A point cloud is a set of points defined in a 3D metric space. Point clouds have become one
of the most significant data formats for 3D representation and are gaining increased …

The topic of this review is geometric registration in robotics. Registration algorithms
associate sets of data into a common coordinate system. They have been used extensively …

As a fundamental problem in computer vision, 3D point cloud registration (PCR) aims to
seek the optimal pose to align a point cloud pair. In this paper, we present a 3D registration …

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

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

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 …

The iterative closest point (ICP) algorithm and its variants are a fundamental technique for
rigid registration between two point sets, with wide applications in different areas from …

We propose 3DSmoothNet, a full workflow to match 3D point clouds with a siamese deep
learning architecture and fully convolutional layers using a voxelized smoothed density …

Simultaneous localization and map** (SLAM) consists in the concurrent construction of a
model of the environment (the map), and the estimation of the state of the robot moving …