ES6D: A Computation Efficient and Symmetry-Aware 6D Pose Regression Framework

Ningkai Mo, Wanshui Gan, Naoto Yokoya, Shifeng Chen

Abstract:

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 TLESS datasets demonstrate the proposed framework’s substantially superior performance in top accuracy and low
computational cost