2024
Efficient Point Cloud Attribute Compression Using Rich Parallelizable Context Model
ICASSP 2024accepted
The autoregressive context model has been proven effective in point cloud attribute compression. However, it suffers from unbearable decoding latency due to the limitations of serial decoding and the large scale of point clouds. In this paper, we propose a rich, parallelizable context model for poin…