Point Cloud Geometry Scalable Coding With a Single End-to-End Deep Learning Model

Point clouds are gaining importance as the format to represent complex 3D objects and scenes, offering high user immersion and interaction, although at the cost of requiring massive data. Scalable coding is an important feature for point cloud coding, especially for real-time applications, where the...

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Vydáno v:Proceedings - International Conference on Image Processing s. 3354 - 3358
Hlavní autoři: Guarda, Andre F. R., Rodrigues, Nuno M. M., Pereira, Fernando
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.10.2020
Témata:
Decoding
deep learning
Encoding
Geometry
Point cloud coding
progressive coding
quality scalability
Three-dimensional displays
Training
Transform coding
ISSN:2381-8549
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Shrnutí:Point clouds are gaining importance as the format to represent complex 3D objects and scenes, offering high user immersion and interaction, although at the cost of requiring massive data. Scalable coding is an important feature for point cloud coding, especially for real-time applications, where the fast and bitrate efficient access to a decoded point cloud is important; however, this issue is still rather unexplored in the literature. With the rise of deep learning methods as a promising solution for efficient coding, this paper proposes the first deep learning-based point cloud geometry scalable coding solution. Experimental results show that the proposed scalable coding solution consistently outperforms the MPEG standard for static point cloud geometry coding. In this way, a new research path is open for point cloud scalable coding technology.
ISSN:2381-8549
DOI:10.1109/ICIP40778.2020.9191021