84 citations · 90 across the 3 of their papers we have counts for
6 papers
Learning Latent Part-Whole Hierarchies for Point Clouds
Xiang Gao, Wei Hu, Renjie Liao
Strong evidence suggests that humans perceive the 3D world by parsing visual scenes and objects into part-whole hierarchies. Although deep neural networks have the capability of le…
Self-Contrastive Learning with Hard Negative Sampling for Self-supervised Point Cloud Learning
Bi'an Du, Xiang Gao, Wei Hu +1
Point clouds have attracted increasing attention. Significant progress has been made in methods for point cloud analysis, which often requires costly human annotation as supervisio…
Self-Supervised Multi-View Learning via Auto-Encoding 3D Transformations
Xiang Gao, Wei Hu, Guo-Jun Qi
3D object representation learning is a fundamental challenge in computer vision to infer about the 3D world. Recent advances in deep learning have shown their efficiency in 3D obje…
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…
Feature Graph Learning for 3D Point Cloud Denoising
Wei Hu, Xiang Gao, Gene Cheung +1
Identifying an appropriate underlying graph kernel that reflects pairwise similarities is critical in many recent graph spectral signal restoration schemes, including image denoisi…
3D Dynamic Point Cloud Denoising via Spatial-Temporal Graph Learning
Wei Hu, Qianjiang Hu, Zehua Wang +1
The prevalence of accessible depth sensing and 3D laser scanning techniques has enabled the convenient acquisition of 3D dynamic point clouds, which provide efficient representatio…