Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Model

Author:

Nguyen Dat Thanh1ORCID,Kaup André1ORCID

Affiliation:

1. Chair of Multimedia Communications and Signal Processing, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany

Funder

Deutsche Forschungsgemeinschaft

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Media Technology

Cited by 10 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Dependence-Based Coarse-to-Fine Approach for Reducing Distortion Accumulation in G-PCC Attribute Compression;IEEE Transactions on Industrial Informatics;2024-09

2. Compressed Point Cloud Quality Index by Combining Global Appearance and Local Details;ACM Transactions on Multimedia Computing, Communications, and Applications;2024-06-15

3. Enhancing Context Models for Point Cloud Geometry Compression With Context Feature Residuals and Multi-Loss;IEEE Journal on Emerging and Selected Topics in Circuits and Systems;2024-06

4. Advanced Patch-Based Affine Motion Estimation for Dynamic Point Cloud Geometry Compression;Sensors;2024-05-15

5. Efficient Point Cloud Attribute Compression Using Rich Parallelizable Context Model;ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2024-04-14

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