Comprehensive review of deep learning-based 3d point cloud completion processing and analysis

B Fei, W Yang, WM Chen, Z Li, Y Li… - IEEE Transactions …, 2022 - ieeexplore.ieee.org
Point cloud completion is a generation and estimation issue derived from the partial point
clouds, which plays a vital role in the applications of 3D computer vision. The progress of …

Msr-gcn: Multi-scale residual graph convolution networks for human motion prediction

L Dang, Y Nie, C Long, Q Zhang… - Proceedings of the IEEE …, 2021 - openaccess.thecvf.com
Human motion prediction is a challenging task due to the stochasticity and aperiodicity of
future poses. Recently, graph convolutional network has been proven to be very effective to …

Point cloud completion via skeleton-detail transformer

W Zhang, H Zhou, Z Dong, J Liu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
Point cloud shape completion plays a central role in diverse 3D vision and robotics
applications. Early methods used to generate global shapes without local detail refinement …

Dense point cloud completion based on generative adversarial network

M Cheng, G Li, Y Chen, J Chen… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
Point cloud completion aims to reconstruct complete point clouds from partial point clouds,
which is widely used in various fields such as autonomous driving and robotics. Most …

PC2-PU: Patch Correlation and Point Correlation for Effective Point Cloud Upsampling

C Long, WX Zhang, R Li, H Wang, Z Dong… - Proceedings of the 30th …, 2022 - dl.acm.org
Point cloud upsampling is to densify a sparse point set acquired from 3D sensors, providing
a denser representation for the underlying surface. Existing methods divide the input points …

Point Cloud Completion: A Survey

KW Tesema, L Hill, MW Jones… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Point cloud completion is the task of producing a complete 3D shape given an input of a
partial point cloud. It has become a vital process in 3D computer graphics, vision and …

[HTML][HTML] Deep-learning-based point cloud completion methods: A review

K Zhang, A Zhang, X Wang, W Li - Graphical Models, 2024 - Elsevier
Point cloud completion aims to utilize algorithms to repair missing parts in 3D data for high-
quality point clouds. This technology is crucial for applications such as autonomous driving …

SD-Net: Spatially-Disentangled Point Cloud Completion Network

J Chen, Y Liu, Y Liang, D Long, X He, R Li - Proceedings of the 31st …, 2023 - dl.acm.org
Point clouds obtained from 3D scanning are typically incomplete, noisy, and sparse.
Previous completion methods aim to generate complete point clouds, while taking into …

3D-ARNet: An accurate 3D point cloud reconstruction network from a single-image

H Chen, Y Zuo - Multimedia Tools and Applications, 2022 - Springer
Generating a more realistic 3D reconstruction point cloud is an ill-posed problem. It is a
challenging task to infer 3D shape from a single image. In this paper, a two-stage training …

Attention models for point clouds in deep learning: a survey

X Wang, Y Jin, Y Cen, T Wang, Y Li - arXiv preprint arXiv:2102.10788, 2021 - arxiv.org
Recently, the advancement of 3D point clouds in deep learning has attracted intensive
research in different application domains such as computer vision and robotic tasks …