# 2DGS-Planner: Rasterization-based Path Planning in 2D Gaussian Splatting Map

arXiv: https://arxiv.org/abs/2610.11752 · HTML: https://mlsanity.com/paper/2610.11752/

## Key facts

- No citing papers in the map yet (data through 19 Oct 2026).
- Placed in SLAM and driving scene reconstruction, one of 15 directions in 3D reconstruction and neural rendering.
- Builds on 23 of its 31 references that are in the map.
- First posted on arXiv 8 Oct 2026.

## Placed in

- 3D reconstruction and neural rendering / [SLAM and driving scene reconstruction](https://mlsanity.com/recon3d/slam-and-driving-scene-reconstruction/)

## Builds on (most cited first, 23 of 23)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers](https://mlsanity.com/paper/2105.15203/) (2021)
- [2D Gaussian Splatting for Geometrically Accurate Radiance Fields](https://mlsanity.com/paper/2403.17888/) (2024)
- [On Evaluation of Embodied Navigation Agents](https://mlsanity.com/paper/1807.06757/) (2018)
- [PGSR: Planar-based Gaussian Splatting for Efficient and High-Fidelity Surface Reconstruction](https://mlsanity.com/paper/2406.06521/) (2024)
- [FAST-LIO2: Fast Direct LiDAR-inertial Odometry](https://mlsanity.com/paper/2107.06829/) (2021)
- [Vision-Only Robot Navigation in a Neural Radiance World](https://mlsanity.com/paper/2110.00168/) (2021)
- [Voxblox: Incremental 3D Euclidean Signed Distance Fields for On-Board MAV Planning](https://mlsanity.com/paper/1611.03631/) (2016)
- [Revising Densification in Gaussian Splatting](https://mlsanity.com/paper/2404.06109/) (2024)
- [RaDe-GS: Rasterizing Depth in Gaussian Splatting](https://mlsanity.com/paper/2406.01467/) (2024)
- [3DGUT: Enabling Distorted Cameras and Secondary Rays in Gaussian Splatting](https://mlsanity.com/paper/2412.12507/) (2024)
- [Splat-Nav: Safe Real-Time Robot Navigation in Gaussian Splatting Maps](https://mlsanity.com/paper/2403.02751/) (2024)
- [Renderable Neural Radiance Map for Visual Navigation](https://mlsanity.com/paper/2303.00304/) (2023)
- [Informed RRT*: Optimal Sampling-based Path Planning Focused via Direct Sampling of an Admissible Ellipsoidal Heuristic](https://mlsanity.com/paper/1404.2334/) (2014)
- [GS-Planner: A Gaussian-Splatting-based Planning Framework for Active High-Fidelity Reconstruction](https://mlsanity.com/paper/2405.10142/) (2024)
- [SAFER-Splat: A Control Barrier Function for Safe Navigation with Online Gaussian Splatting Maps](https://mlsanity.com/paper/2409.09868/) (2024)
- [Let's Make a Splan: Risk-Aware Trajectory Optimization in a Normalized Gaussian Splat](https://mlsanity.com/paper/2409.16915/) (2024)
- [FOCI: Trajectory Optimization on Gaussian Splats](https://mlsanity.com/paper/2505.08510/) (2025)
- [Splatblox: Traversability-Aware Gaussian Splatting for Outdoor Robot Navigation](https://mlsanity.com/paper/2511.18525/) (2025)
- [ReMoSPLAT: Reactive Mobile Manipulation Control on a Gaussian Splat](https://mlsanity.com/paper/2512.09656/) (2025)
- [Neural Radiance Maps for Extraterrestrial Navigation and Path Planning](https://mlsanity.com/paper/2603.17236/) (2026)
- [LiftNav: Path Planning via Semantic Lifting in TSDF-Guided Gaussian Splatting](https://mlsanity.com/paper/2605.31376/) (2026)
- [SplatlessDF: Continuous Distance Field Mapping with Non-Splatting Gaussians](https://mlsanity.com/paper/2606.13990/) (2026)

## Abstract (arXiv)

Gaussian splatting provides an explicit and efficiently rasterizable scene representation for robot navigation. However, individual Gaussian primitives may not reliably represent obstacles as they are jointly optimized through alpha-composited rendering from a finite set of reconstruction views. We propose 2DGS-Planner, a path planner for ground robots that reads planning-relevant geometry from a 2D Gaussian splatting (2DGS) map through rasterization, rather than treating individual Gaussian primitives as obstacles. During offline roadmap construction, multi-view attribution converts rendered normal dispersion into structural scores for non-ground disks supported by the reconstruction views. These scores guide adaptive node sampling on the ground. Path-aligned orthographic queries validate candidate edges, while cylindrical queries estimate local clearance fields that are cached on the edges. During online planning, graph search initializes a route, and path refinement reuses the cached fields while accounting for the robot's dimensions and ground constraints. Experiments demonstrate improved roadmap connectivity, more accurate clearance estimation, and higher planning success compared with the tested baselines. These results support rasterization as an effective geometric query interface for planning directly on Gaussian maps. Code and data are available at https://2dgs-planner.github.io/