# AutoSplat: Constrained Gaussian Splatting for Autonomous Driving Scene Reconstruction

arXiv: https://arxiv.org/abs/2407.02598 · HTML: https://mlsanity.com/paper/2407.02598/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- Builds on 34 of its 41 references that are in the map.
- First posted on arXiv 2 Jul 2024.

## Builds on (most cited first, 30 of 34)

- [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)
- [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://mlsanity.com/paper/2201.05989/) (2022)
- [Masked-attention Mask Transformer for Universal Image Segmentation](https://mlsanity.com/paper/2112.01527/) (2021)
- [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)
- [TensoRF: Tensorial Radiance Fields](https://mlsanity.com/paper/2203.09517/) (2022)
- [NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collections](https://mlsanity.com/paper/2008.02268/) (2020)
- [Efficient Geometry-aware 3D Generative Adversarial Networks](https://mlsanity.com/paper/2112.07945/) (2021)
- [Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction](https://mlsanity.com/paper/2111.11215/) (2021)
- [Block-NeRF: Scalable Large Scene Neural View Synthesis](https://mlsanity.com/paper/2202.05263/) (2022)
- [Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision](https://mlsanity.com/paper/1912.07372/) (2019)
- [Point-E: A System for Generating 3D Point Clouds from Complex Prompts](https://mlsanity.com/paper/2212.08751/) (2022)
- [Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fields](https://mlsanity.com/paper/2304.06706/) (2023)
- [Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fields](https://mlsanity.com/paper/2112.03907/) (2021)
- [FastNeRF: High-Fidelity Neural Rendering at 200FPS](https://mlsanity.com/paper/2103.10380/) (2021)
- [Mega-NeRF: Scalable Construction of Large-Scale NeRFs for Virtual Fly-Throughs](https://mlsanity.com/paper/2112.10703/) (2021)
- [DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous Driving Scenes](https://mlsanity.com/paper/2312.07920/) (2023)
- [MobileNeRF: Exploiting the Polygon Rasterization Pipeline for Efficient Neural Field Rendering on Mobile Architectures](https://mlsanity.com/paper/2208.00277/) (2022)
- [Neural Scene Graphs for Dynamic Scenes](https://mlsanity.com/paper/2011.10379/) (2020)
- [UniSim: A Neural Closed-Loop Sensor Simulator](https://mlsanity.com/paper/2308.01898/) (2023)
- [BakedSDF: Meshing Neural SDFs for Real-Time View Synthesis](https://mlsanity.com/paper/2302.14859/) (2023)
- [MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes](https://mlsanity.com/paper/2302.12249/) (2023)
- [EmerNeRF: Emergent Spatial-Temporal Scene Decomposition via Self-Supervision](https://mlsanity.com/paper/2311.02077/) (2023)
- [PandaSet: Advanced Sensor Suite Dataset for Autonomous Driving](https://mlsanity.com/paper/2112.12610/) (2021)
- [MARS: An Instance-aware, Modular and Realistic Simulator for Autonomous Driving](https://mlsanity.com/paper/2307.15058/) (2023)
- [SUDS: Scalable Urban Dynamic Scenes](https://mlsanity.com/paper/2303.14536/) (2023)
- [Periodic Vibration Gaussian: Dynamic Urban Scene Reconstruction and Real-time Rendering](https://mlsanity.com/paper/2311.18561/) (2023)
- [StreetSurf: Extending Multi-view Implicit Surface Reconstruction to Street Views](https://mlsanity.com/paper/2306.04988/) (2023)

## Built on by (most cited first, 30 of 52)

- [MV-DUSt3R+: Single-Stage Scene Reconstruction from Sparse Views In 2 Seconds](https://mlsanity.com/paper/2412.06974/) (2024)
- [SplatAD: Real-Time Lidar and Camera Rendering with 3D Gaussian Splatting for Autonomous Driving](https://mlsanity.com/paper/2411.16816/) (2024)
- [DrivingForward: Feed-forward 3D Gaussian Splatting for Driving Scene Reconstruction from Flexible Surround-view Input](https://mlsanity.com/paper/2409.12753/) (2024)
- [ReconDreamer++: Harmonizing Generative and Reconstructive Models for Driving Scene Representation](https://mlsanity.com/paper/2503.18438/) (2025)
- [FreeSim: Toward Free-viewpoint Camera Simulation in Driving Scenes](https://mlsanity.com/paper/2412.03566/) (2024)
- [GaussGym: An open-source real-to-sim framework for learning locomotion from pixels](https://mlsanity.com/paper/2510.15352/) (2025)
- [EVolSplat: Efficient Volume-based Gaussian Splatting for Urban View Synthesis](https://mlsanity.com/paper/2503.20168/) (2025)
- [PixelGaussian: Generalizable 3D Gaussian Reconstruction from Arbitrary Views](https://mlsanity.com/paper/2410.18979/) (2024)
- [MTGS: Multi-Traversal Gaussian Splatting](https://mlsanity.com/paper/2503.12552/) (2025)
- [RadarSplat: Radar Gaussian Splatting for High-Fidelity Data Synthesis and 3D Reconstruction of Autonomous Driving Scenes](https://mlsanity.com/paper/2506.01379/) (2025)
- [Extrapolated Urban View Synthesis Benchmark](https://mlsanity.com/paper/2412.05256/) (2024)
- [CCL-LGS: Contrastive Codebook Learning for 3D Language Gaussian Splatting](https://mlsanity.com/paper/2505.20469/) (2025)
- [Dynamic Scene Reconstruction: Recent Advance in Real-time Rendering and Streaming](https://mlsanity.com/paper/2503.08166/) (2025)
- [Learning-based 3D Reconstruction in Autonomous Driving: A Comprehensive Survey](https://mlsanity.com/paper/2503.14537/) (2025)
- [Flux4D: Flow-based Unsupervised 4D Reconstruction](https://mlsanity.com/paper/2512.03210/) (2025)
- [LiHi-GS: LiDAR-Supervised Gaussian Splatting for Highway Driving Scene Reconstruction](https://mlsanity.com/paper/2412.15447/) (2024)
- [Dust to Tower: Coarse-to-Fine Photo-Realistic Scene Reconstruction from Sparse Uncalibrated Images](https://mlsanity.com/paper/2412.19518/) (2024)
- [RGM: Reconstructing High-fidelity 3D Car Assets with Relightable 3D-GS Generative Model from a Single Image](https://mlsanity.com/paper/2410.08181/) (2024)
- [VR-Drive: Viewpoint-Robust End-to-End Driving with Feed-Forward 3D Gaussian Splatting](https://mlsanity.com/paper/2510.23205/) (2025)
- [ReconDrive: Fast Feed-Forward 4D Gaussian Splatting for Autonomous Driving Scene Reconstruction](https://mlsanity.com/paper/2603.07552/) (2026)
- [Advances in Radiance Field for Dynamic Scene: From Neural Field to Gaussian Field](https://mlsanity.com/paper/2505.10049/) (2025)
- [B'ezierGS: Dynamic Urban Scene Reconstruction with B'ezier Curve Gaussian Splatting](https://mlsanity.com/paper/2506.22099/) (2025)
- [Optimized Minimal 4D Gaussian Splatting](https://mlsanity.com/paper/2510.03857/) (2025)
- [Street Gaussians without 3D Object Tracker](https://mlsanity.com/paper/2412.05548/) (2024)
- [GenAssets: Generating in-the-wild 3D Assets in Latent Space](https://mlsanity.com/paper/2604.23010/) (2026)
- [MADrive: Memory-Augmented Driving Scene Modeling](https://mlsanity.com/paper/2506.21520/) (2025)
- [SaLF: Sparse Local Fields for Multi-Sensor Rendering in Real-Time](https://mlsanity.com/paper/2507.18713/) (2025)
- [FixingGS: Enhancing 3D Gaussian Splatting via Training-Free Score Distillation](https://mlsanity.com/paper/2509.18759/) (2025)
- [D2GS: Dense Depth Regularization for LiDAR-free Urban Scene Reconstruction](https://mlsanity.com/paper/2510.25173/) (2025)
- [IDSplat: Instance-Decomposed 3D Gaussian Splatting for Driving Scenes](https://mlsanity.com/paper/2511.19235/) (2025)

## Abstract (arXiv)

Realistic scene reconstruction and view synthesis are essential for advancing autonomous driving systems by simulating safety-critical scenarios. 3D Gaussian Splatting excels in real-time rendering and static scene reconstructions but struggles with modeling driving scenarios due to complex backgrounds, dynamic objects, and sparse views. We propose AutoSplat, a framework employing Gaussian splatting to achieve highly realistic reconstructions of autonomous driving scenes. By imposing geometric constraints on Gaussians representing the road and sky regions, our method enables multi-view consistent simulation of challenging scenarios including lane changes. Leveraging 3D templates, we introduce a reflected Gaussian consistency constraint to supervise both the visible and unseen side of foreground objects. Moreover, to model the dynamic appearance of foreground objects, we estimate residual spherical harmonics for each foreground Gaussian. Extensive experiments on Pandaset and KITTI demonstrate that AutoSplat outperforms state-of-the-art methods in scene reconstruction and novel view synthesis across diverse driving scenarios. Visit our project page at https://autosplat.github.io/.