Synth it Like KITTI: Synthetic Data Generation for Object Detection in Driving Scenarios

Marcus R, Vogel C, Jatzkowski I, Knoop N, Stamminger M (2026)


Publication Type: Conference contribution

Publication year: 2026

Journal

Publisher: Springer Science and Business Media Deutschland GmbH

Book Volume: 2629 CCIS

Pages Range: 414-432

Conference Proceedings Title: Communications in Computer and Information Science

Event location: Porto, PRT

ISBN: 9783032009852

DOI: 10.1007/978-3-032-00986-9_27

Abstract

An important factor in advancing autonomous driving systems is simulation. Yet, there is rather small progress for transferability between the virtual and real world. We revisit this problem for 3D object detection on LiDAR point clouds and propose a dataset generation pipeline based on the CARLA simulator. Utilizing domain randomization strategies and careful modeling, we are able to train an object detector on the synthetic data and demonstrate strong generalization capabilities to the KITTI dataset. Furthermore, we compare different virtual sensor variants to gather insights, which sensor attributes can be responsible for the prevalent domain gap. Finally, fine-tuning with a small portion of real data almost matches the baseline and with the full training set slightly surpasses it.

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How to cite

APA:

Marcus, R., Vogel, C., Jatzkowski, I., Knoop, N., & Stamminger, M. (2026). Synth it Like KITTI: Synthetic Data Generation for Object Detection in Driving Scenarios. In Juha Röning, Joaquim Filipe (Eds.), Communications in Computer and Information Science (pp. 414-432). Porto, PRT: Springer Science and Business Media Deutschland GmbH.

MLA:

Marcus, Richard, et al. "Synth it Like KITTI: Synthetic Data Generation for Object Detection in Driving Scenarios." Proceedings of the 5th International Conference on Robotics, Computer Vision and Intelligent Systems, ROBOVIS 2025, Porto, PRT Ed. Juha Röning, Joaquim Filipe, Springer Science and Business Media Deutschland GmbH, 2026. 414-432.

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