HyRF：混合辐射场——实现内存高效与高质量新视角合成的创新方法

HyRF: Hybrid Radiance Fields for Memory-efficient and High-quality Novel View Synthesis

September 21, 2025

作者: Zipeng Wang, Dan Xu

cs.AI

摘要

近期，3D高斯泼溅（3DGS）作为一种强大的替代方案崭露头角，它通过显式、可优化的3D高斯实现了实时、高质量的新视角合成。然而，3DGS因其依赖每个高斯的参数来建模视角依赖效应和各向异性形状，而面临显著的内存开销问题。尽管最近的研究提出了利用神经场压缩3DGS的方法，但这些方法难以捕捉高斯属性中的高频空间变化，导致精细细节的重建质量下降。我们提出了混合辐射场（HyRF），这是一种新颖的场景表示方法，它结合了显式高斯和神经场的优势。HyRF将场景分解为：（1）一组紧凑的显式高斯，仅存储关键的高频参数；（2）基于网格的神经场，用于预测其余属性。为了增强表示能力，我们引入了一种解耦的神经场架构，分别建模几何（尺度、不透明度、旋转）和视角依赖的颜色。此外，我们提出了一种混合渲染方案，将高斯泼溅与神经场预测的背景相结合，解决了远距离场景表示的局限性。实验表明，HyRF在实现最先进渲染质量的同时，将模型大小相比3DGS减少了超过20倍，并保持了实时性能。我们的项目页面可在https://wzpscott.github.io/hyrf/访问。

English

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 times compared to 3DGS and maintaining real-time performance. Our project page is available at https://wzpscott.github.io/hyrf/.