ANFPCGC++: Point Cloud Geometry Coding Using Augmented Normalizing Flows and Transformer-Based Entropy Model
As immersive media gains increasing prominence, point clouds have emerged as a preferred data representation for presenting complex 3D scenes. However, the large size of point cloud data poses challenges in terms of storage and real-time transmission, prompting the need for highly efficient point cl...
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IEEE
2024-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10735187/ |
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| author | Jui-Chiu Chiang Ji-Jin Chiu Monyneath Yim |
| author_facet | Jui-Chiu Chiang Ji-Jin Chiu Monyneath Yim |
| author_sort | Jui-Chiu Chiang |
| collection | DOAJ |
| description | As immersive media gains increasing prominence, point clouds have emerged as a preferred data representation for presenting complex 3D scenes. However, the large size of point cloud data poses challenges in terms of storage and real-time transmission, prompting the need for highly efficient point cloud compression techniques. In response to these challenges, we introduce a novel approach called ANFPCGC++ (Augmented Normalizing Flow-based Point Cloud Geometry Compression) for lossy static point cloud geometry coding. ANFPCGC++ leverages the power of Augmented Normalizing Flow (ANF) in conjunction with sparse convolution to effectively capture and incorporate spatial correlations inherent in point clouds. ANF offers a higher level of expressiveness compared to conventional methods like variational autoencoders (VAE), resulting in more accurate and faithful latent representations. Furthermore, we introduce a Transformer-based entropy model, that combines the hyperprior and context information, enabling a more precise entropy model that supports parallel computation. Extensive experimental results confirm the superior performance of ANFPCGC++. By comparing to the point cloud coding standards G-PCC and V-PCC, our proposed method achieves remarkable bitrate savings of 63.7% and 60.0% in terms of D1-PSNR, respectively. Additionally, when compared to other deep learning-based point cloud geometry compression methods like PCGCv2 and ANFPCGC, our approach demonstrates an average bitrate reduction of 25.6% and 23.6% in terms of D1-PSNR, respectively. The source code is available at <uri>https://github.com/ymnn1996/ANFPCGC2</uri>. |
| format | Article |
| id | doaj-art-9732597d5e33415980d35f2a5af49ad8 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-9732597d5e33415980d35f2a5af49ad82024-11-12T00:01:32ZengIEEEIEEE Access2169-35362024-01-011216341016342310.1109/ACCESS.2024.348646410735187ANFPCGC++: Point Cloud Geometry Coding Using Augmented Normalizing Flows and Transformer-Based Entropy ModelJui-Chiu Chiang0https://orcid.org/0000-0003-1397-8393Ji-Jin Chiu1Monyneath Yim2https://orcid.org/0009-0005-9402-4744Department of Electrical Engineering, National Chung Cheng University, Chiayi, TaiwanDepartment of Electrical Engineering, National Chung Cheng University, Chiayi, TaiwanDepartment of Electrical Engineering, National Chung Cheng University, Chiayi, TaiwanAs immersive media gains increasing prominence, point clouds have emerged as a preferred data representation for presenting complex 3D scenes. However, the large size of point cloud data poses challenges in terms of storage and real-time transmission, prompting the need for highly efficient point cloud compression techniques. In response to these challenges, we introduce a novel approach called ANFPCGC++ (Augmented Normalizing Flow-based Point Cloud Geometry Compression) for lossy static point cloud geometry coding. ANFPCGC++ leverages the power of Augmented Normalizing Flow (ANF) in conjunction with sparse convolution to effectively capture and incorporate spatial correlations inherent in point clouds. ANF offers a higher level of expressiveness compared to conventional methods like variational autoencoders (VAE), resulting in more accurate and faithful latent representations. Furthermore, we introduce a Transformer-based entropy model, that combines the hyperprior and context information, enabling a more precise entropy model that supports parallel computation. Extensive experimental results confirm the superior performance of ANFPCGC++. By comparing to the point cloud coding standards G-PCC and V-PCC, our proposed method achieves remarkable bitrate savings of 63.7% and 60.0% in terms of D1-PSNR, respectively. Additionally, when compared to other deep learning-based point cloud geometry compression methods like PCGCv2 and ANFPCGC, our approach demonstrates an average bitrate reduction of 25.6% and 23.6% in terms of D1-PSNR, respectively. The source code is available at <uri>https://github.com/ymnn1996/ANFPCGC2</uri>.https://ieeexplore.ieee.org/document/10735187/Point cloud geometry compressionaugmented normalizing flowcontext modeltransformer |
| spellingShingle | Jui-Chiu Chiang Ji-Jin Chiu Monyneath Yim ANFPCGC++: Point Cloud Geometry Coding Using Augmented Normalizing Flows and Transformer-Based Entropy Model IEEE Access Point cloud geometry compression augmented normalizing flow context model transformer |
| title | ANFPCGC++: Point Cloud Geometry Coding Using Augmented Normalizing Flows and Transformer-Based Entropy Model |
| title_full | ANFPCGC++: Point Cloud Geometry Coding Using Augmented Normalizing Flows and Transformer-Based Entropy Model |
| title_fullStr | ANFPCGC++: Point Cloud Geometry Coding Using Augmented Normalizing Flows and Transformer-Based Entropy Model |
| title_full_unstemmed | ANFPCGC++: Point Cloud Geometry Coding Using Augmented Normalizing Flows and Transformer-Based Entropy Model |
| title_short | ANFPCGC++: Point Cloud Geometry Coding Using Augmented Normalizing Flows and Transformer-Based Entropy Model |
| title_sort | anfpcgc point cloud geometry coding using augmented normalizing flows and transformer based entropy model |
| topic | Point cloud geometry compression augmented normalizing flow context model transformer |
| url | https://ieeexplore.ieee.org/document/10735187/ |
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