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arXiv · 2509.06798

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

Zhengqing Chen·Ruohong Mei·Xiaoyang Guo·Qingjie Wang·Yubin Hu·Wei Yin·Weiqiang Ren·Qian Zhang
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Abstract

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 a scalable real2sim2real system that leverages 3D generation to automate asset mining, generation, and rare-case data synthesis.

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Zhengqing Chen, Ruohong Mei, Xiaoyang Guo, Qingjie Wang, Yubin Hu, Wei Yin, Weiqiang Ren, Qian Zhang. 2025-09-08. SynthDrive: Scalable Real2Sim2Real Sensor Simulation Pipeline for High-Fidelity Asset Generation and Driving Data Synthesis. https://arxiv.org/abs/2509.06798

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