3.4k citations · 6.2k across the 136 of their papers we have counts for
18 papers · 1 filter
Learning Category-level Shape Saliency via Deep Implicit Surface Networks
Chaozheng Wu, Lin Sun, Xun Xu +1
This paper is motivated from a fundamental curiosity on what defines a category of object shapes. For example, we may have the common knowledge that a plane has wings, and a chair…
Deep Optimized Priors for 3D Shape Modeling and Reconstruction
Mingyue Yang, Yuxin Wen, Weikai Chen +2
Many learning-based approaches have difficulty scaling to unseen data, as the generality of its learned prior is limited to the scale and variations of the training samples. This h…
Classification of Single-View Object Point Clouds
Zelin Xu, Ke Chen, Kangjun Liu +3
Object point cloud classification has drawn great research attention since the release of benchmarking datasets, such as the ModelNet and the ShapeNet. These benchmarks assume poin…
Sign-Agnostic Implicit Learning of Surface Self-Similarities for Shape Modeling and Reconstruction from Raw Point Clouds
Wenbin Zhao, Jiabao Lei, Yuxin Wen +2
Shape modeling and reconstruction from raw point clouds of objects stand as a fundamental challenge in vision and graphics research. Classical methods consider analytic shape prior…
Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation using Structurally Regularized Deep Clustering
Hui Tang, Xiatian Zhu, Ke Chen +2
Unsupervised domain adaptation (UDA) is to learn classification models that make predictions for unlabeled data on a target domain, given labeled data on a source domain whose dist…
JOLO-GCN: Mining Joint-Centered Light-Weight Information for Skeleton-Based Action Recognition
Jinmiao Cai, Nianjuan Jiang, Xiaoguang Han +2
Skeleton-based action recognition has attracted research attentions in recent years. One common drawback in currently popular skeleton-based human action recognition methods is tha…