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.

Next best view (NBV) is a technology that finds the best view sequence for sensor to perform scanning based on partial information, which is the core part for robot active reconstruction. Traditional works are mostly based on the evaluation of candidate views through time-consuming volu-metric transformation and ray casting, which heavily limits the applications of NBV. Recent deep learning based NBV methods aim to approximately learn the evaluation function by large-scale training, and improve both the effectiveness and efficiency of NBV. However, these methods force the network to regress the exact groundtruth value of each candidate view, which is much harder than simply ranking all the candidate views. Besides, most previous NBV works assume perfect sensing and perform in simulation environments, lacking real application abilities. In this paper, we propose a novel double branch NBV network, DB-NBV, to utilize the ranking process together with the evaluation process. We further design a real NBV robot and a pipeline to conduct real active reconstruction. Experiments on both simulation and real robot show that our method achieves the best performance and can be applied to real application with high accuracy and speed.