3D Gaussian Splatting (3DGS) achieves impressive quality and rendering speed, but with millions of 3D Gaussians and significant storage and transmission costs. In this paper, we aim to develop a simple yet effective method called NeuralGS that compresses the original 3DGS into a compact representation. 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 within each cluster using different tiny MLPs, based on importance scores of Gaussians as fitting weights. We experiment on multiple datasets, achieving a 91-times average model size reduction without harming the visual quality.
@article{arxiv.2503.23162,
title = {NeuralGS: Bridging Neural Fields and 3D Gaussian Splatting for Compact 3D Representations},
author = {Zhenyu Tang and Chaoran Feng and Xinhua Cheng and Wangbo Yu and Junwu Zhang and Yuan Liu and Xiaoxiao Long and Wenping Wang and Li Yuan},
journal= {arXiv preprint arXiv:2503.23162},
year = {2025}
}