The 3D point cloud (3DPC) has significantly evolved and benefited from the advance of
deep learning (DL). However, the latter faces various issues, including the lack of data or …

Point clouds acquired from scanning devices are often perturbed by noise, which affects
downstream tasks such as surface reconstruction and analysis. The distribution of a noisy …

Graph neural networks (GNNs) conduct feature learning by taking into account the local
structure preservation of the data to produce discriminative features, but need to address the …

3D point clouds are often perturbed by noise due to the inherent limitation of acquisition
equipments, which obstructs downstream tasks such as surface reconstruction, rendering …

Over the past decade, we have witnessed an enormous amount of research effort dedicated
to the design of point cloud denoising techniques. In this article, we first provide a …

The captured 3D point clouds by depth cameras and 3D scanners are often corrupted by
noise, so point cloud denoising is typically required for downstream applications. We …

With the advancement of 3D sensing technologies, point clouds are gradually becoming the
main type of data representation in applications such as autonomous driving, robotics, and …

Geometric data acquired from real-world scenes, eg, 2D depth images, 3D point clouds, and
4D dynamic point clouds, have found a wide range of applications including immersive …

Point normal, as an intrinsic geometric property of 3D objects, not only serves conventional
geometric tasks such as surface consolidation and reconstruction, but also facilitates cutting …

To address the point cloud quality assessment (PCQA) problem, GraphSIM was proposed
via jointly considering geometrical and color features, which shows compelling performance …