无需优化即可秒级压缩3D高斯点云，体积缩小20倍仍保画质。
Fast Feedforward 3D Gaussian Splatting Compression
- 单次前向传播完成压缩，跳过耗时的逐场景优化。
- 多路径熵模块平衡压缩率与图像保真度，实现20倍以上压缩比。
- 适合需要快速部署的实时3D内容应用，如VR/AR和元宇宙。
随着3D高斯点阵（3DGS）在新视角合成中实现实时高保真渲染，存储需求成为广泛应用的瓶颈。尽管已有多种压缩技术，但现有方法均需对每个场景进行逐场景优化，导致压缩过程缓慢。为此，我们提出无优化的3DGS快速压缩方法（FCGS），可在单次前向传播中实现快速压缩，将压缩时间从分钟级降至秒级。为提升压缩效率，我们设计了多路径熵模块，将高斯属性分配至不同熵约束路径，以平衡体积与保真度；同时，精心构建了高斯间与高斯内上下文模型，消除非结构化高斯点团间的冗余。总体而言，FCGS实现了超过20倍的压缩比，且在保真度上超越多数基于优化的最新方法。代码已开源：https://github.com/YihangChen-ee/FCGS。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。