19 citations · 36 across the 7 of their papers we have counts for
5 papers · 1 filter
Inter-LPCM: Learning-based Inter-Frame Predictive Coding for LiDAR Point Cloud Compression
Chang Sun, Hui Yuan, Shiqi Jiang +3
Because LiDAR sensors acquire point clouds with a fixed angular resolution, the resulting data can be systematically parameterized and efficiently compressed in the spherical coord…
Point Cloud Feature Coding for Object Detection over an Error-Prone Cloud-Edge Collaborative System
Chongzhen Tian, Hui Yuan, Pan Zhao +3
Cloud-edge collaboration enhances machine perception by combining the strengths of edge and cloud computing. Edge devices capture raw data (e.g., 3D point clouds) and extract salie…
LPCM: Learning-based Predictive Coding for LiDAR Point Cloud Compression
Chang Sun, Hui Yuan, Shiqi Jiang +3
Since the data volume of LiDAR point clouds is very huge, efficient compression is necessary to reduce their storage and transmission costs. However, existing learning-based compre…
Enhancing octree-based context models for point cloud geometry compression with attention-based child node number prediction
Chang Sun, Hui Yuan, Xiaolong Mao +2
In point cloud geometry compression, most octreebased context models use the cross-entropy between the onehot encoding of node occupancy and the probability distribution predicted…
Enhancing context models for point cloud geometry compression with context feature residuals and multi-loss
Chang Sun, Hui Yuan, Shuai Li +2
In point cloud geometry compression, context models usually use the one-hot encoding of node occupancy as the label, and the cross-entropy between the one-hot encoding and the prob…