401 citations · 880 across the 29 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…
DUGAE: Unified Geometry and Attribute Enhancement via Spatiotemporal Correlations for G-PCC Compressed Dynamic Point Clouds
Pan Zhao, Hui Yuan, Chang Sun +3
Existing post-decoding quality enhancement methods for point clouds are designed for static data and typically process each frame independently. As a result, they cannot effectivel…
Blind Quality Enhancement for G-PCC Compressed Dynamic Point Clouds
Tian Guo, Hui Yuan, Chang Sun +3
Point cloud compression often introduces noticeable reconstruction artifacts, which makes quality enhancement necessary. Existing approaches typically assume prior knowledge of the…
Fast Attention-Based Simplification of LiDAR Point Clouds for Object Detection and Classification
Z. Rozsa, Á. Madaras, Q. Wei +5
LiDAR point clouds are widely used in autonomous driving and consist of large numbers of 3D points captured at high frequency to represent surrounding objects such as vehicles, ped…
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…