# 3D Gaussian Splatting in Robotics: A Survey

arXiv: https://arxiv.org/abs/2410.12262 · HTML: https://mlsanity.com/paper/2410.12262/

## Key facts

- Cited by 45 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2024.

## Built on by (most cited first, 30 of 45)

- [What Is The Best 3D Scene Representation for Robotics? From Geometric to Foundation Models](https://mlsanity.com/paper/2512.03422/) (2025)
- [A Survey on 3D Gaussian Splatting Applications: Segmentation, Editing, and Generation](https://mlsanity.com/paper/2508.09977/) (2025)
- [SketchVideo: Sketch-based Video Generation and Editing](https://mlsanity.com/paper/2503.23284/) (2025)
- [UniForward: Unified 3D Scene and Semantic Field Reconstruction via Feed-Forward Gaussian Splatting from Only Sparse-View Images](https://mlsanity.com/paper/2506.09378/) (2025)
- [TC-GS: A Faster Gaussian Splatting Module Utilizing Tensor Cores](https://mlsanity.com/paper/2505.24796/) (2025)
- [GS-LTS: 3D Gaussian Splatting-Based Adaptive Modeling for Long-Term Service Robots](https://mlsanity.com/paper/2503.17733/) (2025)
- [SparseSplat: Towards Applicable Feed-Forward 3D Gaussian Splatting with Pixel-Unaligned Prediction](https://mlsanity.com/paper/2604.03069/) (2026)
- [GaussTrap: Stealthy Poisoning Attacks on 3D Gaussian Splatting for Targeted Scene Confusion](https://mlsanity.com/paper/2504.20829/) (2025)
- [Understanding while Exploring: Semantics-driven Active Mapping](https://mlsanity.com/paper/2506.00225/) (2025)
- [S2-MLLM: Boosting Spatial Reasoning Capability of MLLMs for 3D Visual Grounding with Structural Guidance](https://mlsanity.com/paper/2512.01223/) (2025)
- [Consistent Instance Field for Dynamic Scene Understanding](https://mlsanity.com/paper/2512.14126/) (2025)
- [FreeDriveRF: Monocular RGB Dynamic NeRF without Poses for Autonomous Driving via Point-Level Dynamic-Static Decoupling](https://mlsanity.com/paper/2505.09406/) (2025)
- [SeqAffordSplat: Scene-level Sequential Affordance Reasoning on 3D Gaussian Splatting](https://mlsanity.com/paper/2507.23772/) (2025)
- [InnerGS: Internal Scenes Reconstruction and Segmentation via Factorized 3D Gaussian Splatting](https://mlsanity.com/paper/2508.13287/) (2025)
- [Real2Sim: A Physics-driven and Editable Gaussian Splatting Framework for Autonomous Driving Scenes](https://mlsanity.com/paper/2605.13591/) (2026)
- [Efficient 3D Gaussian Splatting with Axis-Shared Rasterization and Order-independent Transmittance](https://mlsanity.com/paper/2506.07069/) (2025)
- [Fading the Digital Ink: A Universal Black-Box Attack Framework for 3DGS Watermarking Systems](https://mlsanity.com/paper/2508.07263/) (2025)
- [IGFuse: Interactive 3D Gaussian Scene Reconstruction via Multi-Scans Fusion](https://mlsanity.com/paper/2508.13153/) (2025)
- [SVR-GS: Spatially Variant Regularization for Probabilistic Masks in 3D Gaussian Splatting](https://mlsanity.com/paper/2509.11116/) (2025)
- [UniGS: Unified Geometry-Aware Gaussian Splatting for Multimodal Rendering](https://mlsanity.com/paper/2510.12174/) (2025)
- [SplatSearch: Instance Image Goal Navigation for Mobile Robots using 3D Gaussian Splatting and Diffusion Models](https://mlsanity.com/paper/2511.12972/) (2025)
- [Towards Next-Generation SLAM: A Survey on 3DGS-SLAM Focusing on Performance, Robustness, and Future Directions](https://mlsanity.com/paper/2602.04251/) (2026)
- [WeatherCity: Urban Scene Reconstruction with Controllable Multi-Weather Transformation](https://mlsanity.com/paper/2602.22096/) (2026)
- [LangFlash: Feed-forward 3D Language Gaussian Splatting from Sparse Unposed Images](https://mlsanity.com/paper/2605.23287/) (2026)
- [CARLA-GS: Decoupling Representation, Reasoning, and Physics Simulation for Autonomous Driving Corner-Case Synthesis](https://mlsanity.com/paper/2607.07601/) (2026)
- [DSG-World: Learning a 3D Gaussian World Model from Dual State Videos](https://mlsanity.com/paper/2506.05217/) (2025)
- [Geometry Gaussians: Decoupling Appearance and Geometry in Gaussian Splatting](https://mlsanity.com/paper/2606.05124/) (2026)
- [Online Segment 3D Gaussians via Launching Virtual Drones](https://mlsanity.com/paper/2607.01628/) (2026)
- [Preference-Driven Active 3D Scene Representation for Robotic Inspection in Nuclear Decommissioning](https://mlsanity.com/paper/2504.02161/) (2025)
- [GSFeatLoc: Visual Localization Using Feature Correspondence on 3D Gaussian Splatting](https://mlsanity.com/paper/2504.20379/) (2025)

## Abstract (arXiv)

Dense 3D representations of the environment have been a long-term goal in the robotics field. While previous Neural Radiance Fields (NeRF) representation have been prevalent for its implicit, coordinate-based model, the recent emergence of 3D Gaussian Splatting (3DGS) has demonstrated remarkable potential in its explicit radiance field representation. By leveraging 3D Gaussian primitives for explicit scene representation and enabling differentiable rendering, 3DGS has shown significant advantages over other radiance fields in real-time rendering and photo-realistic performance, which is beneficial for robotic applications. In this survey, we provide a comprehensive understanding of 3DGS in the field of robotics. We divide our discussion of the related works into two main categories: the application of 3DGS and the advancements in 3DGS techniques. In the application section, we explore how 3DGS has been utilized in various robotics tasks from scene understanding and interaction perspectives. The advance of 3DGS section focuses on the improvements of 3DGS own properties in its adaptability and efficiency, aiming to enhance its performance in robotics. We then summarize the most commonly used datasets and evaluation metrics in robotics. Finally, we identify the challenges and limitations of current 3DGS methods and discuss the future development of 3DGS in robotics.