  6D pose estimation from a single RGB image is a fundamental task in computer
vision. The current top-performing deep learning-based methods rely on an
indirect strategy, i.e., first establishing 2D-3D correspondences between the
coordinates in the image plane and object coordinate system, and then applying
a variant of the P$n$P/RANSAC algorithm. However, this two-stage pipeline is
not end-to-end trainable, thus is hard to be employed for many tasks requiring
differentiable poses. On the other hand, methods based on direct regression are
currently inferior to geometry-based methods. In this work, we perform an
in-depth investigation on both direct and indirect methods, and propose a
simple yet effective Geometry-guided Direct Regression Network (GDR-Net) to
learn the 6D pose in an end-to-end manner from dense correspondence-based
intermediate geometric representations. Extensive experiments show that our
approach remarkably outperforms state-of-the-art methods on LM, LM-O and YCB-V
datasets. Code is available at https://git.io/GDR-Net.
