  In this paper, we introduce a novel single shot approach for 6D object pose
estimation of rigid objects based on depth images. For this purpose, a fully
convolutional neural network is employed, where the 3D input data is spatially
discretized and pose estimation is considered as a regression task that is
solved locally on the resulting volume elements. With 65 fps on a GPU, our
Object Pose Network (OP-Net) is extremely fast, is optimized end-to-end, and
estimates the 6D pose of multiple objects in the image simultaneously. Our
approach does not require manually 6D pose-annotated real-world datasets and
transfers to the real world, although being entirely trained on synthetic data.
The proposed method is evaluated on public benchmark datasets, where we can
demonstrate that state-of-the-art methods are significantly outperformed.
