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 …

LiDAR has gained popularity in autonomous driving due to advantages like long
measurement distance, rich three-dimensional information, and stability in harsh …

As a fundamental and critical task in various visual applications, image matching can identify
then correspond the same or similar structure/content from two or more images. Over the …

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

A successful point cloud registration often lies on robust establishment of sparse matches
through discriminative 3D local features. Despite the fast evolution of learning-based 3D …

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 …

This work focuses on mitigating two limitations in the joint learning of local feature detectors
and descriptors. First, the ability to estimate the local shape (scale, orientation, etc.) of …

Establishing correspondences between two images requires both local and global spatial
context. Given putative correspondences of feature points in two views, in this paper, we …

Most existing studies on learning local features focus on the patch-based descriptions of
individual keypoints, whereas neglecting the spatial relations established from their keypoint …

We present 3DRegNet, a novel deep learning architecture for the registration of 3D scans.
Given a set of 3D point correspondences, we build a deep neural network to address the …