SynthDrive: Scalable Real2Sim2Real Sensor Simulation Pipeline for High-Fidelity Asset Generation and Driving Data Synthesis

In the field of autonomous driving, sensor simulation is essential for generating rare and diverse scenarios that are difficult to capture in real-world environments. Current solutions fall into two categories: 1) CG-based methods, such as CARLA, which lack diversity and struggle to scale to the vast array of rare cases required for robust perception training; and 2) learning-based approaches, such as NeuSim, which are limited to specific object categories (vehicles) and require extensive multi-sensor data, hindering their applicability to generic objects. To address these limitations, we propose SynthDrive, a scalable "real2sim2real" system that leverages 3D generation to automate asset mining, generation, and rare-case data synthesis.Our framework introduces two key innovations: 1) Automated Rare-Case Mining and Synthesis. Given a text prompt describing specific objects, SynthDrive automatically mines image data from the Internet and then generates corresponding high-fidelity 3D assets, which eliminates the need for costly manual data collection. By integrating these assets into existing street-view data, our pipeline produces photorealistic rare-case data, supporting rapid scaling to diverse assets including irregular obstacles and temporary traffic facilities. 2) High-Fidelity 3D Generation. We propose a hybrid asset generation pipeline that combines a geometry-aware LRM, iterative mesh optimization, and an improved texture fusion algorithm. Our approach achieves 0.0164 Chamfer Distance on the GSO dataset, outperforming InstantMesh by 14.1% in geometry accuracy, and achieves 19.05 PSNR (vs 16.84) for texture quality. This enables fine geometry details and high-resolution texture generation, which is essential for perception model training. Experiments demonstrate that SynthDrive-generated data improves the performance of downstream perception tasks (2D and 3D detection on rare objects) by 2-4% mAP. SynthDrive greatly lowers the data production cost and improves the diversity for corner-case data generation, showcasing extensive potential applications in the field of autonomous driving.