Point cloud sampling is a less explored research topic for this data representation. The most
commonly used sampling methods are still classical random sampling and farthest point …

Recently many approaches directly operate on point clouds for different tasks. These
approaches become more computation and storage demanding when point cloud size is …

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 …

Downsampling is a crucial task for processing large scale and/or dense point clouds with
limited resources. Owing to the development of deep learning, approaches of task-oriented …

Processing large point clouds is a challenging task. Therefore, the data is often
downsampled to a smaller size such that it can be stored, transmitted and processed more …

Point cloud is a discrete and unordered expression of 3D data. A lot of methods have been
proposed to solve the problem in 3D object classification and scene recognition. To handle …

This paper proposes an innovative approach to H ierarchical E dge A ware 3D Point Cloud
Learning (HEA-Net) that seeks to address the challenges of noise in point cloud data, and …

Abstract 3D point cloud has tremendous potential in many application tasks. However, the
huge amount of data limits this potential. To simplify point clouds and improve their …

Light detection and ranging (LiDAR) has become one of the most important sensors in 3-D
perception. With the advancement of sensor technology, the point cloud data generated by …

Point cloud downsampling is a crucial pre-processing operation to downsample points in
order to unify data size and reduce computational cost, to name a few. Recent research on …