.png){kind=icon}
.png){kind=icon}
Return
Towards LiDAR point cloud geometry compression using rate-distortion optimization and adaptive quantization for human-machine vision
.png){kind=icon}
PRE
AI
DOI：10.1016/j.displa.2026.103344.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
• The human and machine vision distortion are derived for G-PCC. • The HDRDO algorithm is constructed for G-PCC. • The importance classification method divides point clouds. • The HDRDO-based AQ algorithm adaptively selects the optimal QP.