Graph signal processing: Overview, challenges, and applications
Research in graph signal processing (GSP) aims to develop tools for processing data
defined on irregular graph domains. In this paper, we first provide an overview of core ideas …
defined on irregular graph domains. In this paper, we first provide an overview of core ideas …
Learning graphs from data: A signal representation perspective
The construction of a meaningful graph topology plays a crucial role in the effective
representation, processing, analysis, and visualization of structured data. When a natural …
representation, processing, analysis, and visualization of structured data. When a natural …
Rgcnn: Regularized graph cnn for point cloud segmentation
Point cloud, an efficient 3D object representation, has become popular with the development
of depth sensing and 3D laser scanning techniques. It has attracted attention in various …
of depth sensing and 3D laser scanning techniques. It has attracted attention in various …
Graph spectral image processing
Recent advent of graph signal processing (GSP) has spurred intensive studies of signals
that live naturally on irregular data kernels described by graphs (eg, social networks …
that live naturally on irregular data kernels described by graphs (eg, social networks …
Optimized skeleton-based action recognition via sparsified graph regression
With the prevalence of accessible depth sensors, dynamic human body skeletons have
attracted much attention as a robust modality for action recognition. Previous methods model …
attracted much attention as a robust modality for action recognition. Previous methods model …
Deep graph-convolutional image denoising
Non-local self-similarity is well-known to be an effective prior for the image denoising
problem. However, little work has been done to incorporate it in convolutional neural …
problem. However, little work has been done to incorporate it in convolutional neural …
Graph Signal Processing: History, development, impact, and outlook
Signal processing (SP) excels at analyzing, processing, and inferring information defined
over regular (first continuous, later discrete) domains such as time or space. Indeed, the last …
over regular (first continuous, later discrete) domains such as time or space. Indeed, the last …
Laplacian matrix learning for point cloud attribute compression with ternary search-based adaptive block partition
C Peng, W Gao - Proceedings of the 32nd ACM International …, 2024 - dl.acm.org
Graph Fourier Transform (GFT) has demonstrated significant effectiveness in point cloud
attribute compression task. However, existing graph modeling methods are based on the …
attribute compression task. However, existing graph modeling methods are based on the …
Imperceptible transfer attack and defense on 3d point cloud classification
Although many efforts have been made into attack and defense on the 2D image domain in
recent years, few methods explore the vulnerability of 3D models. Existing 3D attackers …
recent years, few methods explore the vulnerability of 3D models. Existing 3D attackers …
Feature graph learning for 3D point cloud denoising
Identifying an appropriate underlying graph kernel that reflects pairwise similarities is critical
in many recent graph spectral signal restoration schemes, including image denoising …
in many recent graph spectral signal restoration schemes, including image denoising …