Point cloud sampling is crucial for efficient large-scale point cloud analysis, where learning-
to-sample methods have recently received increasing attention from the community for …

In spite of increasing point density and accuracy, airborne lidar point clouds often exhibit
point density variations. Some of these density variations indicate issues with point clouds …

A point cloud as an information-intensive 3D representation usually requires a large amount
of transmission, storage and computing resources, which seriously hinder its usage in many …

With the development of 3D sensing technologies, point clouds have attracted increasing
attention in a variety of applications for 3D object representation, such as autonomous …

With the development of 3D scanning technology, a huge volume of point cloud data has
been collected at a lower cost. The huge data set is the main burden during the data …

With the development of modern 3D measurement technologies, it becomes easy to capture
dense point cloud datasets. To settle the problem of pruning the redundant points and fast …

We propose a new approach for automatic refinement of unorganized point clouds captured
by LiDAR scanning systems. Given a point cloud, our method first abstracts the input data …

Point cloud (PC)—a collection of discrete geometric samples of a 3D object's surface—is
typically large, which entails expensive subsequent operations. Thus, PC sub-sampling is of …

We develop a phase-resolved wave field reconstruction method by the learning-based
downsampling network for processing large amounts of inhomogeneous data from non …

Due to the huge number of points on three-dimensional point clouds captured by optical
scanning devices, point-based simplification is a crucial step in model reconstruction …