NeuralGS: Bridging Neural Fields and 3D Gaussian Splatting for Compact
  3D Representations
Authors: name: Zhenyu Tang, name: Chaoran Feng, name: Xinhua Cheng, name: Wangbo Yu, name: Junwu Zhang, name: Yuan Liu, name: Xiaoxiao Long, name: Wenping Wang, name: Li Yuan

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.