Compact3D: Compressing Gaussian Splat Radiance Field Models with Vector
  Quantization
KL Navaneet Kossar Pourahmadi Meibodi Soroush Abbasi Koohpayegani Hamed Pirsiavash
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
$20\times$ with a very small drop in the quality of rendered images.