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

• URL: http://arxiv.org/abs/2509.06798v1
• Date: Mon, 08 Sep 2025 15:29:49 GMT
• Title: SynthDrive: Scalable Real2Sim2Real Sensor Simulation Pipeline for High-Fidelity Asset Generation and Driving Data Synthesis
• Authors: Zhengqing Chen, Ruohong Mei, Xiaoyang Guo, Qingjie Wang, Yubin Hu, Wei Yin, Weiqiang Ren, Qian Zhang,
• Abstract summary: We propose a scalable real2sim2real system that leverages 3D generation to automate asset mining, generation, and rare-case data synthesis.<n>In this paper, we show how real2sim2real can scale to the vast array of rare cases required for robust perception training.
• Score: 13.334087135075313
• License: http://creativecommons.org/licenses/by/4.0/
• 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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