Lossless Compression of Point Cloud Sequences Using Sequence Optimized CNN Models

In this paper we propose a new paradigm for encoding the geometry of dense point cloud sequences, where a convolutional neural network (CNN), which estimates the encoding distributions, is optimized on several frames of the sequence to be compressed. We adopt lightweight CNN structures, we perform t...

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Vydané v:	IEEE access Ročník 10; s. 83678 - 83691
Hlavní autori:	Kaya, Emre C., Tabus, Ioan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Arithmetic coding Artificial neural networks Context modeling Convolutional neural networks Decoding Encoding Encoding-Decoding Estimates lossless geometry compression octree coding Octrees Point cloud compression Process parameters Training Training data
ISSN:	2169-3536, 2169-3536
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