Point cloud registration is a research field where the spatial relationship between two or
more sets of points in space is determined. Point clouds are found in multiple applications …

Removing outlier correspondences is one of the critical steps for successful feature-based
point cloud registration. Despite the increasing popularity of introducing deep learning …

Rigid registration is a transformation estimation problem between two point clouds. The two
point clouds captured may partially overlap owing to different viewpoints and acquisition …

Learning-based outlier (mismatched correspondence) rejection for robust 3D registration
generally formulates the outlier removal as an inlier/outlier classification problem. The core …

With the development of 3D matching technology, correspondence-based point cloud
registration gains more attention. Unfortunately, 3D keypoint techniques inevitably produce …

In this paper, an improved iterative closest point (ICP) algorithm based on the curvature
feature similarity of the point cloud is proposed to improve the performance of classic ICP …

With the advancement of 3D sensing technologies, point clouds are gradually becoming the
main type of data representation in applications such as autonomous driving, robotics, and …

Point cloud registration (PCR) is an important technique of 3D vision, which has been widely
applied in many areas such as robotics and photogrammetry. The iterative closest point …

Feature matching, which aims at seeking dependable correspondences between two sets of
features, is of considerable significance to various vision-based tasks. This paper attempts to …

Multi-instance point cloud registration is the problem of estimating multiple poses of source
point cloud instances within a target point cloud. Solving this problem is challenging since …