+ 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. +
+@inproceedings{navaneet2023compact3d,
+ author = {Navaneet, K L and Pourahmadi Meibodi, Kossar and Koohpayegani, Soroush Abbasi and Pirsiavash, Hamed},
+ title = {Compact3D: Compressing Gaussian Splat Radiance Field Models with Vector Quantization},
+ year = {2023}
+}