HyRF: Hybrid Radiance Fields for Memory-efficient and High-quality Novel
  View Synthesis
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/.
68d211241ca7156988a8ed80
Zipeng Wang
claimed_verified
2025-09-23T10:05:49.538Z
65d31df8e3667040af7bc945
/avatars/abe1137fccc45da0e1bd41ef0c172899.svg
Zipeng Wang
user
ZipW
68d211241ca7156988a8ed81
Dan Xu
claimed_verified
2025-09-24T13:55:37.112Z
66feab48651e00e22f33222e
/avatars/7344377e2c796c7ec85194bb2fc78521.svg
Dan Xu
user
danxuhk
HyRF: Hybrid Radiance Fields for Memory-efficient and High-quality Novel
  View Synthesis
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/.
Hybrid Radiance Fields combine explicit Gaussians and neural fields to achieve high-quality rendering with reduced memory usage and real-time performance.
3D Gaussian Splatting NeRF explicit Gaussians neural fields high-frequency parameters grid-based neural fields decoupled neural field architecture hybrid rendering scheme Gaussian splatting neural field-predicted background