3D Gaussian Splatting is a new method for modeling and rendering 3D radiance fields that achieves much faster learning and rendering time compared to SOTA NeRF methods. However, it comes with a drawback in the much larger storage demand compared to NeRF methods since it needs to store the parameters for several 3D Gaussians. We notice that many Gaussians may share similar parameters, so we introduce a simple vector quantization method based on \kmeans algorithm to quantize the Gaussian parameters. Then, we store the small codebook along with the index of the code for each Gaussian. Moreover, we compress the indices further by sorting them and using a method similar to run-length encoding. We do extensive experiments on standard benchmarks as well as a new benchmark which is an order of magnitude larger than the standard benchmarks. We show that our simple yet effective method can reduce the storage cost for the original 3D Gaussian Splatting method by a factor of almost 20× with a very small drop in the quality of rendered images.
3D高斯喷溅是一种新的建模和渲染3D辐射场的方法，与最新的NeRF方法相比，它实现了更快的学习和渲染时间。然而，与NeRF方法相比，它的一个缺点是需要更大的存储需求，因为它需要存储几个3D高斯的参数。我们注意到许多高斯可能具有相似的参数，因此我们引入了一种基于\kmeans算法的简单向量量化方法来量化高斯参数。然后，我们存储小型码本以及每个高斯的码索引。此外，我们通过排序索引并使用类似于游程编码的方法进一步压缩索引。我们在标准基准测试以及一个比标准基准测试大一个数量级的新基准测试上进行了广泛的实验。我们展示了我们这种简单而有效的方法可以将原始3D高斯喷溅方法的存储成本减少近20倍，同时渲染图像的质量只有非常小的下降。