The JPEG Pleno Learning-Based Point Cloud Coding Standard: Serving Man and Machine
Efficient point cloud coding has become increasingly critical for multiple applications such as virtual reality, autonomous driving, and digital twin systems, where rich and interactive 3D data representations may functionally make the difference. Deep learning has emerged as a powerful tool in this...
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| Vydáno v: | IEEE access Ročník 13; s. 43289 - 43315 |
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01.01.2025
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| Abstract | Efficient point cloud coding has become increasingly critical for multiple applications such as virtual reality, autonomous driving, and digital twin systems, where rich and interactive 3D data representations may functionally make the difference. Deep learning has emerged as a powerful tool in this domain, offering advanced techniques for compressing point clouds more efficiently than conventional coding methods while also allowing effective computer vision tasks performed in the compressed domain thus, for the first time, making available a common compressed visual representation effective for both man and machine. Taking advantage of this potential, JPEG has recently finalized the JPEG Pleno Learning-based Point Cloud Coding (PCC) standard offering efficient lossy coding of static point clouds, targeting both human visualization and machine processing by leveraging deep learning models for geometry and color coding. The geometry is processed directly in its original 3D form using sparse convolutional neural networks, while the color data is projected onto 2D images and encoded using the also learning-based JPEG AI standard. The goal of this paper is to provide a complete technical description of the JPEG PCC standard, along with a thorough benchmarking of its performance against the state-of-the-art, while highlighting its main strengths and weaknesses. In terms of compression performance, JPEG PCC outperforms the conventional MPEG PCC standards, especially in geometry coding, achieving significant rate reductions. Color compression performance is less competitive but this is overcome by the power of a full learning-based coding framework for both geometry and color and the associated effective compressed domain processing. |
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| AbstractList | Efficient point cloud coding has become increasingly critical for multiple applications such as virtual reality, autonomous driving, and digital twin systems, where rich and interactive 3D data representations may functionally make the difference. Deep learning has emerged as a powerful tool in this domain, offering advanced techniques for compressing point clouds more efficiently than conventional coding methods while also allowing effective computer vision tasks performed in the compressed domain thus, for the first time, making available a common compressed visual representation effective for both man and machine. Taking advantage of this potential, JPEG has recently finalized the JPEG Pleno Learning-based Point Cloud Coding (PCC) standard offering efficient lossy coding of static point clouds, targeting both human visualization and machine processing by leveraging deep learning models for geometry and color coding. The geometry is processed directly in its original 3D form using sparse convolutional neural networks, while the color data is projected onto 2D images and encoded using the also learning-based JPEG AI standard. The goal of this paper is to provide a complete technical description of the JPEG PCC standard, along with a thorough benchmarking of its performance against the state-of-the-art, while highlighting its main strengths and weaknesses. In terms of compression performance, JPEG PCC outperforms the conventional MPEG PCC standards, especially in geometry coding, achieving significant rate reductions. Color compression performance is less competitive but this is overcome by the power of a full learning-based coding framework for both geometry and color and the associated effective compressed domain processing. |
| Author | Pereira, Fernando Rodrigues, Nuno M. M. Guarda, Andre F. R. |
| Author_xml | – sequence: 1 givenname: Andre F. R. orcidid: 0000-0001-5996-1074 surname: Guarda fullname: Guarda, Andre F. R. email: andre.guarda@lx.it.pt organization: Instituto de Telecomunicações, Lisbon, Portugal – sequence: 2 givenname: Nuno M. M. orcidid: 0000-0001-9536-1017 surname: Rodrigues fullname: Rodrigues, Nuno M. M. organization: Instituto de Telecomunicações, Lisbon, Portugal – sequence: 3 givenname: Fernando orcidid: 0000-0001-6100-947X surname: Pereira fullname: Pereira, Fernando organization: Instituto de Telecomunicações, Lisbon, Portugal |
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| SubjectTerms | Artificial intelligence Artificial neural networks Codecs Coding standards Color coding Compressing Computer vision Deep learning Digital twins Encoding Geometry Image coding Image color analysis Image compression JPEG Pleno standard learning-based coding Machine learning man and machine point cloud coding Point cloud compression Representations Three-dimensional displays Transform coding Virtual reality |
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| Title | The JPEG Pleno Learning-Based Point Cloud Coding Standard: Serving Man and Machine |
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