To reduce the cost of storing, processing, and visualizing a large-scale point cloud, we
propose a randomized resampling strategy that selects a representative subset of points …

Filters are fundamental in extracting information from data. For time series and image data
that reside on Euclidean domains, filters are the crux of many signal processing and …

Graph convolutional neural networks (Graph-CNNs) extend traditional CNNs to handle data
that is supported on a graph. Major challenges when working with data on graphs are that …

As 3D scanning devices and depth sensors mature, point clouds have attracted increasing
attention as a format for 3D object representation, with applications in various fields such as …

We present a review of 3D point cloud processing and learning for autonomous driving. As
one of the most important sensors in autonomous vehicles, light detection and ranging …

We propose a deep autoencoder with graph topology inference and filtering to achieve
compact representations of unorganized 3D point clouds in an unsupervised manner. Many …

In 3D point cloud processing, the spatial continuity of points is convenient for segmenting
point clouds obtained by 3D laser scanners, RGB-D cameras and LiDAR (light detection and …

In point cloud completion task, most previous works fail to deal with diverse corrupted point
clouds with large missing areas. Meanwhile, they are restricted by discrete point clouds …

Accurate observation and comprehension of the surroundings are made possible by 3-D
reconstruction technology. This study suggests a pyramid reference object for a 3-D …

Vertex-domain and temporal-domain smoothness of time-varying graph signals are cardinal
properties that can be exploited for effective graph signal reconstruction from limited …