activity
20182020
most citedDifferentiable Manifold Reconstruction for Point Cloud Denoising

118 citations · 127 across the 4 of their papers we have counts for

collaborators

12 papers

cs.CV2020118 cited

Differentiable Manifold Reconstruction for Point Cloud Denoising

Shitong Luo, Wei Hu

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 an…

cs.CV20204 cited

Edge-aware Graph Representation Learning and Reasoning for Face Parsing

Gusi Te, Yinglu Liu, Wei Hu +2

Face parsing infers a pixel-wise label to each facial component, which has drawn much attention recently. Previous methods have shown their efficiency in face parsing, which howeve…

cs.LG2020

Graph Metric Learning via Gershgorin Disc Alignment

Cheng Yang, Gene Cheung, Wei Hu

We propose a fast general projection-free metric learning framework, where the minimization objective is a convex differentiable f…

cs.CV2019

GraphTER: Unsupervised Learning of Graph Transformation Equivariant Representations via Auto-Encoding Node-wise Transformations

Xiang Gao, Wei Hu, Guo-Jun Qi

Recent advances in Graph Convolutional Neural Networks (GCNNs) have shown their efficiency for non-Euclidean data on graphs, which often require a large amount of labeled data with…

cs.LG2019

Joint Learning of Graph Representation and Node Features in Graph Convolutional Neural Networks

Jiaxiang Tang, Wei Hu, Xiang Gao +1

Graph Convolutional Neural Networks (GCNNs) extend classical CNNs to graph data domain, such as brain networks, social networks and 3D point clouds. It is critical to identify an a…

cs.MM2019

Predictive Generalized Graph Fourier Transform for Attribute Compression of Dynamic Point Clouds

Yiqun Xu, Wei Hu, Shanshe Wang +5

As 3D scanning devices and depth sensors advance, dynamic point clouds have attracted increasing attention as a format for 3D objects in motion, with applications in various fields…