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DEEP PROBABILISTIC MODEL FOR LOSSLESS SCALABLE POINT CLOUD ATTRIBUTE COMPRESSION

Nguyen DT, Nambiar KG, Kaup A (2023)

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Publication Language: English

Publication Type: Conference contribution, Original article

Publication year: 2023

Conference Proceedings Title: International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2023

Event location: Rodos Palace Luxury Convention Resort, Rhodes Island, Greece

URI: https://arxiv.org/abs/2303.06517

DOI: 10.1109/icassp49357.2023.10095385

Abstract

 In this work, we build an end-to-end multiscale point cloud attribute coding method (MNeT) that progressively projects the attributes onto multiscale latent spaces. The multiscale architecture provides an accurate context for the attribute probability modeling and thus minimizes the coding bitrate with a single network prediction. Besides, our method allows scalable coding that lower quality versions can be easily extracted from the losslessly compressed bitstream. We validate our method on a set of point clouds from MVUB and MPEG and show that our method outperforms recently proposed methods and on par with the latest G-PCC version 14. Besides, our coding time is substantially faster than G-PCC.

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How to cite

APA:

Nguyen, D.T., Nambiar, K.G., & Kaup, A. (2023). DEEP PROBABILISTIC MODEL FOR LOSSLESS SCALABLE POINT CLOUD ATTRIBUTE COMPRESSION. In International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 2023. Rodos Palace Luxury Convention Resort, Rhodes Island, Greece, GR.

MLA:

Nguyen, Dat Thanh, Kamal Gopikrishnan Nambiar, and André Kaup. "DEEP PROBABILISTIC MODEL FOR LOSSLESS SCALABLE POINT CLOUD ATTRIBUTE COMPRESSION." Proceedings of the International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Rodos Palace Luxury Convention Resort, Rhodes Island, Greece 2023.

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