Registration is a transformation estimation problem between two point clouds, which has a
unique and critical role in numerous computer vision applications. The developments of …

Abstract Three-dimensional (3D) point cloud registration is a fundamental step for many 3D
modeling and mapping applications. Existing approaches are highly disparate in the data …

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 …

Abstract 3D point cloud registration is a fundamental problem in computer vision and
robotics. Recently, learning-based point cloud registration methods have made great …

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 …

Point cloud registration is a key task in many computational fields. Previous correspondence
matching based methods require the inputs to have distinctive geometric structures to fit a …

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

Point cloud registration is a fundamental problem in 3D computer vision. Outdoor LiDAR
point clouds are typically large-scale and complexly distributed, which makes the …

Deep point cloud registration methods face challenges to partial overlaps and rely on
labeled data. To address these issues, we propose UDPReg, an unsupervised deep …

In this paper, we propose a novel local descriptor-based framework, called You Only
Hypothesize Once (YOHO), for the registration of two unaligned point clouds. In contrast to …