End-to-end learned Lossy Dynamic Point Cloud Attribute Compression
<|reference_start|>End-to-end learned Lossy Dynamic Point Cloud Attribute Compression: 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.<|reference_end|>
@article{nguyen2024end-to-end,
    title={End-to-end learned Lossy Dynamic Point Cloud Attribute Compression},
    author={Dat Thanh Nguyen, Daniel Zieger, Marc Stamminger, Andre Kaup},
    journal={arXiv preprint arXiv:2408.10665},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.10665},
    primaryClass={eess.IV cs.LG}
}