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Communication Dans Un Congrès Année : 2023

NF-PCAC: Normalizing Flow based Point Cloud Attribute Compression

Résumé

Learning-based point cloud (PC) compression is a promising research avenue to reduce the transmission and storage costs for PC applications. Existing learning-based methods to compress PCs have mainly focused on geometry and employ variational autoencoders to learn compact signal representations. However, autoencoders leverage low-dimensional bottlenecks that limit the maximum reconstruction quality, even at high bitrates. In this paper, we propose a different and novel approach to compress PC attributes by using normalizing flows. Since normalizing flows model invertible transforms, the proposed approach can achieve better reconstruction quality than variational autoencoders over a large range of bitrates. Our Normalizing Flow-based Point Cloud Attribute Compression (NF-PCAC) outperforms previous learning-based methods for attribute compression, and has comparable performance as G-PCC v.14, showing the potential of this scheme for PC compression.
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Dates et versions

hal-04026663 , version 1 (26-04-2023)

Identifiants

Citer

Rodrigo Borba Pinheiro, Jean-Eudes Marvie, Giuseppe Valenzise, Frédéric Dufaux. NF-PCAC: Normalizing Flow based Point Cloud Attribute Compression. ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), IEEE, Jun 2023, Rhodes Island, Greece. ⟨10.1109/icassp49357.2023.10096294⟩. ⟨hal-04026663⟩
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