43 citations · 221 across the 18 of their papers we have counts for
7 papers · 1 filter
GeoTransformer: Fast and Robust Point Cloud Registration with Geometric Transformer
Zheng Qin, Hao Yu, Changjian Wang +5
We study the problem of extracting accurate correspondences for point cloud registration. Recent keypoint-free methods have shown great potential through bypassing the detection of…
Deep Graph-based Spatial Consistency for Robust Non-rigid Point Cloud Registration
Zheng Qin, Hao Yu, Changjian Wang +2
We study the problem of outlier correspondence pruning for non-rigid point cloud registration. In rigid registration, spatial consistency has been a commonly used criterion to disc…
Geometric Transformer for Fast and Robust Point Cloud Registration
Zheng Qin, Hao Yu, Changjian Wang +3
We study the problem of extracting accurate correspondences for point cloud registration. Recent keypoint-free methods bypass the detection of repeatable keypoints which is difficu…
ThunderNet: Towards Real-time Generic Object Detection
Zheng Qin, Zeming Li, Zhaoning Zhang +4
Real-time generic object detection on mobile platforms is a crucial but challenging computer vision task. However, previous CNN-based detectors suffer from enormous computational c…
Diagonalwise Refactorization: An Efficient Training Method for Depthwise Convolutions
Zheng Qin, Zhaoning Zhang, Dongsheng Li +2
Depthwise convolutions provide significant performance benefits owing to the reduction in both parameters and mult-adds. However, training depthwise convolution layers with GPUs is…
Merging and Evolution: Improving Convolutional Neural Networks for Mobile Applications
Zheng Qin, Zhaoning Zhang, Shiqing Zhang +2
Compact neural networks are inclined to exploit "sparsely-connected" convolutions such as depthwise convolution and group convolution for employment in mobile applications. Compare…
FD-MobileNet: Improved MobileNet with a Fast Downsampling Strategy
Zheng Qin, Zhaoning Zhang, Xiaotao Chen +1
We present Fast-Downsampling MobileNet (FD-MobileNet), an efficient and accurate network for very limited computational budgets (e.g., 10-140 MFLOPs). Our key idea is applying an a…