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 this http URL for more information and a sortable version of the table.
我们在这里展示了一个关于3D高斯喷溅压缩方法的正在进行的研究调查，重点关注它们在各种基准测试中的统计性能。此调查旨在通过以表格格式总结不同压缩方法的关键统计数据，以促进可比性。评估的数据集包括TanksAndTemples、MipNeRF360、DeepBlending和SyntheticNeRF。对于每种方法，我们报告了峰值信噪比（PSNR）、结构相似性指数（SSIM）、学习感知图像补丁相似性（LPIPS）和结果大小（以兆字节MB计），这些数据由各自的作者提供。这是一个持续进行的开放项目，我们邀请研究社区通过GitHub问题或拉取请求贡献。