mlsanityData to 14 Oct 2026

CRUISE: Cooperative Reconstruction and Editing in V2X Scenarios using Gaussian Splatting

arXiv 2507.18473 ↗First posted 24 Jul 2025cs.CVcited by 11

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Abstract (arXiv)

Vehicle-to-everything (V2X) communication plays a crucial role in autonomous driving, enabling cooperation between vehicles and infrastructure. While simulation has significantly contributed to various autonomous driving tasks, its potential for data generation and augmentation in V2X scenarios remains underexplored. In this paper, we introduce CRUISE, a comprehensive reconstruction-and-synthesis framework designed for V2X driving environments. CRUISE employs decomposed Gaussian Splatting to accurately reconstruct real-world scenes while supporting flexible editing. By decomposing dynamic traffic participants into editable Gaussian representations, CRUISE allows for seamless modification and augmentation of driving scenes. Furthermore, the framework renders images from both ego-vehicle and infrastructure views, enabling large-scale V2X dataset augmentation for training and evaluation. Our experimental results demonstrate that: 1) CRUISE reconstructs real-world V2X driving scenes with high fidelity; 2) using CRUISE improves 3D detection across ego-vehicle, infrastructure, and cooperative views, as well as cooperative 3D tracking on the V2X-Seq benchmark; and 3) CRUISE effectively generates challenging corner cases.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 72 of its 82 references that are in the map. First posted on arXiv 24 Jul 2025.

Builds on

72 of 82 references.

References · 72

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  10. 2023DrivingGaussian: Composite Gaussian Splatting for Surrounding Dynamic Autonomous Driving Scenescited by 360
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  12. 2022V2X-ViT: Vehicle-to-Everything Cooperative Perception with Vision Transformercited by 320
38 more
  1. 2021OPV2V: An Open Benchmark Dataset and Fusion Pipeline for Perception with Vehicle-to-Vehicle Communicationcited by 304
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  3. 2023UniSim: A Neural Closed-Loop Sensor Simulatorcited by 297
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  16. 2022V2X-Sim: Multi-Agent Collaborative Perception Dataset and Benchmark for Autonomous Drivingcited by 157
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  20. 2023Periodic Vibration Gaussian: Dynamic Urban Scene Reconstruction and Real-time Renderingcited by 143
  21. 2024HUGS: Holistic Urban 3D Scene Understanding via Gaussian Splattingcited by 142
  22. 2023NeuSG: Neural Implicit Surface Reconstruction with 3D Gaussian Splatting Guidancecited by 138
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  28. 2023V2X-Seq: A Large-Scale Sequential Dataset for Vehicle-Infrastructure Cooperative Perception and Forecastingcited by 114
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50 most cited of 72 All 72 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 4; 2026: 7.

11 papers.

  1. 2025GaussianArt: Unified Modeling of Geometry and Motion for Articulated Objectscited by 17
  2. 2025GS-Occ3D: Scaling Vision-only Occupancy Reconstruction with Gaussian Splattingcited by 11
  3. 2025Sat2RealCity: Geometry-Aware and Appearance-Controllable 3D Urban Generation from Satellite Imagerycited by 7
  4. 2026ArtifactWorld: Scaling 3D Gaussian Splatting Artifact Restoration via Video Generation Modelscited by 5
  5. 2026You Only Gaussian Once: Controllable 3D Gaussian Splatting for Ultra-Densely Sampled Scenescited by 4
  6. 2026Real2Sim: A Physics-driven and Editable Gaussian Splatting Framework for Autonomous Driving Scenescited by 3
  7. 2025Multimodal Data Storage and Retrieval for Embodied AI: A Surveycited by 2
  8. 2026CARLA-GS: Decoupling Representation, Reasoning, and Physics Simulation for Autonomous Driving Corner-Case Synthesiscited by 2
  9. 2026AdaptSplat: Adapting Vision Foundation Models for Feed-Forward 3D Gaussian Splattingcited by 1
  10. 2026One World, Dual Timeline: Decoupled Spatio-Temporal Gaussian Scene Graph for 4D Cooperative Driving Reconstructioncited by 0
  11. 2026FRUC: Feedforward Dynamic Scene Reconstruction from Uncalibrated Collaborative Driving Viewscited by 0