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Existing refinement methods gradually lose their ability to further improvepose estimation methods' accuracy. In this paper, we propose a new refinementpipeline, Keypoint Refinement with Fusion Network (KRF), for 6D poseestimation, especially for objects with serious occlusion. The pipelineconsists of two steps. It first completes the input point clouds via a novelpoint completion network. The network uses both local and global features,considering the pose information during point completion. Then, it registersthe completed object point cloud with corresponding target point cloud by Colorsupported Iterative KeyPoint (CIKP). The CIKP method introduces colorinformation into registration and registers point cloud around each keypoint toincrease stability. The KRF pipeline can be integrated with existing popular 6Dpose estimation methods, e.g. the full flow bidirectional fusion network, tofurther improved their pose estimation accuracy. Experiments show that ourmethod outperforms the state-of-the-art method from 93.9\% to 94.4\% onYCB-Video dataset and from 64.4\% to 66.8\% on Occlusion LineMOD dataset. Oursource code is available at https://github.com/zhanhz/KRF.

KRF: Keypoint Refinement with Fusion Network for 6D Pose Estimation