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- W4372260571 abstract "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." @default.
- W4372260571 created "2023-05-07" @default.
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- W4372260571 date "2023-06-04" @default.
- W4372260571 modified "2023-10-15" @default.
- W4372260571 title "NF-PCAC: Normalizing Flow Based Point Cloud Attribute Compression" @default.
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- W4372260571 doi "https://doi.org/10.1109/icassp49357.2023.10096294" @default.
- W4372260571 hasPublicationYear "2023" @default.
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