# LT-Gaussian: Long-Term Map Update Using 3D Gaussian Splatting for Autonomous Driving

arXiv: https://arxiv.org/abs/2508.01704 · HTML: https://mlsanity.com/paper/2508.01704/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 36 references that are in the map.
- First posted on arXiv 3 Aug 2025.

## Builds on (most cited first, 18 of 18)

- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [nuScenes: A multimodal dataset for autonomous driving](https://mlsanity.com/paper/1903.11027/) (2019)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [Depth Anything V2](https://mlsanity.com/paper/2406.09414/) (2024)
- [ORB-SLAM3: An Accurate Open-Source Library for Visual, Visual-Inertial and Multi-Map SLAM](https://mlsanity.com/paper/2007.11898/) (2020)
- [DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous Driving Scenes](https://mlsanity.com/paper/2312.07920/) (2023)
- [HDMapNet: An Online HD Map Construction and Evaluation Framework](https://mlsanity.com/paper/2107.06307/) (2021)
- [LIO-SAM: Tightly-coupled Lidar Inertial Odometry via Smoothing and Mapping](https://mlsanity.com/paper/2007.00258/) (2020)
- [FAST-LIO2: Fast Direct LiDAR-inertial Odometry](https://mlsanity.com/paper/2107.06829/) (2021)
- [Periodic Vibration Gaussian: Dynamic Urban Scene Reconstruction and Real-time Rendering](https://mlsanity.com/paper/2311.18561/) (2023)
- [SparseGS: Sparse View Synthesis using 3D Gaussian Splatting](https://mlsanity.com/paper/2312.00206/) (2023)
- [textitS3Gaussian: Self-Supervised Street Gaussians for Autonomous Driving](https://mlsanity.com/paper/2405.20323/) (2024)
- [Real-time Dynamic Object Detection for Autonomous Driving using Prior 3D-Maps](https://mlsanity.com/paper/1809.11036/) (2018)
- [ADMap: Anti-disturbance framework for reconstructing online vectorized HD map](https://mlsanity.com/paper/2401.13172/) (2024)
- [LT-mapper: A Modular Framework for LiDAR-based Lifelong Mapping](https://mlsanity.com/paper/2107.07712/) (2021)
- [Updating Street Maps using Changes Detected in Satellite Imagery](https://mlsanity.com/paper/2110.06456/) (2021)
- [LESS-Map: Lightweight and Evolving Semantic Map in Parking Lots for Long-term Self-Localization](https://mlsanity.com/paper/2310.07390/) (2023)
- [CCTNet: A Circular Convolutional Transformer Network for LiDAR-based Place Recognition Handling Movable Objects Occlusion](https://mlsanity.com/paper/2405.10793/) (2024)

## Built on by (most cited first, 5 of 5)

- [Changes in Real Time: Online Scene Change Detection with Multi-View Fusion](https://mlsanity.com/paper/2511.12370/) (2025)
- [Leveraging Geometric Priors for Unaligned Scene Change Detection](https://mlsanity.com/paper/2509.11292/) (2025)
- [SCD4VPR: Multi-modal Scene Change Detection for Long-term Visual Place Recognition Database Update](https://mlsanity.com/paper/2604.11402/) (2026)
- [Pocket-SLAM: Rendering-Area-Aware Pruning for Memory-Efficient 3DGS-SLAM](https://mlsanity.com/paper/2606.24796/) (2026)
- [ChronoFuseGS: Multi-Temporal Gaussian Fusion with Per-Splat Persistence and Change Visualization](https://mlsanity.com/paper/2609.31339/) (2026)

## Abstract (arXiv)

Maps play an important role in autonomous driving systems. The recently proposed 3D Gaussian Splatting (3D-GS) produces rendering-quality explicit scene reconstruction results, demonstrating the potential for map construction in autonomous driving scenarios. However, because of the time and computational costs involved in generating Gaussian scenes, how to update the map becomes a significant challenge. In this paper, we propose LT-Gaussian, a map update method for 3D-GS-based maps. LT-Gaussian consists of three main components: Multimodal Gaussian Splatting, Structural Change Detection Module, and Gaussian-Map Update Module. Firstly, the Gaussian map of the old scene is generated using our proposed Multimodal Gaussian Splatting. Subsequently, during the map update process, we compare the outdated Gaussian map with the current LiDAR data stream to identify structural changes. Finally, we perform targeted updates to the Gaussian-map to generate an up-to-date map. We establish a benchmark for map updating on the nuScenes dataset to quantitatively evaluate our method. The experimental results show that LT-Gaussian can effectively and efficiently update the Gaussian-map, handling common environmental changes in autonomous driving scenarios. Furthermore, by taking full advantage of information from both new and old scenes, LT-Gaussian is able to produce higher quality reconstruction results compared to map update strategies that reconstruct maps from scratch. Our open-source code is available at https://github.com/ChengLuqi/LT-gaussian.