  The conventional pose estimation of a 3D object usually requires the
knowledge of the 3D model of the object. Even with the recent development in
convolutional neural networks (CNNs), a 3D model is often necessary in the
final estimation. In this paper, we propose a two-stage pipeline that takes in
raw colored point cloud data and estimates an object's translation and rotation
by running 3D convolutions on voxels. The pipeline is simple yet highly
accurate: translation error is reduced to the voxel resolution (around 1 cm)
and rotation error is around 5 degrees. The pipeline is also put to actual
robotic grasping tests where it achieves above 90% success rate for test
objects. Another innovation is that a motion capture system is used to
automatically label the point cloud samples which makes it possible to rapidly
collect a large amount of highly accurate real data for training the neural
networks.
