Autoencoder Architectures for Low-Rate Sparse Point Cloud Geometry Coding

Efficient compression of sparse point cloud geometry remains a critical challenge in 3D content processing, particularly for low-rate scenarios where conventional codecs struggle to maintain efficiency. This work proposes a system-level framework for low-rate sparse geometry coding, leveraging three...

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Bibliografiske detaljer

Principais autores:	Ivaylo Bozhilov, Radostina Petkova, Krasimir Tonchev, Agata Manolova, Vladimir Poulkov, H. Vincent Poor
Format:	Artigo
Sprog:	Inglês
Udgivet:	IEEE 2025-01-01
Serier:	IEEE Access
Fag:	Autoencoder coding compression machine learning point cloud source coding
Online adgang:	https://ieeexplore.ieee.org/document/11303733/
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