# SAGS: Structure-Aware 3D Gaussian Splatting

arXiv: https://arxiv.org/abs/2404.19149 · HTML: https://mlsanity.com/paper/2404.19149/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 41 references that are in the map.
- First posted on arXiv 29 Apr 2024.

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

- [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)
- [PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space](https://mlsanity.com/paper/1706.02413/) (2017)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018)
- [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)
- [SuperGlue: Learning Feature Matching with Graph Neural Networks](https://mlsanity.com/paper/1911.11763/) (2019)
- [SuperPoint: Self-Supervised Interest Point Detection and Description](https://mlsanity.com/paper/1712.07629/) (2017)
- [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://mlsanity.com/paper/2008.02268/) (2020)
- [DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation](https://mlsanity.com/paper/2309.16653/) (2023)
- [Block-NeRF: Scalable Large Scene Neural View Synthesis](https://mlsanity.com/paper/2202.05263/) (2022)
- [Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering](https://mlsanity.com/paper/2312.00109/) (2023)
- [DeepGCNs: Can GCNs Go as Deep as CNNs?](https://mlsanity.com/paper/1904.03751/) (2019)
- [LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS](https://mlsanity.com/paper/2311.17245/) (2023)
- [Compact 3D Gaussian Representation for Radiance Field](https://mlsanity.com/paper/2311.13681/) (2023)
- [SynSin: End-to-end View Synthesis from a Single Image](https://mlsanity.com/paper/1912.08804/) (2019)
- [MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures](https://mlsanity.com/paper/2208.00277/) (2022)
- [Text-to-3D using Gaussian Splatting](https://mlsanity.com/paper/2309.16585/) (2023)
- [Differentiable Surface Splatting for Point-based Geometry Processing](https://mlsanity.com/paper/1906.04173/) (2019)
- [Compressed 3D Gaussian Splatting for Accelerated Novel View Synthesis](https://mlsanity.com/paper/2401.02436/) (2023)
- [BungeeNeRF: Progressive Neural Radiance Field for Extreme Multi-scale Scene Rendering](https://mlsanity.com/paper/2112.05504/) (2021)
- [SparseGS: Sparse View Synthesis using 3D Gaussian Splatting](https://mlsanity.com/paper/2312.00206/) (2023)
- [FoV-NeRF: Foveated Neural Radiance Fields for Virtual Reality](https://mlsanity.com/paper/2103.16365/) (2021)
- [Grad-PU: Arbitrary-Scale Point Cloud Upsampling via Gradient Descent with Learned Distance Functions](https://mlsanity.com/paper/2304.11846/) (2023)
- [Uncertainty-Aware Deep Multi-View Photometric Stereo](https://mlsanity.com/paper/2202.13071/) (2022)
- [Baseline Desensitizing In Translation Averaging](https://mlsanity.com/paper/1901.00643/) (2019)
- [ILSH: The Imperial Light-Stage Head Dataset for Human Head View Synthesis](https://mlsanity.com/paper/2310.03952/) (2023)
- [Revisiting Point Cloud Simplification: A Learnable Feature Preserving Approach](https://mlsanity.com/paper/2109.14982/) (2021)

## Built on by (most cited first, 21 of 21)

- [Speedy-Splat: Fast 3D Gaussian Splatting with Sparse Pixels and Sparse Primitives](https://mlsanity.com/paper/2412.00578/) (2024)
- [DOGS: Distributed-Oriented Gaussian Splatting for Large-Scale 3D Reconstruction Via Gaussian Consensus](https://mlsanity.com/paper/2405.13943/) (2024)
- [EVER: Exact Volumetric Ellipsoid Rendering for Real-time View Synthesis](https://mlsanity.com/paper/2410.01804/) (2024)
- [3D-HGS: 3D Half-Gaussian Splatting](https://mlsanity.com/paper/2406.02720/) (2024)
- [Beyond Gaussians: Fast and High-Fidelity 3D Splatting with Linear Kernels](https://mlsanity.com/paper/2411.12440/) (2024)
- [Arc2Avatar: Generating Expressive 3D Avatars from a Single Image via ID Guidance](https://mlsanity.com/paper/2501.05379/) (2025)
- [QuickSplat: Fast 3D Surface Reconstruction via Learned Gaussian Initialization](https://mlsanity.com/paper/2505.05591/) (2025)
- [GSCodec Studio: A Modular Framework for Gaussian Splat Compression](https://mlsanity.com/paper/2506.01822/) (2025)
- [ZeroGS: Training 3D Gaussian Splatting from Unposed Images](https://mlsanity.com/paper/2411.15779/) (2024)
- [Normal-GS: 3D Gaussian Splatting with Normal-Involved Rendering](https://mlsanity.com/paper/2410.20593/) (2024)
- [Mode-GS: Monocular Depth Guided Anchored 3D Gaussian Splatting for Robust Ground-View Scene Rendering](https://mlsanity.com/paper/2410.04646/) (2024)
- [Holistic Large-Scale Scene Reconstruction via Mixed Gaussian Splatting](https://mlsanity.com/paper/2505.23280/) (2025)
- [Long-LRM++: Preserving Fine Details in Feed-Forward Wide-Coverage Reconstruction](https://mlsanity.com/paper/2512.10267/) (2025)
- [TriSplat: Simulation-Ready Feed-Forward 3D Scene Reconstruction](https://mlsanity.com/paper/2605.26115/) (2026)
- [UniGS: Unified Geometry-Aware Gaussian Splatting for Multimodal Rendering](https://mlsanity.com/paper/2510.12174/) (2025)
- [FreeOcc: Training-Free Embodied Open-Vocabulary Occupancy Prediction](https://mlsanity.com/paper/2604.28115/) (2026)
- [GeoSplat: A Deep Dive into Geometry-Constrained Gaussian Splatting](https://mlsanity.com/paper/2509.05075/) (2025)
- [FreeScale: Scaling 3D Scenes via Certainty-Aware Free-View Generation](https://mlsanity.com/paper/2604.10512/) (2026)
- [MAC-Splat: Multi-Attribute Consistency for High-Fidelity Sparse-View Reconstruction](https://mlsanity.com/paper/2607.10792/) (2026)
- [Towards Understanding 3D Vision: the Role of Gaussian Curvature](https://mlsanity.com/paper/2508.11825/) (2025)
- [Softmax-GS: Generalized Gaussians Learning When to Blend or Bound](https://mlsanity.com/paper/2604.27437/) (2026)

## Abstract (arXiv)

Following the advent of NeRFs, 3D Gaussian Splatting (3D-GS) has paved the way to real-time neural rendering overcoming the computational burden of volumetric methods. Following the pioneering work of 3D-GS, several methods have attempted to achieve compressible and high-fidelity performance alternatives. However, by employing a geometry-agnostic optimization scheme, these methods neglect the inherent 3D structure of the scene, thereby restricting the expressivity and the quality of the representation, resulting in various floating points and artifacts. In this work, we propose a structure-aware Gaussian Splatting method (SAGS) that implicitly encodes the geometry of the scene, which reflects to state-of-the-art rendering performance and reduced storage requirements on benchmark novel-view synthesis datasets. SAGS is founded on a local-global graph representation that facilitates the learning of complex scenes and enforces meaningful point displacements that preserve the scene's geometry. Additionally, we introduce a lightweight version of SAGS, using a simple yet effective mid-point interpolation scheme, which showcases a compact representation of the scene with up to 24× size reduction without the reliance on any compression strategies. Extensive experiments across multiple benchmark datasets demonstrate the superiority of SAGS compared to state-of-the-art 3D-GS methods under both rendering quality and model size. Besides, we demonstrate that our structure-aware method can effectively mitigate floating artifacts and irregular distortions of previous methods while obtaining precise depth maps. Project page https://eververas.github.io/SAGS/.