  Object 6D pose estimation is an important research topic in the field of
computer vision due to its wide application requirements and the challenges
brought by complexity and changes in the real-world. We think fully exploring
the characteristics of spatial relationship between points will help to improve
the pose estimation performance, especially in the scenes of background clutter
and partial occlusion. But this information was usually ignored in previous
work using RGB image or RGB-D data. In this paper, we propose a framework for
6D pose estimation from RGB-D data based on spatial structure characteristics
of 3D keypoints. We adopt point-wise dense feature embedding to vote for 3D
keypoints, which makes full use of the structure information of the rigid body.
After the direction vectors pointing to the keypoints are predicted by CNN, we
use RANSAC voting to calculate the coordinate of the 3D keypoints, then the
pose transformation can be easily obtained by the least square method. In
addition, a spatial dimension sampling strategy for points is employed, which
makes the method achieve excellent performance on small training sets. The
proposed method is verified on two benchmark datasets, LINEMOD and OCCLUSION
LINEMOD. The experimental results show that our method outperforms the
state-of-the-art approaches, achieves ADD(-S) accuracy of 98.7\% on LINEMOD
dataset and 52.6\% on OCCLUSION LINEMOD dataset in real-time.
