Marcus R, Vogel C, Jatzkowski I, Knoop N, Stamminger M (2026)
Publication Type: Conference contribution
Publication year: 2026
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
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.
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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