RealityBridge: Bridging Editable 3D Gaussian Splatting Driving Simulations and Real-World Videos
arXiv:2606.16278v1 Announce Type: cross Abstract: Long-tail hazardous scenarios are essential for safety-oriented autonomous driving, yet they are difficult to collect and reproduce at scale. Editable 3D Gaussian Splatting (3DGS) simulation offers a promising alternative by reconstructing real driving scenes and supporting controllable scene editing. However, edited 3DGS-rendered videos still suffer from a significant Sim-to-Real gap, including rendering artifacts, degraded foreground assets, inconsistent illumination, and temporal flickering. Existing restoration and video generation methods
The continuous evolution of AI and computer vision techniques, particularly 3D Gaussian Splatting, is enabling more realistic and controllable synthetic environments for training autonomous systems.
Improving the realism and utility of AI-driven simulations is critical for accelerating the development and validation of autonomous driving systems, especially for rare and hazardous scenarios.
The ability to bridge the Sim-to-Real gap in editable 3DGS simulations will significantly enhance the efficiency and safety of autonomous vehicle development by providing more effective training data.
- · Autonomous vehicle developers
- · AI simulation companies
- · Robotics industry
- · Computer vision researchers
- · Companies relying solely on real-world data collection for edge cases
Further acceleration of autonomous driving technology development due to better synthetic data.
Reduced testing costs and faster deployment timelines for self-driving cars.
Potential for broader application of high-fidelity synthetic environments in other AI training domains beyond autonomous driving.
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Read at arXiv cs.AI