# Mip-Grid: Anti-aliased Grid Representations for Neural Radiance Fields arXiv: https://arxiv.org/abs/2402.14196 ยท HTML: https://mlsanity.com/paper/2402.14196/ ## Key facts - Cited by 13 papers in the map (data through 14 Oct 2026). - Builds on 36 of its 45 references that are in the map. - First posted on arXiv 22 Feb 2024. ## Builds on (most cited first, 30 of 36) - [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) - [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022) - [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](https://mlsanity.com/paper/2006.10739/) (2020) - [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) - [Zero-1-to-3: Zero-shot One Image to 3D Object](https://mlsanity.com/paper/2303.11328/) (2023) - [On the Spectral Bias of Neural Networks](https://mlsanity.com/paper/1806.08734/) (2018) - [Magic3D: High-Resolution Text-to-3D Content Creation](https://mlsanity.com/paper/2211.10440/) (2022) - [TensoRF: Tensorial Radiance Fields](https://mlsanity.com/paper/2203.09517/) (2022) - [Efficient Geometry-aware 3D Generative Adversarial Networks](https://mlsanity.com/paper/2112.07945/) (2021) - [Nerfies: Deformable Neural Radiance Fields](https://mlsanity.com/paper/2011.12948/) (2020) - [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) - [K-Planes: Explicit Radiance Fields in Space, Time, and Appearance](https://mlsanity.com/paper/2301.10241/) (2023) - [Block-NeRF: Scalable Large Scene Neural View Synthesis](https://mlsanity.com/paper/2202.05263/) (2022) - [pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesis](https://mlsanity.com/paper/2012.00926/) (2020) - [GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis](https://mlsanity.com/paper/2007.02442/) (2020) - [Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields](https://mlsanity.com/paper/2304.06706/) (2023) - [NICE-SLAM: Neural Implicit Scalable Encoding for SLAM](https://mlsanity.com/paper/2112.12130/) (2021) - [HexPlane: A Fast Representation for Dynamic Scenes](https://mlsanity.com/paper/2301.09632/) (2023) - [iMAP: Implicit Mapping and Positioning in Real-Time](https://mlsanity.com/paper/2103.12352/) (2021) - [Neuralangelo: High-Fidelity Neural Surface Reconstruction](https://mlsanity.com/paper/2306.03092/) (2023) - [RealFusion: 360deg Reconstruction of Any Object from a Single Image](https://mlsanity.com/paper/2302.10663/) (2023) - [DynIBaR: Neural Dynamic Image-Based Rendering](https://mlsanity.com/paper/2211.11082/) (2022) - [BANMo: Building Animatable 3D Neural Models from Many Casual Videos](https://mlsanity.com/paper/2112.12761/) (2021) - [NeRF-SLAM: Real-Time Dense Monocular SLAM with Neural Radiance Fields](https://mlsanity.com/paper/2210.13641/) (2022) - [Fourier PlenOctrees for Dynamic Radiance Field Rendering in Real-time](https://mlsanity.com/paper/2202.08614/) (2022) ## Built on by (most cited first, 13 of 13) - [Compact 3D Gaussian Splatting for Static and Dynamic Radiance Fields](https://mlsanity.com/paper/2408.03822/) (2024) - [PyGS: Large-scale Scene Representation with Pyramidal 3D Gaussian Splatting](https://mlsanity.com/paper/2405.16829/) (2024) - [Hybrid 3D-4D Gaussian Splatting for Fast Dynamic Scene Representation](https://mlsanity.com/paper/2505.13215/) (2025) - [Neural Radiance Field-based Visual Rendering: A Comprehensive Review](https://mlsanity.com/paper/2404.00714/) (2024) - [Mipmap-GS: Let Gaussians Deform with Scale-specific Mipmap for Anti-aliasing Rendering](https://mlsanity.com/paper/2408.06286/) (2024) - [CodecNeRF: Toward Fast Encoding and Decoding, Compact, and High-quality Novel-view Synthesis](https://mlsanity.com/paper/2404.04913/) (2024) - [Holistic Large-Scale Scene Reconstruction via Mixed Gaussian Splatting](https://mlsanity.com/paper/2505.23280/) (2025) - [Can we make NeRF-based visual localization privacy-preserving?](https://mlsanity.com/paper/2508.18971/) (2025) - [LoD-Structured 3D Gaussian Splatting for Streaming Video Reconstruction](https://mlsanity.com/paper/2601.18475/) (2026) - [Physics-Driven Neural Compensation For Electrical Impedance Tomography](https://mlsanity.com/paper/2504.18067/) (2025) - [Freq-Mip-AA : Frequency Mip Representation for Anti-Aliasing Neural Radiance Fields](https://mlsanity.com/paper/2406.13251/) (2024) - [Generalizable NGP-SR: Generalizable Neural Radiance Fields Super-Resolution via Neural Graph Primitives](https://mlsanity.com/paper/2603.20128/) (2026) - [CausalGS: Learning Physical Causality of 3D Dynamic Scenes with Gaussian Representations](https://mlsanity.com/paper/2605.10586/) (2026) ## Abstract (arXiv) Despite the remarkable achievements of neural radiance fields (NeRF) in representing 3D scenes and generating novel view images, the aliasing issue, rendering "jaggies" or "blurry" images at varying camera distances, remains unresolved in most existing approaches. The recently proposed mip-NeRF has addressed this challenge by rendering conical frustums instead of rays. However, it relies on MLP architecture to represent the radiance fields, missing out on the fast training speed offered by the latest grid-based methods. In this work, we present mip-Grid, a novel approach that integrates anti-aliasing techniques into grid-based representations for radiance fields, mitigating the aliasing artifacts while enjoying fast training time. The proposed method generates multi-scale grids by applying simple convolution operations over a shared grid representation and uses the scale-aware coordinate to retrieve features at different scales from the generated multi-scale grids. To test the effectiveness, we integrated the proposed method into the two recent representative grid-based methods, TensoRF and K-Planes. Experimental results demonstrate that mip-Grid greatly improves the rendering performance of both methods and even outperforms mip-NeRF on multi-scale datasets while achieving significantly faster training time. For code and demo videos, please see https://stnamjef.github.io/mipgrid.github.io/.