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

• URL: http://arxiv.org/abs/2509.17083v2
• Date: Tue, 23 Sep 2025 03:08:14 GMT
• Title: HyRF: Hybrid Radiance Fields for Memory-efficient and High-quality Novel View Synthesis
• Authors: Zipeng Wang, Dan Xu,
• Abstract summary: We present Hybrid Radiance Fields (HyRF), a novel scene representation that combines the strengths of explicit Gaussians and neural fields.<n>HyRF achieves state-of-the-art rendering quality while reducing model size by over 20 times compared to 3DGS.
• Score: 11.71939856454585
• License: http://creativecommons.org/licenses/by/4.0/
• 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 times compared to 3DGS and maintaining real-time performance. Our project page is available at https://wzpscott.github.io/hyrf/.

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