NeuralGS: Bridging Neural Fields and 3D Gaussian Splatting for Compact 3D Representations

• URL: http://arxiv.org/abs/2503.23162v1
• Date: Sat, 29 Mar 2025 17:36:53 GMT
• Title: NeuralGS: Bridging Neural Fields and 3D Gaussian Splatting for Compact 3D Representations
• Authors: Zhenyu Tang, Chaoran Feng, Xinhua Cheng, Wangbo Yu, Junwu Zhang, Yuan Liu, Xiaoxiao Long, Wenping Wang, Li Yuan,
• Abstract summary: 3DGS demonstrates superior quality and rendering speed, but with millions of 3D Gaussians and significant storage and transmission costs.<n>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.<n>In this paper, we aim to develop a simple yet effective method called SplatGS that explores in another way to compress the original 3DGS into a compact representation without the voxel structure and complex quantization strategies.
• Score: 39.343445598839125
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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