  This paper addresses the task of estimating the 6 degrees of freedom pose of
a known 3D object from depth information represented by a point cloud. Deep
features learned by convolutional neural networks from color information have
been the dominant features to be used for inferring object poses, while depth
information receives much less attention. However, depth information contains
rich geometric information of the object shape, which is important for
inferring the object pose. We use depth information represented by point clouds
as the input to both deep networks and geometry-based pose refinement and use
separate networks for rotation and translation regression. We argue that the
axis-angle representation is a suitable rotation representation for deep
learning, and use a geodesic loss function for rotation regression. Ablation
studies show that these design choices outperform alternatives such as the
quaternion representation and L2 loss, or regressing translation and rotation
with the same network. Our simple yet effective approach clearly outperforms
state-of-the-art methods on the YCB-video dataset. The implementation and
trained model are avaliable at: https://github.com/GeeeG/CloudPose.
