Compression of 3D Gaussian Splatting with Optimized Feature Planes and Standard Video Codecs

• URL: http://arxiv.org/abs/2501.03399v1
• Date: Mon, 06 Jan 2025 21:37:30 GMT
• Title: Compression of 3D Gaussian Splatting with Optimized Feature Planes and Standard Video Codecs
• Authors: Soonbin Lee, Fangwen Shu, Yago Sanchez, Thomas Schierl, Cornelius Hellge,
• Abstract summary: 3D Splatting is a recognized method for 3D scene representation, known for its high rendering quality and speed.<n>We introduce an efficient compression technique that significantly reduces storage overhead by using compact representation.<n> Experimental results demonstrate that our method outperforms existing methods in data compactness while maintaining high rendering quality.
• Score: 5.583906047971048
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D Gaussian Splatting is a recognized method for 3D scene representation, known for its high rendering quality and speed. However, its substantial data requirements present challenges for practical applications. In this paper, we introduce an efficient compression technique that significantly reduces storage overhead by using compact representation. We propose a unified architecture that combines point cloud data and feature planes through a progressive tri-plane structure. Our method utilizes 2D feature planes, enabling continuous spatial representation. To further optimize these representations, we incorporate entropy modeling in the frequency domain, specifically designed for standard video codecs. We also propose channel-wise bit allocation to achieve a better trade-off between bitrate consumption and feature plane representation. Consequently, our model effectively leverages spatial correlations within the feature planes to enhance rate-distortion performance using standard, non-differentiable video codecs. Experimental results demonstrate that our method outperforms existing methods in data compactness while maintaining high rendering quality. Our project page is available at https://fraunhoferhhi.github.io/CodecGS

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