  In this paper, a computation efficient regression framework is presented for
estimating the 6D pose of rigid objects from a single RGB-D image, which is
applicable to handling symmetric objects. This framework is designed in a
simple architecture that efficiently extracts point-wise features from RGB-D
data using a fully convolutional network, called XYZNet, and directly regresses
the 6D pose without any post refinement. In the case of symmetric object, one
object has multiple ground-truth poses, and this one-to-many relationship may
lead to estimation ambiguity. In order to solve this ambiguity problem, we
design a symmetry-invariant pose distance metric, called average (maximum)
grouped primitives distance or A(M)GPD. The proposed A(M)GPD loss can make the
regression network converge to the correct state, i.e., all minima in the
A(M)GPD loss surface are mapped to the correct poses. Extensive experiments on
YCB-Video and T-LESS datasets demonstrate the proposed framework's
substantially superior performance in top accuracy and low computational cost.
