51 citations · 68 across the 6 of their papers we have counts for
6 papers
RIConv++: Effective Rotation Invariant Convolutions for 3D Point Clouds Deep Learning
Zhiyuan Zhang, Binh-Son Hua, Sai-Kit Yeung
3D point clouds deep learning is a promising field of research that allows a neural network to learn features of point clouds directly, making it a robust tool for solving 3D scene…
CVFNet: Real-time 3D Object Detection by Learning Cross View Features
Jiaqi Gu, Zhiyu Xiang, Pan Zhao +4
In recent years 3D object detection from LiDAR point clouds has made great progress thanks to the development of deep learning technologies. Although voxel or point based methods a…
End-to-end Learning the Partial Permutation Matrix for Robust 3D Point Cloud Registration
Zhiyuan Zhang, Jiadai Sun, Yuchao Dai +3
Even though considerable progress has been made in deep learning-based 3D point cloud processing, how to obtain accurate correspondences for robust registration remains a major cha…
Global Context Aware Convolutions for 3D Point Cloud Understanding
Zhiyuan Zhang, Binh-Son Hua, Wei Chen +2
Recent advances in deep learning for 3D point clouds have shown great promises in scene understanding tasks thanks to the introduction of convolution operators to consume 3D point…
Rotation Invariant Convolutions for 3D Point Clouds Deep Learning
Zhiyuan Zhang, Binh-Son Hua, David W. Rosen +1
Recent progresses in 3D deep learning has shown that it is possible to design special convolution operators to consume point cloud data. However, a typical drawback is that rotatio…
ShellNet: Efficient Point Cloud Convolutional Neural Networks using Concentric Shells Statistics
Zhiyuan Zhang, Binh-Son Hua, Sai-Kit Yeung
Deep learning with 3D data has progressed significantly since the introduction of convolutional neural networks that can handle point order ambiguity in point cloud data. While bei…