We present two algorithms for aligning two colored point clouds. The two algorithms are designed to minimize a probabilistic cost based on the color-supported soft matching of points in a point cloud to their K-closest points in the other point cloud. The first algorithm, like prior iterative closest point algorithms, refines the pose parameters to minimize the cost. Assuming that the point clouds are obtained from RGB-depth images, our second algorithm regards the measured depth values as variables and minimizes the cost to obtain refined depth values. Experiments with our synthetic dataset show that our pose refinement algorithm gives better results compared to the existing algorithms. Our depth refinement algorithm is shown to achieve more accurate alignments from the outputs of the pose refinement step. Our algorithms are applied to a real-world dataset, providing accurate and visually improved results.

中文翻译：

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用于彩色点云配准的迭代 K-最近点算法。


我们提出了两种用于对齐两个彩色点云的算法。这两种算法旨在基于点云中的点与另一个点云中的K最近点的颜色支持软匹配来最小化概率成本。第一种算法与之前的迭代最近点算法一样，细化姿态参数以最小化成本。假设点云是从 RGB 深度图像获得的，我们的第二个算法将测量的深度值视为变量，并最小化获得精细深度值的成本。对我们的合成数据集的实验表明，与现有算法相比，我们的姿态细化算法给出了更好的结果。我们的深度细化算法可以从姿势细化步骤的输出中实现更准确的对齐。我们的算法应用于现实世界的数据集，提供准确且视觉上改进的结果。