Deep Learning-based Point Cloud Geometry Coding with Resolution Scalability

Guarda, André F. R.; Rodrigues, Nuno M. M.; Pereira, Fernando

http://hdl.handle.net/10400.8/13753

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Deep Learning-based Point Cloud Geometry Coding with Resolution Scalability.pdf	Point clouds are a 3D visual representation format that has recently become fundamentally important for immersive and interactive multimedia applications. Considering the high number of points of practically relevant point clouds, and their increasing market demand, efficient point cloud coding has become a vital research topic. In addition, scalability is an important feature for point cloud coding, especially for real-time applications, where the fast and rate efficient access to a decoded point cloud is important; however, this issue is still rather unexplored in the literature. In this context, this paper proposes a novel deep learning-based point cloud geometry coding solution with resolution scalability via interlaced sub-sampling. As additional layers are decoded, the number of points in the reconstructed point cloud increases as well as the overall quality. Experimental results show that the proposed scalable point cloud geometry coding solution outperforms the recent MPEG Geometry-based Point Cloud Compression standard which is much less scalable.	1.11 MB	Adobe PDF	Download

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Authors

Guarda, André F. R.

Rodrigues, Nuno M. M.

Pereira, Fernando

Abstract(s)

Point clouds are a 3D visual representation format that has recently become fundamentally important for immersive and interactive multimedia applications. Considering the high number of points of practically relevant point clouds, and their increasing market demand, efficient point cloud coding has become a vital research topic. In addition, scalability is an important feature for point cloud coding, especially for real-time applications, where the fast and rate efficient access to a decoded point cloud is important; however, this issue is still rather unexplored in the literature. In this context, this paper proposes a novel deep learning-based point cloud geometry coding solution with resolution scalability via interlaced sub-sampling. As additional layers are decoded, the number of points in the reconstructed point cloud increases as well as the overall quality. Experimental results show that the proposed scalable point cloud geometry coding solution outperforms the recent MPEG Geometry-based Point Cloud Compression standard which is much less scalable.

Description

EISBN - 978-1-7281-9320-5
Article number - 9287060; Conference date - 21 September 2020 - 24 September 2020; Conference code - 165866

Keywords

point cloud coding deep learning scalable coding resolution scalability interlaced sampling

URI

http://hdl.handle.net/10400.8/13753

Citation

A. F. R. Guarda, N. M. M. Rodrigues and F. Pereira, "Deep Learning-based Point Cloud Geometry Coding with Resolution Scalability," 2020 IEEE 22nd International Workshop on Multimedia Signal Processing (MMSP), Tampere, Finland, 2020, pp. 1-6, doi: https://doi.org/10.1109/MMSP48831.2020.9287060.