1. End-to-end learned Lossy Dynamic Point Cloud Attribute Compression
- Author
-
Nguyen, Dat Thanh, Zieger, Daniel, Stamminger, Marc, and Kaup, Andre
- Subjects
Electrical Engineering and Systems Science - Image and Video Processing ,Computer Science - Machine Learning - Abstract
Recent advancements in point cloud compression have primarily emphasized geometry compression while comparatively fewer efforts have been dedicated to attribute compression. This study introduces an end-to-end learned dynamic lossy attribute coding approach, utilizing an efficient high-dimensional convolution to capture extensive inter-point dependencies. This enables the efficient projection of attribute features into latent variables. Subsequently, we employ a context model that leverage previous latent space in conjunction with an auto-regressive context model for encoding the latent tensor into a bitstream. Evaluation of our method on widely utilized point cloud datasets from the MPEG and Microsoft demonstrates its superior performance compared to the core attribute compression module Region-Adaptive Hierarchical Transform method from MPEG Geometry Point Cloud Compression with 38.1% Bjontegaard Delta-rate saving in average while ensuring a low-complexity encoding/decoding., Comment: 6 pages, accepted for presentation at 2024 IEEE International Conference on Image Processing (ICIP) 2024
- Published
- 2024