  Some robust point cloud registration approaches with controllable pose
refinement magnitude, such as ICP and its variants, are commonly used to
improve 6D pose estimation accuracy. However, the effectiveness of these
methods gradually diminishes with the advancement of deep learning techniques
and the enhancement of initial pose accuracy, primarily due to their lack of
specific design for pose refinement. In this paper, we propose Point Cloud
Completion and Keypoint Refinement with Fusion Data (PCKRF), a new pose
refinement pipeline for 6D pose estimation. The pipeline consists of two steps.
First, it completes the input point clouds via a novel pose-sensitive point
completion network. The network uses both local and global features with pose
information during point completion. Then, it registers the completed object
point cloud with the corresponding target point cloud by our proposed Color
supported Iterative KeyPoint (CIKP) method. The CIKP method introduces color
information into registration and registers a point cloud around each keypoint
to increase stability. The PCKRF pipeline can be integrated with existing
popular 6D pose estimation methods, such as the full flow bidirectional fusion
network, to further improve their pose estimation accuracy. Experiments
demonstrate that our method exhibits superior stability compared to existing
approaches when optimizing initial poses with relatively high precision.
Notably, the results indicate that our method effectively complements most
existing pose estimation techniques, leading to improved performance in most
cases. Furthermore, our method achieves promising results even in challenging
scenarios involving textureless and symmetrical objects. Our source code is
available at https://github.com/zhanhz/KRF.
