224 citations · 563 across the 9 of their papers we have counts for
9 papers
4DAC: Learning Attribute Compression for Dynamic Point Clouds
Guangchi Fang, Qingyong Hu, Yiling Xu +1
With the development of the 3D data acquisition facilities, the increasing scale of acquired 3D point clouds poses a challenge to the existing data compression techniques. Although…
Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds
Yifan Zhang, Qingyong Hu, Guoquan Xu +3
We study the problem of efficient object detection of 3D LiDAR point clouds. To reduce the memory and computational cost, existing point-based pipelines usually adopt task-agnostic…
3DAC: Learning Attribute Compression for Point Clouds
Guangchi Fang, Qingyong Hu, Hanyun Wang +2
We study the problem of attribute compression for large-scale unstructured 3D point clouds. Through an in-depth exploration of the relationships between different encoding steps an…
Box2Seg: Learning Semantics of 3D Point Clouds with Box-Level Supervision
Yan Liu, Qingyong Hu, Yinjie Lei +3
Learning dense point-wise semantics from unstructured 3D point clouds with fewer labels, although a realistic problem, has been under-explored in literature. While existing weakly…
Learning Semantic Segmentation of Large-Scale Point Clouds with Random Sampling
Qingyong Hu, Bo Yang, Linhai Xie +5
We study the problem of efficient semantic segmentation of large-scale 3D point clouds. By relying on expensive sampling techniques or computationally heavy pre/post-processing ste…
SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds
Qingyong Hu, Bo Yang, Guangchi Fang +4
Labelling point clouds fully is highly time-consuming and costly. As larger point cloud datasets with billions of points become more common, we ask whether the full annotation is e…
SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration
Sheng Ao, Qingyong Hu, Bo Yang +2
Extracting robust and general 3D local features is key to downstream tasks such as point cloud registration and reconstruction. Existing learning-based local descriptors are either…
Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs
Ruigang Fu, Qingyong Hu, Xiaohu Dong +3
To have a better understanding and usage of Convolution Neural Networks (CNNs), the visualization and interpretation of CNNs has attracted increasing attention in recent years. In…
RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds
Qingyong Hu, Bo Yang, Linhai Xie +5
We study the problem of efficient semantic segmentation for large-scale 3D point clouds. By relying on expensive sampling techniques or computationally heavy pre/post-processing st…