard rasterization frameworks. Our flexible implementation can render a NeRF in real-time with low memory requirements and on a wide range of resource-constrained devices, including mobiles and AR/VR headsets. Notably, we find that Re-ReND can achieve over a 2.6-fold increase in rendering speed versus the state-of-the-art without perceptible losses in quality.