title: NeuralGS: Bridging Neural Fields and 3D Gaussian Splatting for Compact
    3D Representations
summary: 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.
paper_authors._id: 67efd3e5afbb03a0596ff310
paper_authors.name: Zhenyu Tang
paper_authors._id: 67efd3e5afbb03a0596ff311
paper_authors.name: Chaoran Feng
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-04-08T06:54:02.424Z
paper_authors._id: 65250cda87ad4a39f84d482d
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/65250cda87ad4a39f84d482d/ryzc7hx6x09DbL4eiEBip.jpeg
paper_authors.fullname: Feng Chaoran
paper_authors.type: user
paper_authors.user: Falcary
paper_authors._id: 67efd3e5afbb03a0596ff312
paper_authors.name: Xinhua Cheng
paper_authors._id: 67efd3e5afbb03a0596ff313
paper_authors.name: Wangbo Yu
paper_authors._id: 67efd3e5afbb03a0596ff314
paper_authors.name: Junwu Zhang
paper_authors._id: 67efd3e5afbb03a0596ff315
paper_authors.name: Yuan Liu
paper_authors._id: 67efd3e5afbb03a0596ff316
paper_authors.name: Xiaoxiao Long
paper_authors._id: 67efd3e5afbb03a0596ff317
paper_authors.name: Wenping Wang
paper_authors._id: 67efd3e5afbb03a0596ff318
paper_authors.name: Li Yuan
paper_title: NeuralGS: Bridging Neural Fields and 3D Gaussian Splatting for Compact
    3D Representations
paper_summary: 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.
paper_ai_summary: NeuralGS compresses 3D Gaussian Splatting into a compact neural field representation, achieving significant model size reduction without degrading visual quality.
paper_ai_keywords: 3D Gaussian Splatting NeuralGS Scaffold-GS neural fields Multi-Layer Perceptron MLP clustering strategy fitting weights compression performance