  Object pose estimation is a key perceptual capability in robotics. We propose
a fully-convolutional extension of the PoseCNN method, which densely predicts
object translations and orientations. This has several advantages such as
improving the spatial resolution of the orientation predictions -- useful in
highly-cluttered arrangements, significant reduction in parameters by avoiding
full connectivity, and fast inference. We propose and discuss several
aggregation methods for dense orientation predictions that can be applied as a
post-processing step, such as averaging and clustering techniques. We
demonstrate that our method achieves the same accuracy as PoseCNN on the
challenging YCB-Video dataset and provide a detailed ablation study of several
variants of our method. Finally, we demonstrate that the model can be further
improved by inserting an iterative refinement module into the middle of the
network, which enforces consistency of the prediction.
