  We present a learning-based method for 6 DoF pose estimation of rigid objects
in point cloud data. Many recent learning-based approaches use primarily RGB
information for detecting objects, in some cases with an added refinement step
using depth data. Our method consumes unordered point sets with/without RGB
information, from initial detection to the final transformation estimation
stage. This allows us to achieve accurate pose estimates, in some cases
surpassing state of the art methods trained on the same data.
