mlsanityData to 14 Oct 2026

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

arXiv 2509.06798 ↗First posted 8 Sep 2025cs.CVcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 54 of its 62 references that are in the map. First posted on arXiv 8 Sep 2025.

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54 of 62 references.

References · 54

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38 more
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Citing papers per year: 2026: 1.

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  1. 2026In-the-Wild Camouflage Attack on Vehicle Detectors through Controllable Image Editingcited by 0