# Grid4D: 4D Decomposed Hash Encoding for High-Fidelity Dynamic Gaussian Splatting

arXiv: https://arxiv.org/abs/2410.20815 · HTML: https://mlsanity.com/paper/2410.20815/

## Key facts

- Cited by 49 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 55 references that are in the map.
- First posted on arXiv 28 Oct 2024.

## Builds on (most cited first, 30 of 44)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [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)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [D-NeRF: Neural Radiance Fields for Dynamic Scenes](https://mlsanity.com/paper/2011.13961/) (2020)
- [Efficient Geometry-aware 3D Generative Adversarial Networks](https://mlsanity.com/paper/2112.07945/) (2021)
- [4D Gaussian Splatting for Real-Time Dynamic Scene Rendering](https://mlsanity.com/paper/2310.08528/) (2023)
- [Deformable 3D Gaussians for High-Fidelity Monocular Dynamic Scene Reconstruction](https://mlsanity.com/paper/2309.13101/) (2023)
- [Neural Scene Flow Fields for Space-Time View Synthesis of Dynamic Scenes](https://mlsanity.com/paper/2011.13084/) (2020)
- [K-Planes: Explicit Radiance Fields in Space, Time, and Appearance](https://mlsanity.com/paper/2301.10241/) (2023)
- [Dynamic 3D Gaussians: Tracking by Persistent Dynamic View Synthesis](https://mlsanity.com/paper/2308.09713/) (2023)
- [HexPlane: A Fast Representation for Dynamic Scenes](https://mlsanity.com/paper/2301.09632/) (2023)
- [Real-time Photorealistic Dynamic Scene Representation and Rendering with 4D Gaussian Splatting](https://mlsanity.com/paper/2310.10642/) (2023)
- [Non-Rigid Neural Radiance Fields: Reconstruction and Novel View Synthesis of a Dynamic Scene From Monocular Video](https://mlsanity.com/paper/2012.12247/) (2020)
- [Dynamic View Synthesis from Dynamic Monocular Video](https://mlsanity.com/paper/2105.06468/) (2021)
- [SC-GS: Sparse-Controlled Gaussian Splatting for Editable Dynamic Scenes](https://mlsanity.com/paper/2312.14937/) (2023)
- [Fast Dynamic Radiance Fields with Time-Aware Neural Voxels](https://mlsanity.com/paper/2205.15285/) (2022)
- [Spacetime Gaussian Feature Splatting for Real-Time Dynamic View Synthesis](https://mlsanity.com/paper/2312.16812/) (2023)
- [Neural Radiance Flow for 4D View Synthesis and Video Processing](https://mlsanity.com/paper/2012.09790/) (2020)
- [Differentiable Surface Splatting for Point-based Geometry Processing](https://mlsanity.com/paper/1906.04173/) (2019)
- [DynIBaR: Neural Dynamic Image-Based Rendering](https://mlsanity.com/paper/2211.11082/) (2022)
- [Tensor4D : Efficient Neural 4D Decomposition for High-fidelity Dynamic Reconstruction and Rendering](https://mlsanity.com/paper/2211.11610/) (2022)
- [Gaussian-Flow: 4D Reconstruction with Dynamic 3D Gaussian Particle](https://mlsanity.com/paper/2312.03431/) (2023)
- [BANMo: Building Animatable 3D Neural Models from Many Casual Videos](https://mlsanity.com/paper/2112.12761/) (2021)
- [Robust Dynamic Radiance Fields](https://mlsanity.com/paper/2301.02239/) (2023)
- [4D-Rotor Gaussian Splatting: Towards Efficient Novel View Synthesis for Dynamic Scenes](https://mlsanity.com/paper/2402.03307/) (2024)
- [3DGStream: On-the-Fly Training of 3D Gaussians for Efficient Streaming of Photo-Realistic Free-Viewpoint Videos](https://mlsanity.com/paper/2403.01444/) (2024)
- [NeRFPlayer: A Streamable Dynamic Scene Representation with Decomposed Neural Radiance Fields](https://mlsanity.com/paper/2210.15947/) (2022)
- [Mixed Neural Voxels for Fast Multi-view Video Synthesis](https://mlsanity.com/paper/2212.00190/) (2022)

## Built on by (most cited first, 30 of 49)

- [AD-GS: Object-Aware B-Spline Gaussian Splatting for Self-Supervised Autonomous Driving](https://mlsanity.com/paper/2507.12137/) (2025)
- [1000+ FPS 4D Gaussian Splatting for Dynamic Scene Rendering](https://mlsanity.com/paper/2503.16422/) (2025)
- [LocalDyGS: Multi-view Global Dynamic Scene Modeling via Adaptive Local Implicit Feature Decoupling](https://mlsanity.com/paper/2507.02363/) (2025)
- [HAIF-GS: Hierarchical and Induced Flow-Guided Gaussian Splatting for Dynamic Scene](https://mlsanity.com/paper/2506.09518/) (2025)
- [Instant4D: 4D Gaussian Splatting in Minutes](https://mlsanity.com/paper/2510.01119/) (2025)
- [AdaGaR: Adaptive Gabor Representation for Dynamic Scene Reconstruction](https://mlsanity.com/paper/2601.00796/) (2026)
- [Light4GS: Lightweight Compact 4D Gaussian Splatting Generation via Context Model](https://mlsanity.com/paper/2503.13948/) (2025)
- [D-FCGS: Feedforward Compression of Dynamic Gaussian Splatting for Free-Viewpoint Videos](https://mlsanity.com/paper/2507.05859/) (2025)
- [Physics-Informed Deformable Gaussian Splatting: Towards Unified Constitutive Laws for Time-Evolving Material Field](https://mlsanity.com/paper/2511.06299/) (2025)
- [ParticleGS: Learning Neural Gaussian Particle Dynamics from Videos for Prior-free Physical Motion Extrapolation](https://mlsanity.com/paper/2505.20270/) (2025)
- [Holistic Large-Scale Scene Reconstruction via Mixed Gaussian Splatting](https://mlsanity.com/paper/2505.23280/) (2025)
- [Advances in 4D Representation: Geometry, Motion, and Interaction](https://mlsanity.com/paper/2510.19255/) (2025)
- [IDSplat: Instance-Decomposed 3D Gaussian Splatting for Driving Scenes](https://mlsanity.com/paper/2511.19235/) (2025)
- [TED-4DGS: Temporally Activated and Embedding-based Deformation for 4DGS Compression](https://mlsanity.com/paper/2512.05446/) (2025)
- [ClipGStream: Clip-Stream Gaussian Splatting for Any Length and Any Motion Multi-View Dynamic Scene Reconstruction](https://mlsanity.com/paper/2604.13746/) (2026)
- [Splannequin: Freezing Monocular Mannequin-Challenge Footage with Dual-Detection Splatting](https://mlsanity.com/paper/2512.05113/) (2025)
- [MotionPhysics: Learnable Motion Distillation for Text-Guided Simulation](https://mlsanity.com/paper/2601.00504/) (2026)
- [PackUV: Packed Gaussian UV Maps for 4D Volumetric Video](https://mlsanity.com/paper/2602.23040/) (2026)
- [STGV: Spatio-Temporal Hash Encoding for Gaussian-based Video Representation](https://mlsanity.com/paper/2604.10910/) (2026)
- [Disentangled 4D Gaussian Splatting: Rendering High-Resolution Dynamic World at 343 FPS](https://mlsanity.com/paper/2503.22159/) (2025)
- [From Tokens to Nodes: Semantic-Guided Motion Control for Dynamic 3D Gaussian Splatting](https://mlsanity.com/paper/2510.02732/) (2025)
- [Interaction-Aware 4D Gaussian Splatting for Dynamic Hand-Object Interaction Reconstruction](https://mlsanity.com/paper/2511.14540/) (2025)
- [SharpTimeGS: Sharp and Stable Dynamic Gaussian Splatting via Lifespan Modulation](https://mlsanity.com/paper/2602.02989/) (2026)
- [Mango-GS: Enhancing Spatio-Temporal Consistency in Dynamic Scenes Reconstruction using Multi-Frame Node-Guided 4D Gaussian Splatting](https://mlsanity.com/paper/2603.11543/) (2026)
- [textit4DSurf: High-Fidelity Dynamic Scene Surface Reconstruction](https://mlsanity.com/paper/2603.28064/) (2026)
- [DySurface: Consistent 4D Surface Reconstruction via Bridging Explicit Gaussians and Implicit Functions](https://mlsanity.com/paper/2605.10360/) (2026)
- [On the Design of Mixture-of-Experts for Dynamic Gaussian Splatting](https://mlsanity.com/paper/2607.08250/) (2026)
- [Reconstructing the Dynamic World: A Representation-Centric View of 4D Scene Reconstruction](https://mlsanity.com/paper/2609.39960/) (2026)
- [Laplacian Analysis Meets Dynamics Modelling: Gaussian Splatting for 4D Reconstruction](https://mlsanity.com/paper/2508.04966/) (2025)
- [Gaussians on Fire: High-Frequency Reconstruction of Flames](https://mlsanity.com/paper/2511.22459/) (2025)

## Abstract (arXiv)

Recently, Gaussian splatting has received more and more attention in the field of static scene rendering. Due to the low computational overhead and inherent flexibility of explicit representations, plane-based explicit methods are popular ways to predict deformations for Gaussian-based dynamic scene rendering models. However, plane-based methods rely on the inappropriate low-rank assumption and excessively decompose the space-time 4D encoding, resulting in overmuch feature overlap and unsatisfactory rendering quality. To tackle these problems, we propose Grid4D, a dynamic scene rendering model based on Gaussian splatting and employing a novel explicit encoding method for the 4D input through the hash encoding. Different from plane-based explicit representations, we decompose the 4D encoding into one spatial and three temporal 3D hash encodings without the low-rank assumption. Additionally, we design a novel attention module that generates the attention scores in a directional range to aggregate the spatial and temporal features. The directional attention enables Grid4D to more accurately fit the diverse deformations across distinct scene components based on the spatial encoded features. Moreover, to mitigate the inherent lack of smoothness in explicit representation methods, we introduce a smooth regularization term that keeps our model from the chaos of deformation prediction. Our experiments demonstrate that Grid4D significantly outperforms the state-of-the-art models in visual quality and rendering speed.