PCKRF：面向6D位姿估计的融合数据点云补全与关键点精修
PCKRF: Point Cloud Completion and Keypoint Refinement With Fusion Data for 6D Pose Estimation
AI总结：
针对传统点云配准位姿精修方法在初始位姿精度提升后效果减弱的问题，提出包含位姿敏感点云补全与颜色辅助迭代关键点配准的PCKRF流水线，可集成于现有6D位姿估计方法提升精度，在高初始精度、无纹理对称物体场景下表现优异。
摘要：
ICP（迭代最近点）及其变体等具备可控位姿精修幅度的鲁棒点云配准方法，常被用于提升6D位姿估计精度。然而，随着深度学习技术的发展与初始位姿精度的提升，这类方法的效果逐渐减弱，核心原因在于它们缺乏针对位姿精修的专门设计。本文提出了PCKRF（融合数据点云补全与关键点精修），一种用于6D位姿估计的新型位姿精修流水线。该流水线包含两个步骤：首先，通过一种新型位姿敏感点云补全网络对输入点云进行补全，该网络在点云补全过程中同时融合了携带位姿信息的局部与全局特征；随后，通过本文提出的CIKP（颜色辅助迭代关键点）方法，将补全后的目标物体点云与对应目标点云进行配准，CIKP方法在配准中引入颜色信息，并围绕每个关键点进行点云配准以提升稳定性。PCKRF流水线可与现有主流6D位姿估计方法（如全流双向融合网络）集成，进一步提升其位姿估计精度。实验表明，在优化精度相对较高的初始位姿时，本文方法相比现有方案具备更优的稳定性。值得注意的是，结果显示该方法可有效补充绝大多数现有位姿估计技术，在多数场景下实现性能提升。此外，即使在涉及无纹理、对称物体的挑战性场景中，该方法仍能取得可观效果。本文源代码可在https://github.com/zhanhz/KRF 获取。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
