We study the problem of extracting accurate correspondences for point cloud registration.
Recent keypoint-free methods bypass the detection of repeatable keypoints which is difficult …

Despite recent success in incorporating learning into point cloud registration, many works
focus on learning feature descriptors and continue to rely on nearest-neighbor feature …

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 …

Three-dimensional point cloud registration is an important field in computer vision. Recently,
due to the increasingly complex scenes and incomplete observations, many partial-overlap …

An ideal point cloud registration framework should have superior accuracy, acceptable
efficiency, and strong generalizability. However, this is highly challenging since existing …

Loop closure detection is an essential component of simultaneous localization and map**
(SLAM) systems, which reduces the drift accumulated over time. Over the years, several …

We study the problem of extracting accurate correspondences for point cloud registration.
Recent keypoint-free methods have shown great potential through bypassing the detection …

Point cloud registration (PCR) involves determining a rigid transformation that aligns one
point cloud to another. Despite the plethora of outstanding deep learning (DL)-based …

Over the past years, 3D point cloud registration has attracted unprecedented attention.
Researchers develop various approaches to tackle the challenging task, such as …

Global localization plays a critical role in many robot applications. LiDAR-based global
localization draws the community's focus with its robustness against illumination and …