3D Gaussian Splatting (3DGS) demonstrates superior quality and rendering
speed, but with millions of 3D Gaussians and significant storage and
transmission costs. Recent 3DGS compression methods mainly concentrate on
compressing Scaffold-GS, achieving impressive performance but with an
additional voxel structure and a complex encoding and quantization strategy. In
this paper, we aim to develop a simple yet effective method called NeuralGS
that explores in another way to compress the original 3DGS into a compact
representation without the voxel structure and complex quantization strategies.
Our observation is that neural fields like NeRF can represent complex 3D scenes
with Multi-Layer Perceptron (MLP) neural networks using only a few megabytes.
Thus, NeuralGS effectively adopts the neural field representation to encode the
attributes of 3D Gaussians with MLPs, only requiring a small storage size even
for a large-scale scene. To achieve this, we adopt a clustering strategy and
fit the Gaussians with different tiny MLPs for each cluster, based on
importance scores of Gaussians as fitting weights. We experiment on multiple
datasets, achieving a 45-times average model size reduction without harming the
visual quality. The compression performance of our method on original 3DGS is
comparable to the dedicated Scaffold-GS-based compression methods, which
demonstrate the huge potential of directly compressing original 3DGS with
neural fields.