  In this work, we present FFB6D, a Full Flow Bidirectional fusion network
designed for 6D pose estimation from a single RGBD image. Our key insight is
that appearance information in the RGB image and geometry information from the
depth image are two complementary data sources, and it still remains unknown
how to fully leverage them. Towards this end, we propose FFB6D, which learns to
combine appearance and geometry information for representation learning as well
as output representation selection. Specifically, at the representation
learning stage, we build bidirectional fusion modules in the full flow of the
two networks, where fusion is applied to each encoding and decoding layer. In
this way, the two networks can leverage local and global complementary
information from the other one to obtain better representations. Moreover, at
the output representation stage, we designed a simple but effective 3D
keypoints selection algorithm considering the texture and geometry information
of objects, which simplifies keypoint localization for precise pose estimation.
Experimental results show that our method outperforms the state-of-the-art by
large margins on several benchmarks. Code and video are available at
\url{https://github.com/ethnhe/FFB6D.git}.
