Octree-Retention Fusion: A High-Performance Context Model for Point Cloud Geometry Compression
Pages 1150 - 1154
Abstract
Point cloud compression is a pivotal technology for efficient storage and transmission of 3D point cloud data, which has significant implications for practical applications in virtual reality, autonomous driving, and cultural heritage preservation. In this paper, we propose a new learning-based model using the Retentive Network (RetNet) for point cloud compression, which achieves a lower bitrate while maintaining a high peak signal-to-noise ratio (PSNR). We first use an octree structure to segment the point cloud objects. Then, we use octree-based contextual windows to extract pivotal features from relevant sibling and ancestor nodes. Finally, we employ our proposed Octree-Retention model to effectively exploit the prior information between the spatially adjacent nodes for compression. The experimental results show that our method outperforms the state-of-the-art methods on both the LIDAR dataset(SemanticKITTI) and the object dataset(MPEG 8i), demonstrating its effectiveness.
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Index Terms
- Octree-Retention Fusion: A High-Performance Context Model for Point Cloud Geometry Compression
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Published In
May 2024
1379 pages
ISBN:9798400706196
DOI:10.1145/3652583
- General Chairs:
- Cathal Gurrin,
- Rachada Kongkachandra,
- Klaus Schoeffmann,
- Program Chairs:
- Duc-Tien Dang-Nguyen,
- Luca Rossetto,
- Shin'ichi Satoh,
- Liting Zhou
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Published: 07 June 2024
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- Yongjiang Sci-Tech Innovation 2035
- Ningbo Municipal Major Project of Science and Technology Innovation 2025
- Zhejiang Provincial Natural Science Foundation of China
- National Natural Science Foundation of China,
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