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

• URL: http://arxiv.org/abs/2303.06519v2
• Date: Wed, 20 Mar 2024 10:00:15 GMT
• Title: Lossless Point Cloud Geometry and Attribute Compression Using a Learned Conditional Probability Model
• Authors: Dat Thanh Nguyen, Andre Kaup,
• Abstract summary: We present an efficient point cloud compression method that uses tensor-based deep neural networks to learn point cloud geometry and color probability. Our method represents a point cloud with both occupancy feature and three features at different bit depths in a unified representation.
• Score: 2.670322123407995
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, we have witnessed the presence of point cloud data in many aspects of our life, from immersive media, autonomous driving to healthcare, although at the cost of a tremendous amount of data. In this paper, we present an efficient lossless point cloud compression method that uses sparse tensor-based deep neural networks to learn point cloud geometry and color probability distributions. Our method represents a point cloud with both occupancy feature and three attribute features at different bit depths in a unified sparse representation. This allows us to efficiently exploit feature-wise and point-wise dependencies within point clouds using a sparse tensor-based neural network and thus build an accurate auto-regressive context model for an arithmetic coder. To the best of our knowledge, this is the first learning-based lossless point cloud geometry and attribute compression approach. Compared with the-state-of-the-art lossless point cloud compression method from Moving Picture Experts Group (MPEG), our method achieves 22.6% reduction in total bitrate on a diverse set of test point clouds while having 49.0% and 18.3% rate reduction on geometry and color attribute component, respectively.

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