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...

Ausführliche Beschreibung

Bibliographische Detailangaben

Veröffentlicht in:	IEEE access Jg. 10; S. 83678 - 83691
Hauptverfasser:	Kaya, Emre C., Tabus, Ioan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2022 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	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
Online-Zugang:	Volltext
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!