Fast Feedforward 3D Gaussian Splatting Compression

• URL: http://arxiv.org/abs/2410.08017v2
• Date: Fri, 11 Oct 2024 14:51:20 GMT
• Title: Fast Feedforward 3D Gaussian Splatting Compression
• Authors: Yihang Chen, Qianyi Wu, Mengyao Li, Weiyao Lin, Mehrtash Harandi, Jianfei Cai,
• Abstract summary: 3D Gaussian Splatting (FCGS) is an optimization-free model that can compress 3DGS representations rapidly in a single feed-forward pass. FCGS achieves a compression ratio of over 20X while maintaining fidelity, surpassing most per-scene SOTA optimization-based methods.
• Score: 55.149325473447384
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With 3D Gaussian Splatting (3DGS) advancing real-time and high-fidelity rendering for novel view synthesis, storage requirements pose challenges for their widespread adoption. Although various compression techniques have been proposed, previous art suffers from a common limitation: for any existing 3DGS, per-scene optimization is needed to achieve compression, making the compression sluggish and slow. To address this issue, we introduce Fast Compression of 3D Gaussian Splatting (FCGS), an optimization-free model that can compress 3DGS representations rapidly in a single feed-forward pass, which significantly reduces compression time from minutes to seconds. To enhance compression efficiency, we propose a multi-path entropy module that assigns Gaussian attributes to different entropy constraint paths for balance between size and fidelity. We also carefully design both inter- and intra-Gaussian context models to remove redundancies among the unstructured Gaussian blobs. Overall, FCGS achieves a compression ratio of over 20X while maintaining fidelity, surpassing most per-scene SOTA optimization-based methods. Our code is available at: https://github.com/YihangChen-ee/FCGS.

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