118 citations · 225 across the 11 of their papers we have counts for
8 papers · 1 filter
Deep Point Set Resampling via Gradient Fields
Haolan Chen, Bi'an Du, Shitong Luo +1
3D point clouds acquired by scanning real-world objects or scenes have found a wide range of applications including immersive telepresence, autonomous driving, surveillance, etc. T…
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…
Score-Based Point Cloud Denoising
Shitong Luo, Wei Hu
Point clouds acquired from scanning devices are often perturbed by noise, which affects downstream tasks such as surface reconstruction and analysis. The distribution of a noisy po…
Self-Supervised Graph Representation Learning via Topology Transformations
Xiang Gao, Wei Hu, Guo-Jun Qi
We present the Topology Transformation Equivariant Representation learning, a general paradigm of self-supervised learning for node representations of graph data to enable the wide…
Generic Reversible Visible Watermarking Via Regularized Graph Fourier Transform Coding
Wenfa Qi, Sirui Guo, Wei Hu
Reversible visible watermarking (RVW) is an active copyright protection mechanism. It not only transparently superimposes copyright patterns on specific positions of digital images…
Diffusion Probabilistic Models for 3D Point Cloud Generation
Shitong Luo, Wei Hu
We present a probabilistic model for point cloud generation, which is fundamental for various 3D vision tasks such as shape completion, upsampling, synthesis and data augmentation.…