Realistically distributing object placements in synthetic training data
  improves the performance of vision-based object detection models
Setareh Dabiri Vasileios Lioutas Berend Zwartsenberg Yunpeng Liu Matthew Niedoba Xiaoxuan Liang Dylan Green Justice Sefas Jonathan Wilder Lavington Frank Wood Adam Scibior
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.