52 citations · 98 across the 4 of their papers we have counts for
5 papers
Soft Masked Transformer for Point Cloud Processing with Skip Attention-Based Upsampling
Yong He, Hongshan Yu, Chaoxu Mu +5
Point cloud processing methods leverage local and global point features %at the feature level to cater to downstream tasks, yet they often overlook the task-level context inherent…
DANet: Density Adaptive Convolutional Network with Interactive Attention for 3D Point Clouds
Yong He, Hongshan Yu, Zhengeng Yang +3
Local features and contextual dependencies are crucial for 3D point cloud analysis. Many works have been devoted to designing better local convolutional kernels that exploit the co…
Deep Learning Based 3D Segmentation: A Survey
Yong He, Hongshan Yu, Xiaoyan Liu +4
3D segmentation is a fundamental and challenging problem in computer vision with applications in autonomous driving and robotics. It has received significant attention from the com…
Self-supervised Learning with Fully Convolutional Networks
Zhengeng Yang, Hongshan Yu, Yong He +2
Although deep learning based methods have achieved great success in many computer vision tasks, their performance relies on a large number of densely annotated samples that are typ…
Fast ORB-SLAM without Keypoint Descriptors
Qiang Fu, Hongshan Yu, Xiaolong Wang +4
Indirect methods for visual SLAM are gaining popularity due to their robustness to environmental variations. ORB-SLAM2 \cite{orbslam2} is a benchmark method in this domain, however…