Video Killed the HD-Map: Predicting Driving Behavior Directly From Drone Images
Published in CVPR 2023 Workshop-2023, 2023
Recommended citation: Setareh, Dabiri, et al. "Realistically distributing object placements in synthetic training data improves the performance of vision-based object detection models" CVPR 2023 Workshop 2023. https://arxiv.org/abs/2305.14621
When training object detection models on synthetic data, it is important to make the distribution of synthetic data as close as possible to the distribution of real data. We investigate specifically the impact of object placement distribution, keeping all other aspects of synthetic data fixed. Our experiment, training a 3D vehicle detection model in CARLA and testing on KITTI, demonstrates a substantial improvement resulting from improving the object placement distribution.
@misc{dabiri2023realistically,
title={Realistically distributing object placements in synthetic training data improves the performance of vision-based object detection models},
author={Setareh Dabiri and Vasileios Lioutas and Berend Zwartsenberg and Yunpeng Liu and Matthew Niedoba and Xiaoxuan Liang and Dylan Green and Justice Sefas and Jonathan Wilder Lavington and Frank Wood and Adam Scibior},
year={2023},
eprint={2305.14621},
archivePrefix={arXiv},
primaryClass={cs.CV}
}