Point clouds are an increasingly relevant data type but they are often corrupted by noise. We
propose a deep neural network based on graph-convolutional layers that can elegantly deal …

Identifying an appropriate underlying graph kernel that reflects pairwise similarities is critical
in many recent graph spectral signal restoration schemes, including image denoising …

3D point clouds acquired by scanning real-world objects or scenes have found a wide range
of applications including immersive telepresence, autonomous driving, surveillance, etc …

Photo-realistic point cloud capture and transmission are the fundamental enablers for
immersive visual communication. The coding process of dynamic point clouds, especially …

Point clouds are an increasingly relevant geometric data type but they are often corrupted by
noise and affected by the presence of outliers. We propose a deep learning method that can …

Graph sampling set selection, where a subset of nodes are chosen to collect samples to
reconstruct a smooth graph signal, is a fundamental problem in graph signal processing …

Point cloud is a collection of 3D coordinates that are discrete geometric samples of an
object's 2D surfaces. Using a low-cost 3D scanner to acquire data means that point clouds …

Current point cloud denoising (PCD) models optimize single networks, trying to make their
parameters adaptive to each point in a large pool of point clouds. Such a denoising network …

3D dynamic point clouds provide a natural discrete representation of real-world objects or
scenes in motion, with a wide range of applications in immersive telepresence, autonomous …

Acquiring original point cloud data from 3D sensors is easily affected by the environment
and inherent limitations of the sensor, which inevitably contain noise and outliers. The …