G-PCC++: Enhanced Geometry-based Point Cloud Compression-Reference-Cited by-同舟云学术

G-PCC++: Enhanced Geometry-based Point Cloud Compression

Published:2023-10-26 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 31st ACM International Conference on Multimedia
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Author:

Zhang Junzhe¹^ORCID,Chen Tong²^ORCID,Ding Dandan¹^ORCID,Ma Zhan²^ORCID

Affiliation:

1. Hangzhou Normal University, Hangzhou, China

2. Nanjing University, Nanjing, China

Funder

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3581783.3613827

Reference63 articles.

1. A. AKhtar Z. Li G. V. Auwera L. Li and J. Chen. 2022. PU-Dense: Sparse Tensor-based Point Cloud Geometry Upsampling. IEEE Trans. Image Processing (2022). A. AKhtar Z. Li G. V. Auwera L. Li and J. Chen. 2022. PU-Dense: Sparse Tensor-based Point Cloud Geometry Upsampling. IEEE Trans. Image Processing (2022).

2. Zaghetto Alexandre , Danillo Graziosi , and Tabatabai Ali . Jan. 2022. Dataset split for AI-PCC. ISO/IEC JTC 1/SC 29/WG 7 m58754 (Jan . 2022 ). Zaghetto Alexandre, Danillo Graziosi, and Tabatabai Ali. Jan. 2022. Dataset split for AI-PCC. ISO/IEC JTC 1/SC 29/WG 7 m58754 (Jan. 2022).

3. G. Bjøntegaard . 2001 . Calculation of average PSNR differences between RD-curves. In ITU-T SG 16/Q6 , 13th VCEG Meeting. document VCEG-M33 . G. Bjøntegaard. 2001. Calculation of average PSNR differences between RD-curves. In ITU-T SG 16/Q6, 13th VCEG Meeting. document VCEG-M33.

4. 3D Point Cloud Compression

5. Compression of Sparse and Dense Dynamic Point Clouds—Methods and Standards

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