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3D Gaussian Splatting (3DGS) is a new method for modeling and rendering 3Dradiance fields that achieves much faster learning and rendering time comparedto SOTA NeRF methods. However, it comes with a drawback in the much largerstorage demand compared to NeRF methods since it needs to store the parametersfor several 3D Gaussians. We notice that many Gaussians may share similarparameters, so we introduce a simple vector quantization method based onK-means to quantize the Gaussian parameters while optimizing them. Then, westore the small codebook along with the index of the code for each Gaussian. Wecompress the indices further by sorting them and using a method similar torun-length encoding. Moreover, we use a simple regularizer to encourage zeroopacity (invisible Gaussians) to reduce the storage and rendering time by alarge factor through reducing the number of Gaussians. We do extensiveexperiments on standard benchmarks as well as an existing 3D dataset that is anorder of magnitude larger than the standard benchmarks used in this field. Weshow that our simple yet effective method can reduce the storage cost for 3DGSby 40 to 50x and rendering time by 2 to 3x with a very small drop in thequality of rendered images.

CompGS: Smaller and Faster Gaussian Splatting with Vector Quantization