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 …

This study had two main aims:(1) to provide a comprehensive review of terrestrial laser
scanner (TLS) point cloud registration methods and a better understanding of their strengths …

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

Extracting robust and general 3D local features is key to downstream tasks such as point
cloud registration and reconstruction. Existing learning-based local descriptors are either …

Extracting geometric features from 3D scans or point clouds is the first step in applications
such as registration, reconstruction, and tracking. State-of-the-art methods require …

Recently, deep learning based point cloud descriptors have achieved impressive results in
the place recognition task. Nonetheless, due to the sparsity of point clouds, how to extract …

Registration performs an individual and deciding role in multiple intelligent transport
systems. The advancement of deep-learning-based methods enhances the robustness and …

Chamfer Distance (CD) and Earth Mover's Distance (EMD) are two broadly adopted metrics
for measuring the similarity between two point sets. However, CD is usually insensitive to …

We present RoReg, a novel point cloud registration framework that fully exploits oriented
descriptors and estimated local rotations in the whole registration pipeline. Previous …

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 …