# GaussReg: Fast 3D Registration with Gaussian Splatting

arXiv: https://arxiv.org/abs/2407.05254 · HTML: https://mlsanity.com/paper/2407.05254/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 26 of its 43 references that are in the map.
- First posted on arXiv 7 Jul 2024.

## Builds on (most cited first, 26 of 26)

- [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)
- [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)
- [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)
- [TensoRF: Tensorial Radiance Fields](https://mlsanity.com/paper/2203.09517/) (2022)
- [KPConv: Flexible and Deformable Convolution for Point Clouds](https://mlsanity.com/paper/1904.08889/) (2019)
- [Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction](https://mlsanity.com/paper/2111.11215/) (2021)
- [MVSNet: Depth Inference for Unstructured Multi-view Stereo](https://mlsanity.com/paper/1804.02505/) (2018)
- [Objaverse-XL: A Universe of 10M+ 3D Objects](https://mlsanity.com/paper/2307.05663/) (2023)
- [From Coarse to Fine: Robust Hierarchical Localization at Large Scale](https://mlsanity.com/paper/1812.03506/) (2018)
- [3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions](https://mlsanity.com/paper/1603.08182/) (2016)
- [Deep Closest Point: Learning Representations for Point Cloud Registration](https://mlsanity.com/paper/1905.03304/) (2019)
- [Geometric Transformer for Fast and Robust Point Cloud Registration](https://mlsanity.com/paper/2202.06688/) (2022)
- [D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features](https://mlsanity.com/paper/2003.03164/) (2020)
- [The Perfect Match: 3D Point Cloud Matching with Smoothed Densities](https://mlsanity.com/paper/1811.06879/) (2018)
- [F2-NeRF: Fast Neural Radiance Field Training with Free Camera Trajectories](https://mlsanity.com/paper/2303.15951/) (2023)
- [3DRegNet: A Deep Neural Network for 3D Point Registration](https://mlsanity.com/paper/1904.01701/) (2019)
- [REGTR: End-to-end Point Cloud Correspondences with Transformers](https://mlsanity.com/paper/2203.14517/) (2022)
- [3D Registration with Maximal Cliques](https://mlsanity.com/paper/2305.10854/) (2023)
- [Fast and Robust Iterative Closest Point](https://mlsanity.com/paper/2007.07627/) (2020)
- [nerf2nerf: Pairwise Registration of Neural Radiance Fields](https://mlsanity.com/paper/2211.01600/) (2022)
- [DReg-NeRF: Deep Registration for Neural Radiance Fields](https://mlsanity.com/paper/2308.09386/) (2023)
- [Iterative Global Similarity Points : A robust coarse-to-fine integration solution for pairwise 3D point cloud registration](https://mlsanity.com/paper/1808.03899/) (2018)
- [NeRFuser: Large-Scale Scene Representation by NeRF Fusion](https://mlsanity.com/paper/2305.13307/) (2023)

## Built on by (most cited first, 19 of 19)

- [LoopSplat: Loop Closure by Registering 3D Gaussian Splats](https://mlsanity.com/paper/2408.10154/) (2024)
- [What Is The Best 3D Scene Representation for Robotics? From Geometric to Foundation Models](https://mlsanity.com/paper/2512.03422/) (2025)
- [IAAO: Interactive Affordance Learning for Articulated Objects in 3D Environments](https://mlsanity.com/paper/2504.06827/) (2025)
- [Diffusion Knows Transparency: Repurposing Video Diffusion for Transparent Object Depth and Normal Estimation](https://mlsanity.com/paper/2512.23705/) (2025)
- [SIREN: Semantic, Initialization-Free Registration of Multi-Robot Gaussian Splatting Maps](https://mlsanity.com/paper/2502.06519/) (2025)
- [GSVisLoc: Generalizable Visual Localization for Gaussian Splatting Scene Representations](https://mlsanity.com/paper/2508.18242/) (2025)
- [Automated 3D-GS Registration and Fusion via Skeleton Alignment and Gaussian-Adaptive Features](https://mlsanity.com/paper/2507.20480/) (2025)
- [GERA: Geometric Embedding for Efficient Point Registration Analysis](https://mlsanity.com/paper/2410.00589/) (2024)
- [EigenGS Representation: From Eigenspace to Gaussian Image Space](https://mlsanity.com/paper/2503.07446/) (2025)
- [PAOLI: Pose-free Articulated Object Learning from Sparse-view Images](https://mlsanity.com/paper/2509.04276/) (2025)
- [LTGS: Long-Term Gaussian Scene Chronology From Sparse View Updates](https://mlsanity.com/paper/2510.09881/) (2025)
- [Scene Grounding In the Wild](https://mlsanity.com/paper/2603.26584/) (2026)
- [GSCompleter: A Distillation-Free Plugin for Metric-Aware 3D Gaussian Splatting Completion in Seconds](https://mlsanity.com/paper/2604.20155/) (2026)
- [Evaluation of Visual Place Recognition Methods for Image Pair Retrieval in 3D Vision and Robotics](https://mlsanity.com/paper/2603.13917/) (2026)
- [Cross-Instance Gaussian Splatting Registration via Geometry-Aware Feature-Guided Alignment](https://mlsanity.com/paper/2603.21936/) (2026)
- [Compact Keyframe-Optimized Multi-Agent Gaussian Splatting SLAM](https://mlsanity.com/paper/2604.00804/) (2026)
- [Graph-GSReg: Leveraging 3D Scene Graphs for Gaussian Splatting Registration](https://mlsanity.com/paper/2606.29782/) (2026)
- [CoRef-GS: Cooperative Referring Gaussian Splatting for Multi-Agent Scene Understanding](https://mlsanity.com/paper/2609.20586/) (2026)
- [ChronoFuseGS: Multi-Temporal Gaussian Fusion with Per-Splat Persistence and Change Visualization](https://mlsanity.com/paper/2609.31339/) (2026)

## Abstract (arXiv)

Point cloud registration is a fundamental problem for large-scale 3D scene scanning and reconstruction. With the help of deep learning, registration methods have evolved significantly, reaching a nearly-mature stage. As the introduction of Neural Radiance Fields (NeRF), it has become the most popular 3D scene representation as its powerful view synthesis capabilities. Regarding NeRF representation, its registration is also required for large-scale scene reconstruction. However, this topic extremly lacks exploration. This is due to the inherent challenge to model the geometric relationship among two scenes with implicit representations. The existing methods usually convert the implicit representation to explicit representation for further registration. Most recently, Gaussian Splatting (GS) is introduced, employing explicit 3D Gaussian. This method significantly enhances rendering speed while maintaining high rendering quality. Given two scenes with explicit GS representations, in this work, we explore the 3D registration task between them. To this end, we propose GaussReg, a novel coarse-to-fine framework, both fast and accurate. The coarse stage follows existing point cloud registration methods and estimates a rough alignment for point clouds from GS. We further newly present an image-guided fine registration approach, which renders images from GS to provide more detailed geometric information for precise alignment. To support comprehensive evaluation, we carefully build a scene-level dataset called ScanNet-GSReg with 1379 scenes obtained from the ScanNet dataset and collect an in-the-wild dataset called GSReg. Experimental results demonstrate our method achieves state-of-the-art performance on multiple datasets. Our GaussReg is 44 times faster than HLoc (SuperPoint as the feature extractor and SuperGlue as the matcher) with comparable accuracy.