  We present SynPick, a synthetic dataset for dynamic scene understanding in
bin-picking scenarios. In contrast to existing datasets, our dataset is both
situated in a realistic industrial application domain -- inspired by the
well-known Amazon Robotics Challenge (ARC) -- and features dynamic scenes with
authentic picking actions as chosen by our picking heuristic developed for the
ARC 2017. The dataset is compatible with the popular BOP dataset format. We
describe the dataset generation process in detail, including object arrangement
generation and manipulation simulation using the NVIDIA PhysX physics engine.
To cover a large action space, we perform untargeted and targeted picking
actions, as well as random moving actions. To establish a baseline for object
perception, a state-of-the-art pose estimation approach is evaluated on the
dataset. We demonstrate the usefulness of tracking poses during manipulation
instead of single-shot estimation even with a naive filtering approach. The
generator source code and dataset are publicly available.
