We present a work-in-progress survey on 3D Gaussian Splatting compression methods, focusing on their statistical performance across various benchmarks. This survey aims to facilitate comparability by summarizing key statistics of different compression approaches in a tabulated format. The datasets evaluated include TanksAndTemples, MipNeRF360, DeepBlending, and SyntheticNeRF. For each method, we report the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Learned Perceptual Image Patch Similarity (LPIPS), and the resultant size in megabytes (MB), as provided by the respective authors. This is an ongoing, open project, and we invite contributions from the research community as GitHub issues or pull requests. Please visit http://w-m.github.io/3dgs-compression-survey/ for more information and a sortable version of the table.

我们介绍了一份关于3D高斯喷洒压缩方法的进行中调查，并着重讨论其在各种基准测试中的统计性能。该调查旨在通过以表格形式总结不同压缩方法的关键统计数据来促进可比性。评估的数据集包括TanksAndTemples，MipNeRF360，DeepBlending和SyntheticNeRF。针对每种方法，我们报告了峰值信噪比（PSNR），结构相似性指数（SSIM），学习感知图像块相似性（LPIPS）和结果大小（以兆字节（MB）为单位），这些数据由各自的作者提供。这是一个正在进行中的开放项目，我们欢迎研究界通过GitHub问题或拉取请求做出贡献。请访问此http网址获取更多信息和可排序的表格版本。

3DGS.zip：关于3D高斯弹粒压缩方法的调查