We introduce PREDATOR, a model for pairwise pointcloud registration with deep attention
to the overlap region. Different from previous work, our model is specifically designed to …

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 …

We focus on learning descriptive geometry and motion features from 4D point cloud
sequences in this work. Existing works usually develop generic 4D learning tools without …

Registration of distant outdoor LiDAR point clouds is crucial to extending the 3D vision of
collaborative autonomous vehicles, and yet is challenging due to small overlapping area …

The paper discusses the issue of using artificial neural networks and point clouds for
calculating displacements of cultural heritage structures. The model trained on laboratory …

Due to errors in sensors and positioning, there exist mismatches between different phases of
mobile laser scanning point clouds, which impede the application of point cloud, such as …

Registration of point clouds collected from a pair of distant vehicles provides a
comprehensive and accurate 3D view of the driving scenario which is vital for driving safety …

Areal deformation monitoring based on point clouds can be a very valuable alternative to the
established point-based monitoring techniques, especially for deformation monitoring of …

Due to the increased use of areal measurement techniques, such as laser scanning in
geodetic monitoring tasks, areal analysis strategies have considerably gained in importance …

This paper presents a simple yet very effective data-driven approach to fuse both low-level
and high-level local geometric features for 3D rigid data matching. It is a common practice to …