  6D object pose estimation is widely applied in robotic tasks such as grasping
and manipulation. Prior methods using RGB-only images are vulnerable to heavy
occlusion and poor illumination, so it is important to complement them with
depth information. However, existing methods using RGB-D data cannot adequately
exploit consistent and complementary information between RGB and depth
modalities. In this paper, we present a novel method to effectively consider
the correlation within and across both modalities with attention mechanism to
learn discriminative and compact multi-modal features. Then, effective fusion
strategies for intra- and inter-correlation modules are explored to ensure
efficient information flow between RGB and depth. To our best knowledge, this
is the first work to explore effective intra- and inter-modality fusion in 6D
pose estimation. The experimental results show that our method can achieve the
state-of-the-art performance on LineMOD and YCB-Video dataset. We also
demonstrate that the proposed method can benefit a real-world robot grasping
task by providing accurate object pose estimation.
