# HyRF : Hybrid Radiance Fields for Efficient and High-quality Novel View Synthesis

NeurIPS 2025
Zipeng Wang, Dan Xu*,
The Hong Kong University of Science and Technology (HKUST)
*Corresponding author
Paper arXiv Code 🤗 HuggingFace

## Abstract

Recently, 3D Gaussian Splatting (3DGS) has emerged as a powerful alternative to NeRF-based approaches, enabling real-time, high-quality novel view synthesis through explicit, optimizable 3D Gaussians. However, 3DGS suffers from significant memory overhead due to its reliance on per-Gaussian parameters to model view-dependent effects and anisotropic shapes. While recent works propose compressing 3DGS with neural fields, these methods struggle to capture high-frequency spatial variations in Gaussian properties, leading to degraded reconstruction of fine details. We present Hybrid Radiance Fields (HyRF), a novel scene representation that combines the strengths of explicit Gaussians and neural fields. HyRF decomposes the scene into (1) a compact set of explicit Gaussians storing only critical high-frequency parameters and (2) grid-based neural fields that predict remaining properties. To enhance representational capacity, we introduce a decoupled neural field architecture, separately modeling geometry (scale, opacity, rotation) and view-dependent color. Additionally, we propose a hybrid rendering scheme that composites Gaussian splatting with a neural field-predicted background, addressing limitations in distant scene representation. Experiments demonstrate that HyRF achieves state-of-the-art rendering quality while reducing model size by over 20× compared to 3DGS and maintaining real-time performance.
TLDR: Radiance fields with SOTA quality, NeRF size and 3DGS speed.

## Method Overview

Framework overview. Our method represents the scene using grid-based neural fields and a set of compact explicit Gaussians storing only 3D position, 3D diffuse color, isotropic scale, and opacity. We encode the point position into a high-dimensional feature using the neural field and decode it into Gaussian properties with tiny MLP. These Gaussian properties are then aggregated with the explicit Gaussians and integrated into the 3DGS rasterizer.

## Trajectory Comparison

## Novel View Comparison

## Comparison to 3DGS

3DGS (1291MB) Ours (82MB) 3DGS (1034MB) Ours (60MB) 3DGS (414MB) Ours (42MB) 3DGS (281MB) Ours (30MB) 3DGS (240MB) Ours (37MB) 3DGS (515MB) Ours (32MB)

## Comparison to ScaffoldGS

ScaffoldGS (248MB) Ours (82MB) ScaffoldGS (493MB) Ours (60MB) ScaffoldGS (173MB) Ours (42MB) ScaffoldGS (258MB) Ours (30MB) ScaffoldGS (66MB) Ours (37MB) ScaffoldGS (63MB) Ours (32MB)

## BibTeX

    @article{wang2025hyrf,
      title={HyRF: Hybrid Radiance Fields for Efficient and High-quality Novel View Synthesis},
      author={Zipeng Wang and Dan Xu},
      journal={The Thirty-Ninth Annual Conference on Neural Information Processing Systems (NeurIPS)},
      year={2025},
    }