HyRF: Hybrid Radiance Fields for Memory-efficient and High-quality Novel View Synthesis
Authors: Zipeng Wang
Dan Xu
Venue: NeurIPS 2025 poster ()
Keywords: Neural Radiance Fields
3D Gaussian Splatting
Primary area: applications
TL;DR: Radiance fields with SOTA quality, NeRF size and 3DGS speed.

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.

HyRF: Hybrid Radiance Fields for Memory-efficient and High-quality Novel View Synthesis
Zipeng Wang zwang2530cse ust. hk
Dan Xu danxuêcse ust hk
Department of Computer Science and Engineering The Hong Kong University of Science and Technology
Abstract
Recently, 3D Gaussian Splatting (3DGS) has emerged as a powerful alternative NeRF-based upproaches, enabling real-time, high-quality novel view synthesis through explicit, optimizable 3D Gaussians. However, 3DGS suffers from signif- icant memory overhead due its reliance per-Gaussian parameters model view-dependent effects and anisotropic shapes. While recent works propose com- pressing 3DGS with neural fields, these methods struggle capture high-frequency spatial variations Gaussian properties, leading to degraded reconstruction of fine details. We present Hybrid Radiance Fields (HyRF), novel scene representation that combines the strengths of explicit Gaussians and neural fields. HyRF decom- poses the scene into (1) 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 decoupled neu- ral field urchitecture, separately modeling geometry (scale, opacity, rotation) and view-dependent color. Additionally, we propose hybrid rendering scheme that composites Gaussian splatting with neural field filld-predcced background, addressing limitations in distant scene representation. Experiments demonstrate that HyRF achieves state-of-the-art rendering quality while reducing model size by over 20x compared to 3DGS and maintaining real-time performance. Our project page is available at hittps://wzpscotte githubbio/hyrf/
1
Introduction
Novel view synthesis is critical area in computer vision, with applications in scene manipulation
32
45 47 33).
autonomous driving [34 43). virtual fly-throughs 44 25] 24), and 3D generation models
12 36). Neural Radiance Fields (NeRF) [26] have emerged as a leading technology, leveraging implicit scene representations through neural networks and volume rendering generate novel views. While NeRF-based methods excel in producing high-quality renderings with compact model sizes, they hindered by slow rendering speeds. In recent advancements, the 3D Gaussian Splatting (3DGS) [15] method has emerged as a compelling alternative to NeRF-based approaches, enabling real-time rendering of high-resolution novel views. Unlike NeRF, which relies on continuous neural networks, 3DGS employs set of explicit, optimizable 3D Gaussians to represent scenes. This approach is able bypass the computational overhead of volume rendering by leveraging an achieving real-time performance while
efficient differentiable paint-based splatting process [57 52
enhancing rendering quality.
However, 3DGS suffers from significant memory overhead due to its parameter-intensive representa- tion of view-dependent colors and anisotropic shapes. Each 3D Gaussian requires 59 parameters, with 48 parameters dedicated to view-dependent color representation via spherical harmonics and 7 parameters encoding anisotropic scale and rotation. This stands in stark contrast to NeRF-based
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

---

Previous methods
Ground Truth
MipNeRF-360 Missing detallls, low apoed
3DG$
Neural Field 3DGS
Lango si2e, Blury Background
Missing detaila, la, Blunt beic backkgound
0.06 FPS 8.6 MB
125 FPS 240 MB
117 FPS 32 MB
Ours methods (HyRF)
Full Rendering 118 FPS 37 MB
Neural Gaussians
Explicit Caussians
Background Map
Figure 1: Mip-NeRF360 B struggles with inaccuracies in fine details and slow rendering speeds, while 3DGS [15] face challenges of large model sizes and blurry background. naive combination of neural fields and 3DGS leads to loss of high-frequency information. Our method overcomes these challenges through an innovative hybrid architecture. By synergistically combining neural fields, explicit Gaussians, and neural background map, we achieve competitive or superior performance in both visual quality and model compactness, while maintaining real-time rendering capabilities.
methods, which efficiently model view-dependent effects through neural network conditioning with minimal parameter growth.
A natural approach to reducing 3DGS storage costs is to encode 3D Gaussian properties in grid-based neural fields (49 42 However, this method faces a fundamental limitation: the fixed resolution of grid-based representations struggles to capture the high-frequency spatial variations in 3D Gaussian properties. This issue is particularly pronounced when modeling scenes with rapid opacity and scale changes at object boundaries or figh-frreyency view-dependent effects. As a result, naively fitting 3D Gaussians to neural fields often fails to reconstruct fine details, such as thin geometric structures and fre-eeryy color variations.
In this paper, we present Hybrid Radiance Fields (HyRF), novel scene representation that effec- tively addresses the frequency limitations of neural Gaussian approaches while maintaining low memory overhead. Our key insight is to decompose the representation into two complementary components: grid-based neural fields that capture low-frequency variations, and sparse set of explicit compact Gaussians that preserve high-frequency details. Our neural component employs decoupled architecture with two specialized neural fields: a geometry network dedicated modeling geometric Gaussian properties (scale, opacity, and rotation), and a separate appearance network for view-dependent color prediction. This explicit disentanglement of geometric and photometric learning objectives significantly enhances representational capacity of neural fields while maintaining parameter efficiency. Meanwhile, our explicit Gaussian component stores only essential properties, ie., 3D positions, isotropic scales, opacity values, and diffuse colors, in order to minimize memory overhead while preserving critical scene details.
To achieve both efficiency and rendering quality, we propose a in three stages. First, our visibility pre-culling module eliminates Gaussians outside the current view frustum, significantly reducing computational overhead of querying neural fields. Next, we process the remaining visible Gaussians by querying their positions through our neural field to predict neural Gaussian properties, which are then combined with the stored explicit parameters to recover freeufrry details. To address the insufficient background modeling of Gaussian representations, we implement a learnable solution where the neural field generates a background map projected onto a background sphere. This background map is composited with the foreground Gaussian rendering through alpha blending, therefore achieves high visual quality for both foreground and remote background objects.
a hybrid rendering pipeline that operates
In summary, our key contributions include: (i) A novel integration of neural fields with explicit compact Gaussians, preserving high- figh-ffreuency details while minimizing memory overhead. (i) A

---

dual-field architecture that improves the modeling of Gaussian properties by disentangling geometry and view-dependent effects. (iii) A hybrid rendering strategy that reduces computational overhead and improves rendering quality for backgrounds. (iv) Extensive experiments demonstrate that our method achieves superior rendering quality, reduces model size by 20x compared to 3DGS [15] and maintains real-time performance.
2
Related Work
revolutionized novel view synthesis by modeling scenes as volumetric radiance fields, where each point in space is associated with radiance and density values through a multi-layer perceptron (MLP). The state-of-the-art MLP-based method, Mip-NeRF360 [3]. has achieved significant improvements in anti-aliasing and handling unbounded scenes. However, ML.P-base radiance fields suffer from slow training and rendering speeds due to the extensive querying required for volume rendering. To address these inefficiencies. recent approaches have integrated NeRF with structured arrays of learnable features [2] 53 37 41 For
Neural Radiance Fields. Neural Radiance Fields (NeRF) 26
instance, TensoRF [4] employs tensor decomposition to represent scenes using compact low-rank tensor components, while Instant-NGP (28) combines a multi-resolution hash table with a fully-fused
MLP (27), significantly accelerating rendering. Despite these advancements, grid-based methods still face challenges in achieving real-time rendering and matching the quality of MLP-baaeed approaches, often due to limited grid resolution hash collisions.
48 explores replacing implicit neural fields with explicit, point-based scene representations, which can be rendered more efficiently using rasterization techniques. Notably, 3D Gaussian Splatting (3DGS) [5] introduced scene representa- tion based on 3D Gaussians, synthesizing novel views through point-based alpha blending [57]. This approach achieves state-of-the-an rendering quality and real-time performance. However, the size of models using 3D Gaussian representations is always considerably larger than NeRF-based methods.
Explicit Radiance Fields. Another line of research
Compressed 3D Gaussian Splatting. While 3D Gaussian Splatting (3DGS) achieves superior rendering performance compared to NeRF-based methods, its significantly larger model size has motivated research into compact representations that preserve its performance advantages. Exist- ing approaches fall into two main categories: (1) parameter compression techniques using vector quantization [7 29, and (2) hybrid neural-3DGS architectures (30
that uses neural components to predict 3D Gaussian properties instead of explicitly storing them. Closely related to our work, Scaffold-GS (23 employs anchor points with neural features to predict local Gaussian properties, achieving superior compactness while maintaining rendering quality. Our approach differs fundamentally by predicting all Gaussian properties globally through grid-based neural fields, while augmenting high-frequency details with explicit residual Gaussians. This architecture enables both superior compression ratios and enhanced view quality. Furthermore, our method remains compatible with vector quantization techniques, achieving additional efficiency gains since explicit Gaus- sians contain far fewer parameters than conventional 3DGS representations. Recently, LocoGS [40] explores similar idea by storing Gaussian properties in neural fields. In contrast, method stores explicit residuals for Gaussian shapes and introduces decoupled neural fields, leading to improved representation of high-frequency scene components.
3 Methodology
3.1 Preliminary: 3DGS
In 3DGS, a scene is depicted through a collection of optimizable 3D Gaussians. Each Gaussian is defined by its 3D coordinates p. opacity a, rotation r, scaling factor and color c. The opacity is defined as a scalar value ranging from 0 to 1. The size of the Gaussian in 3D is indicated by scale s. Rotation is expressed as quaternion r. The color view-dependent effects, which then converted into an RGB color before rasterization.
set of spherical harmonics to account for
3DGS uses 3D points obtained from 3D Gaussians and adaptively densifies them based on the accumulated gradients. During rendering, the 3D Gaussians ordered by depth, projected onto 2D image planes, and combined using the
libraries like COLMAP 38 39 as initial
3

---

Scene Representation Field
View Rendering
Gea Field
Neuml Rotation R**
Rotation A²
Hybrid Gauesian
Explicit Scale E it
a
Scale E H?
Scale R?
Decoupled Neural
3D Points
Opacity
Add
Opacity E R
Explicit
(()
Opacity E
Explict Scale 8
Neural
Add
Explicit
g
Colore R²
Explisit Opecity R
Rad. Field
View direction
Foreground
Backkrouudd
Background
Colors X*
Sample
Com
Background apherre
Explielt Goussians
Hybrid Rondering
Figure 2: Framework overview. Our method represents the scene using grid-based neural fields and of compact explicit Gaussians storing only 3D position, 3D diffuse color, isotropic scale, and opacity. We encode the point position into a nigh-dimensional feature using the neural field and decode into Gaussian properties with tiny MLP. These Gaussian properties are then aggregated with the explicit Gaussians and integrated into the 3DGS rasterizer.
following point-based alpha-blending method.
C= Σ c.a; II az),
(1)
iEN
j=1
where C' is the final predicted pixel color, M is the set of sorted Gaussians projected onto the pixel.
3.2 Hybrid Radiance Fields
Our method represents scene using 1) explicit set of 3D Gaussians each holds only parameters,
including positions P. E R3, diffuse color сę E R3, isotropic scale $e E R and opacity a E R.
and 2) compact grid-based neural field. We choose the multi-resolution hash encoding (28) as neural field for its efficiency and strong performance. An overview illustrated in Fig. 2
Empirical results demonstrate that predicting all Gaussian properties through a single neural field fails achieve satisfactory performance. We attribute this limitation to the weak correlation between Gaussian geometry and appearance attributes, which makes them hard to be learned jointly within a single neural field. address this issue, we propose decoupled neural field architecture, which predicts geometry properties (scale, opacity and rotation) and appearance property (view-dependent color) with two separate neural fields Ggeo and Orad-
Decoupled neural fields:
Given the position of a 3D point p,, we first employ a scene contraction technique similar to that in MipNeRF360 [3] constrain the input coordinates. We first normalize the coordinates using the axis-aligned bounding box (AABB) B, of the scene, which we defined the minimum and maximum camera positions. Next, we contract the normalized points the range (0, 1) using the following formula:
0.25 +
if |p: <
contract (p:)
0.25
otherwise.
| P.
Note that we contract the points to (0, 1) instead of (-2 to meet the input requirements for the multi-resolution hash 28
Then we the decoupled neural fields to encode it into two high-dimensional features:
Fiud enc(p;; Orad), gro enc(p;; Ogen),
(3)
where aad and geo are the encoded features.

---

Foreground Gra
Beckground Gog
Viewpoint
Gra
Caa
Gr + (1 am
View
Viewoint
Frustum
Beckground
Viewpaint
Sphere
Visibility pre-culling
(b) Hybrid rendering
Figure (a) Visibility Pre-Culling. We first determine whether each Gaussian lies within the current view frustum before applying neural field decoding. (b) Hybrid Rendering Pipeline. For each camera ray, we: (1) compute its intersection point pa with background sphere, (2) sample the radiance field at and (3) composite the foreground and background colors using alpha blending.
The encoded features are then decoded into 3D Gaussian properties using two MI.P-based decoders. For view-independent properties of opacity scale and rotation directly decoded them as:
(Oh 8n rn) dec eneee
& gen
(4)
To account for the view-dependent effects of Gaussian colors, we incorporate a view direction component to the MLP input using positional encoding techniques similar to NeRF-based methods 228 3 The view direction encoding is calculated as:
P Peam
dir = PE
(5)
pp Peam
where PE(.) is positional encoding technique [26]. The view-dependent color is decoded as:
Cn = dec(f + &e),
(6)
where denotes tensor concatenation. Note the derived neural Gaussian properties (an, Fny 8uz Cn)
here are raw outputs from MLP without activations.
Grid-based neural fields often overlook high- frequencyy scene components such as intrinsic structures. We address this problem by aggregating the predicted properties from neural fields with explicit properties stored in each Gaussian. Similar to 3DGS, apply the sigmoid function to activate opacity and color, and use a normalization function for rotation:
Aggregation with explicit Gaussians:
aann Ote)s (C,, + Ca), Normalize(rn):
8 = (sn 8c)
(7)
where denotes the sigmoid function, and Normalize(-) denotes L2 normalization of the quaternion. The aggregated Gaussian properties (ar, r, 8, c) are then fed to the 3DGS rasterizer.
3.3 Hybrid Rendering
Visibility pre-culling: To reduce the computational overhead of querying the neural fields, climinate points that will not be projected onto the image plane before deriving their properties using the neural fields. An illustration of the visibility pre-culling process is provided in Fig. 3 a). Specifically, given point p, and camera viewpoint, we calculate the camera-space coordinates of the point p, using the camera's rotation matrix R E R and translation vector t E R follows:
p
Rp; + t.
We retain a point only if it is projected within the image frame, determined by the condition:
(|ayl < 1 + tol) A (lysl < 1 + tol),
where v; and yi are the first and second elements of p,, respectively. We incorporate tolerance band tol in the culling process preserve Gaussians that partially projected outside but still intersect
5

---

with the image plane. Additionallyy, we discard Gaussians positioned too close to the image plane, as they may introduce optimization instability.
Background rendering: 3DGS often struggle to effectively densify and optimize extremely distant objects, frequently resulting in blurry backgrounds. To address this issue, we propose a hybrid rendering technique that leverages the radiance field rrd to predict the background color. An illustration of the background rendering process is provided in Fig. 3b).
Unlike 18], which predicts the background as points at infinity, we construct a background sphere with large radius For each ray projected from a given camera viewpoint, compute the intersection point P. between the ray and the sphere. We then use the radiance field and decoder to predict the color at point Ps. The background color Cbg combines the background point color with remaining visibility after accumulating the foreground Gaussians:
Cbg II as)ec. ==1
(10)
Finally, the pixel color is obtained by combining the foreground and background colors:
C²= + Cbg.
(11)
where Cig is given by Eq In the rendering stage, we predict Obg only for pixels with an accumulated transmittance T = II a.) lower than threshold Tr, thereby increasing rendering speed.
3.4 Optimization
Our method is optimized using the same L1 loss and SSIM loss [46 as the original 3DGS:
X XL
(12)
where À is the weight for SSIM loss. Similar the original 3DGS, we periodically reset the explicit opacity to a small value during densification and prune Gaussians with low opacity.
Experiments
4.1 Experimental Setup
Dataset:
We conduct experiments on three standard real-world datasets: MipNeRF360 B. Tanks &
Temples [16), and Deep Blending [13], which together encompass total of 13 scenes. Additionally, we utilize the NeRF Synthetic dataset [26). featuring object-centered scenes. Furthermore, we examine two large-scale urban datasets captured by drones: Mill19 [44] and Urbanscene3D (20),
which collectively include scenes. In total, our experiments span 25 scenes across various datasets.
Baselines: For the MipNeRF360 [3) Tanks & Temples [16.. and Deep Blending 13 datasets, we compare our method with the MLP-based NeRF method MipNeRF360 [3]. two popular grid- based NeRF
8 and Instant-NGP (88 aas well as the original 3DGS [5] and
its advanced derivative, Scaffold-GS 23 For the NeRF Synthetic dataset (26 we compare our method with MipNeRF (2). Instant-NGP 228 3DGS [15), and Scaffold-Gs (3) For the urban-scale datasets 20], we evaluate our method with two prominent NeRF-based techniques: MegaN- eRF 44 and SwitchNeRF [25] in addition to 3DGS [15] and Scaffold-Gs, 23 To demonstrate the
compactness of our method, we also compare a compressed version of our approach with five recent
3DGS compression methods 22 17 29
Implementation: Our method is built on top of the original 3DGS implementation. For the neural fields, we adopt multi-resolution hash encodings (28) with 16 levels, where each hash entry stores feature of size 2. The maximum hash size per level for the radiance field is set to 2n7 for synthetic scenes, 218 for standard scenes, and 2 for large scenes. The hash size for the geometry field is half that the radiance field. For the decoder, we use a fully-fused MLP 27 with 2 hidden layers, each containing 64 neurons. For background rendering,
set the transmittance threshold 7 to 0.2 and r 100 for all scenes. All other hyperparameters remain consistent with the original 3DGS. All experiments are conducted one NVIDIA 3090 GPU.
Evaluation metrics: We evaluate rendering quality of novel view synthesis using PSNR, SSIM 46 and LPIPS [56). We also report the rendering frame rate (FPS) and model size in MB.

---

MipNoRF-380
3DGS
Scaffold
Ours
Ground Truth
Figure 4: Qualitative comparisons of our method against previous approaches standard real-world datasets 16 13). The selected scenes include the bicycle and counter scenes from the MipNeRF360 dataset 2) the playroom scene from the DeepBlending dataset [13], and the truck scene from the Tanks & Temples dataset [6]. Arrows and insets are used to highlight key differences.
Table 1: Quantitative evaluation of our method compared to previous works on the MipNeRF360 3
Tanks & Temples [16), and Deep Blending [3] datasets. We consistently achieve the best rendering
quality, with model sizes comparable to NeRF-based methods and rendering speeds similar to 3DGS- based methods. The best results are indicated in bold, while the second-best results are underlined.
Dataset
Mip NeRF360 3]
Temps&Temppes 1IPPS
1D
Deep Blending [3]
SSIM
LPIPS
FPS
SizetMB)
PSNR!
FPS
Stac/BB)
SSIM!
LPIPS
SiarMBa/
Instm--NGPP
26
25.59
0.609
0.331
9.43
.45
21.92
0.745
0.305
14.4
24.96
0.517
0.390
2.79
M NeRI260
27.69
0.792
0.237
86
22.22
0.759
0.257
8.6
29.40
0.501
0.245
0.09
3DGS
27.21
0.815
0314
23.
0.841
0.183
29:41
0.903
0.243
112
Scatlold GS
27.39
0,806
0.252
244
23.96
0.853
0.172
98
$6.5
30.21
0.506
0.254
120
66
Ours
27.78
0.211
24,02
0.176
30.37
0.910
0.341
4.2 Results and Evaluation
Standard real-world scenes: Table I presents the quantitative results evaluated on real-world scenes. Our method achieves state-of-the-art rendering quality while maintaining a compact model size and real-time rendering speed. Compared 3DGS [5]. our method delivers superior rendering quality while reducing the model size by over 12 times and maintaining comparable rendering speed. When compared to Scaffold-Gr GS [23], our method shows significant improvements in rendering quality, with model sizes 1.5 to 5 times smaller and faster rendering speeds.
Qualitative comparisons between our method and previous ap- proaches are illustrated in Fig. 4 Our method excels in capturing fine details, demonstrated the bicycle, counter, and playroom scenes, while also achieving better background modeling, as seen
Table Comparison the NeRF Synthetic dataset (26).
PSNR
Size/MD)
MipNeRF 2
32.63
2.4
in the truck scenes.
Instani-NGP
33.18
12
3DGS [15]
33.32
53
Object-centered synthetic scenes: Table, presennts the qualitative results the NeRF Synthetic (26] dataset. Our method achieves the best results among all the comparison methods, with a size slightly larger than Instant-NGP (28) and over 4 times smaller than 3DGS.
Scaffold-Go GS
33.68
23
Ours
33.72
Large-scale real-world scenes: Table. presents the qualitative results for two urban-scale datasets 44 20). Our approach achieves superior rendering quality with more compact model size
7

---

Table 3: Quantitative evaluation of our method compared previous works two urban-scale datasets: Mill19 44 and Urbanscene3D (20] dataset. Our method achieves the best rendering quality among all compared methods, being 4 to 7 times smaller than 3DGS- methods and over 7000
times faster than NeRI-based methods.
Dataset
Mil19 [44]
Urbanscene3D
20
PSNR
SSIM
LPIPS*
FPS
Size(MB)
PSNR
SSIM
LPIPS*
FPS
Size(MB)*
MegaNeRI 44
22.50
0.55
0.510
<0.01
32
23.84
0.699
0.440
<0.01
32
SwitchNRRF
25
22.93
0.571
0.485
<0.01
17
24.54
0.725
0.418
<0.01
17
3DGS (15]
22.41
0.695
0.348
81
1566
21.41
0.763
0.287
84
935
Scaffold-GS
22.33
0.658
0.339
560
0.295
435
Oun
23.52
0.709
0.319
75
215
24.68
0.791
0.272
77
202
Table 4: Quantitative evaluation of our method compared to previous 3DGS compression work on the MipNeRF-360 dataset 3
Table 5: Ablation studies of the key compo- nents of our method on the Tanks & Temples dataset [16].
PSNR
SSIM
LPIPS-
Size(MBB)
PSNR
SSIM
L.PIPS
FPS
Size(MBj*
Niodermayy al.
811
26.98
0
0.238
28.84
Full modkl
24.07
0.847
0.175
106
41
al.
27.08
0.247
wio Decouple.
23.78
0.187
101
Chen et al.
Girish et al.
Papuntonakis ct al.
35
27.15
27.1
0.808
0.808
0.809
0.234
0.228
0.226
25.40
68.10
wlo Background
wio Explicit
wio Nesuaal
22.22
23.43
23.45
0.797
0.829
0.206
0.196
0.19
121
127
27
14
Ours
27.66
0.814
0.210
18.04
wio Pre Pre-cuiling
24.06
0.847
0.175
27
41
compared to 3DGS. Notably, the gap of rendering speed between our method and 3DGS narrows as the number of rendered points increases. In contrast, Scaffold-GS experiences significant decline in speed as the number of Gaussians grows. A qualitative comparison is can be found in the supple- mentary materials, where our method demonstrates better ability capture fine details and handle lighting variations, where 3DGS and Scaffold-GS suffers from blurs and artifacts.
Model compression:
Though our method does not inherently include post-processing compression techniques, it remains compatible with most existing 3DGS compression approaches [2 17 Our representation achieves better performance by storing significantly fewer explicit Gaussian parameters.
To evaluate our method's 's compactness, we apply post-processing techniques similar to [17), including:
(I) storing point positions half- malff-rreciion tensors, (2) applying residual vector quantization (R- VQ) and Huffman encoding to explicit Gaussian properties, and (3) employing Huffman encoding with 8-bit min-max quantization for the hash table (see supplementary materials for details).
As shown in Table 4 our compressed results outperform five state-of-the-arr 3DGS compression methods in both model size and rendering quality. Notably, while conventional 3DGS compression methods typically sacrifice rendering quality for storage efficiency, our approach maintains superior visual fidelity even after aggressive compression.
4.3 Model Analysis
We conduct comparative analysis between decoupled neural fields approach and a single neural field that predicts all Gaussian parameters simultaneously. To maintain experimental faireess, we configure the maximum hash size of the single neural field to be 2 which leads to a slightly larger parameter count as our decoupled architecture. As demonstrated in Table the single neural field exhibits consistent degradation across all image quality metrics. This limitation arises from the inherent challenge of using a single network to concurrently represent both geometric and appearance properties of 31) Gaussians, resulting in compromised rendering fidelity and inaccurate geometry such as gaps and holes, as visually confirmed in Fig. 5
Decoupled neural fields:
Hybrid rendering: We evaluate model using two rendering approaches: (1) our proposed hybrid rendering pipeline and (2) conventional 3DGS rasterization. Quantitative results in Table show that disabling background rendering results in significantly degraded visual quality, despite offering only marginal improvements in rendering speed. This finding supports our hypothesis that standard 3DGS approaches struggle to properly densify and optimize distant objects. As shown in Fig. 6 our qualitative analysis further reveals that background rendering plays a crucial role in maintaining high-frequency details for distant scene elements, with particularly notable of fine cloud structures.
Neural Gaussians: Our method leverages neural fields to predict the anisotropic shape and view- dependent color of 3D Gaussians. Without these neural components, our framework falls back to
8

---

decouple
w/o background
Full
Figure 5: Ablation of decoupled neural fields. Using a single neural field predict Gaussian properties causes gaps and holes.
Figure 6: Ablation of background rendering.
The learnable background map improves the qual- ity of distant objects (see the clouds and sky).
w/o explicit color
w/o explicit scale
PSNN CONNOO (88)
3DGS
Scaffold-GS
Ours
w/o explicit opacity
Full model
Modal (MB))
100
Wallclock Time (6)
Figure 7: Detailed ablation studies of each of the explicit Gaussian properties.
Figure Comparison PSNR and model size changes during the training phase.
isotropic Gaussians with diffuse shading which has limited representation capacity, leading noticeable degradation novel view synthesis quality, as demonstrated in Tab.
Visibility pre-culling: As demonstrated in Table our frustum pre-culling strategy achieves a 3.9x rendering speed improvement while maintaining equivalent visual quality for real-world 360 scenes, which represent our primary target scenario.
Training time: We analyze the training time of our method and compare it with other baseline methods in Fig. 8 Our method achieves significantly faster convergence, while maintaining a substantially smaller model size compared baselines.
Explicit Gaussians: In Table 5, we evaluate the impact of removing all explicit Gaussian properties except posi- tions, which are retained as they are required for neural field queries. We analyze the contribution of each explicit Gaussian component-colun scale, and througghh systematic ablation. Visual comparisons Blending dataset [13 are presented in Fig. 7 Remov- ing explicit color components causes noticeable quality deterioration, as the neural network struggles to model illumination variations and may produce unnatural colors
Table 6: Detailed ablation studies of each of the explicit Gaussian properties.
PSNR
SSIM
LPIPS
Full model
30.37
0.910
0.241
the Deep
w/o color.
29.18
0.896
0.251
w/o w/o opacity
28.74
0.282
30.21
0.902
0.247
due to hash collisions. The absence of explicit scale significantly impairs reconstruction of thin structures like edges and corners. We also observes removing of explicit scale often Ieads to instability in training. Finally, removing explicit opacity results in floaters, which also degrades output quality.
5 Conclusion
We have presented Hybrid Radiance Fields (HyRF), novel approach that bridges the gap between the rendering efficiency of 3D Gaussian Splatting and the compact representation of neural fields. Our work addresses the fundamental limitations of current novel view synthesis methods by introducing hybrid explicit-implicit representation that preserves high-frequency details, decoupled neural field architecture that separately optimizes geometric and appearance properties, and hybrid rendering pipeline that effectively combines the strengths of both representations. Our approach resolves the memory bottleneck of explicit Gaussian representations without sacrificing their rendering quality

---

speed advantages. As novel view synthesis continues to play crucial role in diverse applications from virtual production autonomous systems, we believe our contributions represent significant step toward practical, high-quality real-time neural rendering.
Limitations: As in the original 3DGS, our present method does not address the aliasing issue [54 and sometimes produces inaccurate surface reconstruction. Moreover, the neural field components in HyRF currently benefit from high-end GPUs for high rendering speed. Achieving comparable efficiency on web platforms or integrated graphics remains an open challenge for the community.
Acknowledgments and Disclosure of Funding
The work supported in part by the Early Career Scheme of the Research Grants Council (RGC) of the Hong Kong SAR under grant No. 26202321, ITF PRP/046/24FX, SAIL Research Project, and HKUST-Zeekr Collaborative Research Fund.
References
[1] Kara-Ali Aliev, Artem Sevastopolsky, Maria Kolos, Dmitry Ulyanov, and Victor Lempitsky. Neural point-based graphics. In ECCV, 2020.
[2] Jonathan T Barron, Ben Mildenhall, Matthew Tancik, Peter Hedman, Ricardo Martin Bartin-Brualla, and Pratul P Srinivasan. Mip-nerf: multiscale representation for anti-aliasing neural radiance fields. In CVPR, 2021.
[3] Jonathan T Barron, Ben Mildenhall, Dor Verbin, Pratul P Srinivasan, and Peter Hedman. Mip-nerf 360: Unbounded anti-aliased neural radiance fields. In CVPR, 2022.
[4] Anpei Chen, Zexiang Xu, Andreas Geiger, Jingyi Yu, and Hao Su. Tensorf: Tensorial radiance fields. In ECCV, 2022.
[5] Youyu Chen, Junjun Jiang, Kui Jiang, Xiao Tang, Zhihao Li, Xianming Liu, and Yinyu Nie. Dashgaussian: Optimizing 3d gaussian splatting in 200 seconds. In Proceedings of the Computer Vision and Pattern Recognition Conference, pages 11 11146-111 1155, 2025.
[6] Yihang Chen, Qianyi Wu, Weiyao Lin, Mehrtash Harandi, and Jianfei Cai. Hac: Hash-grid assisted context for 3d gaussian splatting compression. In ECCV, 2025.
[7] Guangehi Fang and Bing Wang. Mini-splatting:: Building 360 scenes within minutes via aggressive gaussian densification. arXiv preprint arXiv:2411.12788 2024.
[8] Sara Fridovich-Keii Alex Yu, Matthew Tancik, Qinhong Chen, Benjamin Recht, and Angjoo Kanazawa, Plenoxels: Radiance fields without neural networks. In CVPR, 2022.
[9] Sara Krilo Giacomo Meanti, Frederik Rahbæk Warburg, Benjamin Recht, and Angioo Kanazawa. K-planes: Explicit radiance fields in space, time, and appearance. In CVPR, 2023.
[10] Andreas Geiger, Philip Lenz, Christoph Stiller, and Raquel Urtasun. Vision meets robotics: The kitti dataset. The international journal of robotics research, 32(11):22111123, 2013.
[11] Sharath Girish, Kamal Gupta, and Abhinav Shrivastava. Eagles: Efficient accelerated 3d gaussians with lightweight encodings. arXiv preprint axXiv:2312.04564, 2023.
[12] Ayaan Haque, Matthew Tancik, Alexei A Efros, Aleksander Holynski, and Angjoo Kanazawa. Instruct-nerfZnerff: Editing 3d scenes with instructions. In CVPR, 2023.
[13] Peter Hedman, Julien Philip, True Price, Jan-Michael Frahm, George Drettakis, and Gabriel Brostow. Deep blending for free-viewpoint image-based based rendering. TOG, 2018.
[14] Ajay Jain, Ben Mildenhall, Jonathan T Barron, Pieter Abbeel, and Ben Poole. Zero-shot text-guided object generation with dream fields. In CVPR, 2022.
[15] Bernhard Kerbl, Georgios Kopanas, Thomas Leinkühler, and George Drettakis. 3d gaussian splatting for real-time radiance field rendering. ToG, 2023.
10

---

[16] Arno Knapitsch, Jaesik Park, Qian-Y Yi Zhou, and Vladlen Koltun. Tanks and temples: Bench- marking large-scale scene reconstruction. TOG, 2017.
[17] Joo Chan Lee, Daniel Rho, Xiangyu Sun, Jong Hwan Ko, and Eunbyung Park. Compact 3d gaussian representation for radiance field. CVPR, 2024.
[18] Wanzhang Li, Fukun Yin, Wen Liu, Yiying Yang, Xin Chen, Biao Jiang, Gang Yu, and Jiayuan Fan. Unbounded-ge -g: Extending 3d gaussian splatting with bybrid representation for unbounded large-scale scene reconstruction. IEEE Robotics and Automation Letters, 2024.
[19] Chen-Hsuan Lin, Jun Gao, Luming Tang, Towaki Takikawa, Xiaohui Zeng. Xun Huang, Karsten Kreis, Sanja Fidler, Ming-Yu Yu Liu, and Tsung-Yi Yi Lin. Magic3d: High-resolution text-to-3d content creation. In CVPR, 2023.
[20] Liqiang Lin, Yilin Liu, Yue Hu, Xingguang Yan, Ke Xie, and Hui Huang. Capturing, recon- structing, and simulating: the urbanscene3d dataset. In ECCV, 2022.
[21] Lingji Liu, Jiatao Gu, Kyaw Zaw Lin, Tat-Seng Chua, and Christian Theobalt. Neural sparse voxel fields. NeurIPS, 2020.
[22] Xiangrui Liu, Xinju Wu, Pingping Zhang, Shiqi Wang, Zhu Li, and Sam Kwong. Compgs: Efficient 3d scene representation via compressed gaussian splatting. arXiv preprint arXiv:2404.099 09458, 2024.
[23] Tao Lu, Mulin Yu, Linning Xu, Yuanbo Xiangli, Limin Wang, Dahua Lin, and Bo Dai. Scaffold- gs: Structured 3d gaussians for view-adaptive rendering. CVPR, 2024.
[24] Ricardo Martin-Brualla, Noha Radwan, Mehdi SM Sajjadi, Jonathan T Barron, Alexey Dosovit- skiy, and Daniel Duckworth. Nerf in the wild: Neural radiance fields for unconstrained photo collections. In CVPR, 2021.
[25] Zhenxing Mi and Dan Xu. Switch-nerf: Learning scene decomposition with mixture of experts for large-scale neural radiance fields. In ICLR, 2022.
[26] Ben Mildenhall, Pratul P Srinivasan, Matthew Tancik, Jonathan T Barron, Ravi Ramamoorthi, and Ren Ng. Nerf: Representing scenes neural radiance fields for view synthesis. ECCV, 2021.
[27] Thomas Müller. tiny-cuda-mi -nn 2021.
[28] Thomas Müller, Alex Evans, Christoph Schied, and Alexander Keller. Instant neural graphics primitives with multiresolution hash encoding. TOG, 2022.
[29] KL Navaneet, Kossar Pourahmadi Meibodi, Soroush Abbasi Koohpayegani, and Hamed Pirsi- avash. Compact3d: Compressing gaussian splat radiance field models with vector quantization. arXiv preprint arXh::2311.18159 2023.
[30] KL Navaneet, Kossar Pourahmadi Meibodi, Soroush Abbasi Koohpayegani, and Hamed Pir- siavash. Compgs: Smaller and faster gaussian splatting with vector quantization. In ECCV, 2024.
[31] Simon Niedermayr, Josef Stumpfegger, and Rüdiger Westermann. Compressed 3d gaussian splatting for accelerated novel view synthesis. In CVPR, 2024.
[32] Michael Niemeyer and Andreas Geiger. Giraffe: Representing scenes as compositional genera- tive neural feature fields. In CVPR, 2021.
[33] Takashi Otonari, Satoshi Ikehata, and Kiyoharu Aizawa. Entity-nerf: Detecting and removing moving entities in urban scenes. In CVPR, 2024.
[34] Jingyi Pan, Zipeng Wang, and Lin Wang. Co-oce: Coupling explicit feature fusion with volume rendering regularization for multi-modal 3d semantic occupancy prediction. RAL, 2024.
11

---

[35] Panagiotis Papantonakis, Georgios Kopanas, Bernhard Kerbl, Alexandre Lanvin, and George Drettakis. Reducing the memory footprint of 3d gaussian splatting. Proceedings of the ACM on Computer Graphics and Interactive Techniques, 7(1):1-17, 2024.
[36]
Ben Poole, Ajay Jain, Jonathan T Barron, and Ben Mildenhall. Dreamfusion: Text-to-3d using 2d diffusion. arXiv preprint arXiv:2209. 14988, 2022.
[37] Christian Reiser, Rick Szeliski, Dor Verbin, Pratul Srinivasan, Ben Mildenhall, Andreas Geiger, Jon Barron, and Peter Hedman. Merf: Memory-efficient radiance fields for real-time view synthesis in unbounded scenes. TOG, 2023.
[38] Johannes Lutz Schönberger and San-Michael Frahm. Structure-from from--otioommnnnoooo revisited. In CVPR, 2016.
[39] Johannes Lutz Schönberger, Enliang Zheng, Mare Pollefeys, and Jan- Michaell Frahm. Pixelwise view selection for unstructured multi-view stereo. In ECCV, 2016.
[40] Seungjoo Shin, Jaesik Park, and Sunghyun Cho. -eeality-wware gaussian compression for fast and high-quality rendering. International Conference on Learning Representations, 2025.
[41] Cheng Sun, Min Sun, and Hwann-Tzome Chen. Direct voxel grid optimization: Super-fast convergence for radiance fields reconstruction. In CVPR, 2022.
[42] Xiangyu Sun, Joo Chan Lee, Daniel Rho, Jong Hwan Ko, Usman Ali, and Eunbyung Park. F-3dgs: Factorized coordinates and representations for 3d gaussian splatting. arXiv preprint arXiv:2405. 7083, 2024.
[43] Adam Tonderski, Carl Lindström, Georg Hess, William Ljungbergh, Lennart Svensson, and Christoffer Petersson. Neurad: Neural rendering for autonomous driving. In CVPR, 2024.
[44] Haithem Turki, Deva Ramanan, and Mahadev Satyanarayanan, Mega-nerf: Scalable construc- tion of lcrge-scale nerfs for virtual fly-throughs. In CVPR, 2022.
[45] Yuxin Wang, Wayne Wu, and Dan Xu. Learning unified decompositional and compositional nerf for editable novel view synthesis. In CVPR, 2023.
[46] Zhou Wang, Alan C Bovik, Hamid Sheikh, and Eero Simoncelli. Image quality assessment: from error visibility to structural similarity. TIP, 2004.
[47] Silvan Weder, Guillermo Garcia-Hernande Aron Monszpart, Marc Pollefeys, Gabriel J Brostow, Michael Firman, and Sara Vicente. Removing objects from neural radiance fields. In CVPR, 2023.
[48] Olivia Wiles, Georgia Gkioxari, Richard Szeliski, and Justin Johnson. Synsin: End-to-end view synthesis from a single image. In CVPR, 2020.
[49] Minye Wu and Tinne Tuytelaars. Implicit gaussian splatting with efficient multi-level tri- tri-plane representation. arXiv preprint arXiv:2408. 20024008.004,, 2024.
[50] Qiangeng Xu, Zexiang Xu, Julien Philip, Sai Bi, Zhixin Shu, Kalyan Sunkavalli, and Ulrich Neumann. Point-nerf: Point-based neural radiance fields. In CVPR, 2022.
[51] Ziyi Yang, Xinyu Gao, Yang-Tian Sun, Yihua Huang, Xiaoyang Lyu, Wen Zhou, Shaohui Jiao, Xiaojuan Qi. and Xiaogang Jin. 3d gaussian splatting. Advances in Neural Information Processing Systems, 37:61 17261192. 2024.
Anisotropic view-dependent appearance for
[52] Wang Yifan, Felice Serena, Shihao Wu, Cengiz Öztireli, and Olga Sorkine Hoakine.Hoonugg... Differen- tiable surface splatting for point-based geometry processing. TOG, 2019.
[53] Alex Yu, Ruilong Li, Matthew Tancik, Hao Li, Ren Ng, and Angjoo Kanazawa. Plenoctrees for real-time rendering of neural radiance fields. In CVPR, 2021.
[54] Zehao Yu, Anpei Chen, Binbin Huang, Torsten Sattler, and Andreas Geiger. Mip-splatting: Alias-free 3d gaussian splatting. In CVPR, 2024.
12

---

[55] Zehao Yu, Torsten Sattler, and Andreas Geiger. Gaussian opacity fields: Efficient adaptive surface reconstruction in unbounded scenes. ACM Transactions on Graphics (ToG), 43(())1-13,, 2024.
[56] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreason- able effectiveness of deep features as perceptual metric. In CVPR, 2018.
[57] Matthias Zwicker, Hanspeter Pfister, Jeroen Van Baar, and Markus Gross. Ewa splatting. TVCG, 2002.
13

---

NeurIPS Paper Checklist
1. Claims
Question: Do the main claims made in the abstract and introduction accurately reflect the paper's contributions and scope?
Answer: [Yes]
Justification: Please refer to our methodology and experiments sections.
Guidelines:
The answer NA means that the abstract and introduction do not include the claims made in the paper.
The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. A No or
NA answer to this question will not perceived well by the reviewers.
The claims made should match theoretical and experimental results, and reflect how much the results
be expected to generalize other settings.
is fine to include aspirational goals as motivation as long is clear that these goals are not attained by the paper.
2. Limitations
Question: Does the paper discuss the limitations of the work performed by the authors?
Answer: [Yes]
Justification: Please refer to the limitation section in our conclusion.
Guidelines:
The answer NA means that the paper has no limitation while the answer No means that the paper has limitations, but those not discussed in the paper.
The authors are encouraged to create separate "Limitations" section in their paper.
The paper should point out any strong assumptions and how robust the results are to violations of these assumptions (e.g., independence assumptions, noiseless settings, model well-specification. asymptotic approximations only holding locally). The authors should reflect how these assumptions might be violated in practice and what the implications would be.
The authors should reflect on the scope of the claims made, e.g., if the approach was only tested on few datasets or with few runs. In general, empirical results often depend on implicit assumptions, which should be articulated.
The authors should reflect the factors that influence the performance of the approach. For example, facial recognition algorithm may perform poorly when image resolution low images are taken in low lighting. Or speech-to-text system might be used reliably to provide closed captions for online lectures because it fails to handle technical jargon.
The authors should discuss the computational efficiency of the proposed algorithms and how they scale with dataset size.
If applicable, the authors should discuss possible limitations of their approach address problems of privacy and faimess.
While the authors might fear that complete honesty about limitations might be used by reviewers as grounds for rejection, a worse outcome might be that reviewers discover limitations that aren't 't acknowledged in the paper. The authors should use their best judgment and recognize that individual actions in favor of transparency play an impor- tant role in developing norms that preserve the integrity of the community. Reviewers will be specifically instructed to not penalize honesty concerning limitations.
3. Theory assumptions and proofs
Question:
For each theoretical result, does the paper provide the full set of assumptions and complete (and correct) proof?
Answer: [Yes]
14

---

Justification:
Our derivation for can be found in our method section.
Guidelines:
The answer NA means that the paper does not include theoretical results.
All the theorems, formulas, and proofs in the paper should be numbered and cross- referenced.
All assumptions should be clearly stated referenced in the statement of any theorems.
The proofs can either appear in the main paper or the supplemental material, but they appear in the supplemental material, the authors are encouraged to provide short proof sketch provide intuition.
Inversely, any informal proof provided in the core of the paper should be complemented by formal proofs provided in appendix or supplemental material.
Theorems and Lemmas that the proof relies upon should be properly referenced.
4. Experimental result reproducibility
Question: Does the paper fully disclose all the information needed to reproduce the main ex- perimental results of the paper to the extent that affects the main claims and/or conclusions of the paper (regardless whether the code and data are provided or not)?
Answer: [Yes]
Justification: We'll release our full code the public.
Guidelines:
The answer NA means that the paper does not include experiments.
If the paper includes experiments, a No answer to this question will not be perceived well by the reviewers: Making the paper reproducible is important, regardless of whether the code and data are provided or not.
If the contribution is dataset and/or model, the authors should describe the steps taken to make their results reproducible or verifiable.
Depending on the contribution, reproducibility can be accomplished in various ways. For example, if the contribution is a novel architecture, describing the architecture fully might suffice, or if the contribution is a specific model and empirical evaluation, it may be necessary to either make possible for others to replicate the model with the same dataset, or provide access to the model. In general. releasing code and data is often one good way accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g.. in the case of large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed.
While NeurIPS does not require releasing code, the conference does require all submis- sions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example
(a) If the contribution is primarily a new algorithm, the paper should make clear how to reproduce that algorithm. (b) the contribution is primarily a new model architecture, the paper should describe the architccture clearly and fully.
(c) If the contribution is new model (e.g., a large language model), then there should either way to access this model for reproducing the results or way to reproduce the model (e.g., with an open-source dataset instructions for how to construct the dataset).
(d) We recognize that reproducibility may be tricky in some cases, in which case authors are welcome to describe the particular way they provide for reprodueibility. the case of closed-source models, may be that access to the model is limited in some way (e.g., to registered users), but it should be possible for other researchers have some path to reproducing or verifying the results.
5. Open access to data and code
Question: Does the paper provide open access to the data and code, with sufficient instruc- tions to faithfully reproduce the main experimental results, as described in supplemental material?
15

---

Answer: [Yes]
Justification:
We'll 'lll release our full code to the public.
Guidelines:
The answer NA means that paper does not include experiments requiring code.
Please see the NeurIPS code and data submission guidelines (https://nips.cm
iy for more details.
While we encourage the release of code and data, we understand that this might not be possible, so "No" an acceptable answer. Papers cannot be rejected simply for not including code, unless this is central to the contribution (e.g., for new open-source benchmark).
The instructions should contain the exact command and environment needed to run to reproduce the results. See the NeurIPS code and data submission guidelines (https
/ ccpppi.ccppulli//uuuiu wwwiii/gguie///eeeeeeeeic CoodeSubeiiie ssi wsionnolli onnolicy)))) for more details.
The authors should provide instructions on data access and preparation, including how access the raw data, preprocessed data, intermediate data, and generated data, etc.
The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only subset of experiments are reproducible, they should state which ones are omitted from the script and why.
At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable).
Providing as much information possible in supplemental material (appended to the paper) recommended, but including URLs to data and code permitted.
6. Experimental setting/details
Question: Does the paper specify all the training and test details (e.g., data splits, hyper- parameters, how they were chosen, type of optimizer, etc.) necessary to understand the results?
Answer: [Yes]
Justification:
We specify all the training and test details in the experiment section.
Guidelines:
The answer NA means that the paper does not include experiments.
The experimental setting should be presented in the core of the paper to level of detail that is necessary to appreciate the results and make sense of them.
The full details can be provided either with the code, in appendix, or as supplemental material.
7. Experiment statistical significance
Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments?
Answer: [Yes]
Justification:
We ran our experiments for three times and reports the average scores.
Guidelines:
The answer NA means that the paper does not include experiments.
The authors should answer "Yes" if the results are accompanied by error bars, confi- dence intervals, statistical significance tests, at least for the experiments that support main claims of the paper.
The factors of variability that the error bars are capturing should be clearly stated (for example, train/test split, initialization, random drawing of some parameter, overall run with given experimental conditions).
The method for calculating the error bars should be explained (closed form formula, call a library function, bootstrap, etc.)
The assumptions made should be given (e.g.. Normally distributed errors).
should be clear whether the error bar is the standard deviation the standard error of the mean.
16

---

It is OK to report 1-sigma error bars, but one should state it. The authors should preferably report a 2-sigma error bar than state that they have 96% CI, if the hypothesis of Normality of enrors is not verified.
For asymmetric distributions, the authors should be careful not to show in tables or figures symmetric error bars that would yield results that are out range (e.g. negative error rates).
If error bars are reported in tables or plots, The authors should explain in the text how they were calculated and reference the corresponding figures or tables in the text.
8. Experiments compute resources
For each experiment, does the paper provide sufficient information on the com- puter resources (type of compute workers, memory, time of execution) needed to reproduce the experiments?
Question:
Answer: [Yes]
Justification:
We report the computer resources in our experiment section.
Guidelines:
The answer NA means that the paper does not include experiments.
The paper should indicate the type of compute workers CPU or GPU, internal cluster, or cloud provider, including relevant memory and storage.
The paper should provide the amount of compute required for each of the individual experimental runs well estimate the total compute.
The paper should disclose whether the full research project required more compute than the experiments reported in the paper (e.g.. preliminary or failed experiments that didn't make into the paper).
9. Code of ethics
Question: Does the research conducted in the paper conform, in every respect, with the NeurIPS Code of Ethics https: http:::////uis./eeeeeeeee httppppcccppppp..t.tcccccccccaea
Answer: [Yes]
Justification: Our research conform with the NeurlPS Code of Ethics.
Guidelines:
The answer NA means that the authors have not reviewed the NeurIPS Code of Ethics.
If the authors answer No, they should explain the special cireumstances that require deviation from the Code of Ethics.
The authors should make sure to preserve anonymity (e.g., if there is a special consid- eration due to laws or regulations their jurisdiction).
10. Broader impacts
Question: Does the paper discuss both potential positive societal impacts and negative societal impacts of the work performed?
Answer: [Yes]
Justification: The potential societal impacts discussed in conclusion section.
Guidelines:
The answer NA means that there is no societal impact of the work performed.
If the authors answer NA or No. they should explain why their work has no societal impact or why the paper does not address societal impact.
Examples of negative societal impacts include potential malicious or unintended uses (e.g., disinformation, generating fake profiles, surveillance), fairness considerations (e.g., deployment of technologies that could make decisions that unfairly impact specific groups), privacy considerations, and security considerations.
The conference expects that many papers will be foundational research and not tied to particular applications, let alone deployments. However, if there is direct path to any negative applications, the authors should point it out. For example, it is legitimate to point out that improvement in the quality of generative models could be used to
17

---

generate deepfakes for disinformation. On the other hand, it is not needed to point out that a generic algorithm for optimizing neural networks could enable people train models that generate Deepfakes faster.
The authors should consider possible harms that could arise when the technology is being used as intended and functioning correctly, harms that could arise when the technology is being used as intended but gives incorrect results, and harms following from (intentional or unintentional) misuse of the technology.
there are negative socictal impacts, the authors could also discuss possible mitigation strategies (e.g., gated release of models, providing defenses in addition attacks, mechanisms for monitoring misuse, mechanisms to monitor how a system learns from feedback over time, improving the efficiency and accessibility of ML).
11. Safeguards
Does the paper describe safeguards that have been put in place for responsible release of data models that have high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)?
Question:
Answer: [NA]
Justification: [NA]
Guidelines:
The answer NA means that the paper poses no such risks.
Released models that have high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters.
Datasets that have been scraped from the Internet could pose safety risks. The authors should describe how they avoided releasing unsafe images.
We recognize that providing effective safeguards challenging, and many papers do require this, but we encourage authors to take this into account and make best faith effort.
12. Licenses for existing assets
Are the creators original owners of assets (e.g., code, data, models), used in the paper, properly credited and are the license and terms of use explicitly mentioned and properly respected?
Question:
Answer: [Yes]
Justification: All our used assets are open-soureed.
Guidelines:
The answer NA means that the paper does not use existing assets.
The authors should cite the original paper that produced the code package dataset.
The authors should state which version of the asset is used and, if possible, include URL.
The name of the license (e.g., CC-BY 4.0) should be included for each asset.
For scraped data from a particular source (c.g., website), the copyright and terms of service of that source should be provided.
If assets are released, the license, copyright information, and terms of use in the package should be provided. For popular datasets, wwppppswpptseeee whworee.wthhhe....c. com/ datasets has curated licenses for some datasets. Their licensing guide can help determine the license of dataset.
For existing datasets that are re-packaged, both the original license and the license of the derived asset (if has changed) should be provided.
If this information is not available online, the authors are encouraged to reach out to the asset's is creators.
13. New assets
Question: Are new assets introduced in the paper well documented and the documentation provided alongside the assets?
18

---

Answer: [Yes]
Justification:
We submit our code together with our supplementary results.
Guidelines:
The answer NA means that the paper does not release new assets.
Researchers should communicate the details the dataset/code/model as part of their submissions via structured templates. This includes details about training, license, limitations, etc.
The paper should discuss whether and how consent was obtained from people whose asset used.
At submission time, remember to anonymize your assets (if applicable). You can either create anonymized URL or include an anonymized zip file.
14. Crowdsourcing and research with human subjects
Question: For crowdsourcing experiments and research with human subjects, does the paper include the full text of instructions given to participants and screenshots, if applicable, as well details about compensation (if any)?
Answer: [NA]
Justification:
The paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Including this information the supplemental material is fine, but if the main contribu- tion of the paper involves human subjects, then as much detail as possible should be included the main paper.
According to the NeurIPS Code of Ethics, workers involved in data collection, curation, other labor should be paid at least the minimum wage in the country of the data collector.
Institutional review board (IRB) approvals or equivalent for research with human subjects
Question: Does the paper describe potential risks incurred by study participants, whether such risks were disclosed to the subjects, and whether Institutional Review Board (IRB) approvals (or equivalent approval/review based on the requirements of your country or institution) were obtained?
Answer: [NA]
Justification: The paper does not involve crowdsourcing nor research with human subjects.
Guidelines:
The answer NA means that the paper does not involve crowdsourcing nor research with human subjects.
Depending on the country in which research is conducted, IRB approval (or equivalent)
may be required for any human subjects research. If you obtained IRB approval, you should clearly state this in the paper.
We recognize that the procedures for this may vary significantly between institutions and locations, and we expect authors to adhere to the NeurIPS Code of Ethics and the guidelines for their institution.
For initial submissions, do not include any information that would break anonymity (if applicable), such as the institution conducting the review.
16. Declaration of LLM usage
Question: Does the paper describe the usage of LLMs if it is an important, original, or non-standard component of the core methods in this research? Note that if the LLM is used only for writing, editing, or formatting purposes and does not impact the core methodology, scientific rigorousness, originality of the research, declaration is not required.
Answer: [NA]
19

---

Justification:
The core method development in this research does not involve LLMs.
Guidelines:
The answer NA means that the core method development in this research does not involve LLMs as any important, original, or non-standard components.
Please refer to LLM policy hettap///eaarriio.cccc//ooorreep for what should or should not be described.
20

---

A Technical Appendices and Supplementary Material
A.1 Scene Contraction
We employ
contraction technique similar to that in MipNeRF360 3 to constrain the input
coordinates of the multi-resolution hash to the range (0, 1). First, we normalize the coordinates using the axis-aligned bounding box (AABB) Bg of the scene. For NeRF synthetic dataset, we the minimum and maximum and the AABB to be -1.3 and 1.3. For standard dataset, we define the AABB using the minimum and maximum camera positions. For large-scale datasets, points between the 1st and 99th percentiles of the initial point clouds to establish the AABB. The normalized point p' is derived follows:
the
P
p =
(13)
Bo
Next, we contract the normalized points to the range (0, 1) using the following formula:
0.25 p' +1
if ||p'|| S
contact(p') = 0.25 (2
(14)
PT +1 otherwise,
where contact() the scene contraction function. Note that we contract the points (0, 1) instead of (-2, 2) to meet the input requirements for the multi-resolution hash (28
A.2 Derivation of Ray-Sphere Intersection
In this section, we provide the detailed derivation the ray-sphere intersection, which is used the hybrid rendering module compute background points. Given ray r(t) td and sphere centered at the origin with radius r, we substitute the ray equation into the sphere equation:
(o - -d) (o + td) = r2,
(15)
which expands to:
0 20(o d) + (2 d) =
(16)
Let A - d, B 2(000000000 d), and C 0-0 ,2_ The equation then simplifies quadratic in t:
At + Bt CC
(17)
The solutions to this quadratic equation are given by:
B 4AC
(18)
2A
Since the ray originates inside the sphere, the equation always yields two real solutions. We select the positive solution, corresponds to the intersection point in the forward direction of the ray.
A.3 Ablation for View-dependent Appearance Modeling
We provide an additional ablation study that compares two approaches (SH Coefficients for "high rank per Gaussian spherical harmonics parameters" and Hybrid for "MLP and integration of neural field and explicit Gaussian") for view-dependent appearance modeling, as shown in Table. 7 Our hybrid approach not only achieves significant reduction in model size, but also achieves slightly better visual quality compared with using SH coefficients. This comparison demonstrates that our hybrid approach provides compact and more powerful way in modeling view-dependent appearance.
Table 7: Ablation study of SH and MLP based appearance modeling.
PNSR
SSIM
LPIPS
Size (MB)
SH Coefficients
30.12
0.908
0.243
267
Hybrid (Ours)
30.37
0.910
0.241
34
21

---

A.4 Evaluation in Street Scenes
To evaluate HyRF's performance in street scenes, we conducted experiments on the KITTI [T]] dataset (2011 1 09 26 drive _0e00000222000 sequence), as shown in Table. [0] Our method achieves similar visual quality compared with 3DGS while being over 10 times smaller in model size. After adding the background rendering technique, our complete method shows consistent quality improvements, particularly for distant objects and sky regions.
Table 8: Evaluation in street scenes on the KITTI 10 dataset.
PNSR
SSIM
I.PPS
Size (MR)
3DGS
19.37
0.665
0.272
472
HyRF (wlo background)
19.42
0,660
0.273
36.7
HyRF (Full)
19.56
0.667
0.273
36.4
A.5 Number of Explicit Gaussians
The significant memory savings of HyRF come from both decreased per-Gaussian storage and reduced number of Gaussians. stated in the paper, HyRF only stores parameters per- per-Gausiian, in contrast to 59 parameters as in 3DGS. Moreover, HyRF naturally converges to fewer Gaussians while maintaining quality. shown in Table. [1] HyRF achieves 24-45% reduction in the number of explicit Gaussians compared to 3DGS on three dataset (MipNeRF360, Vanks&Temple and DeepBlending), without additional pruning techniques. We hypothesize this reduction of number of Gaussians stems from two key factors: (1) Faster convergence during training, reducing the need for aggressive densification, and (2) The neural field's ability to represent view-dependent effects without requiring excessive Gaussians.
Table Comparison of number of explicit Gaussians.
MipNeR1330
Tanks&Temples
DeepBlending
3DGS
3.31M
1.84M 1.01M
2.81M
HyRP
2.52M
1.74M
A.6 Additional Comparison with Recent 3DGS-based Methods
We conduct additional comparison experiments with several recent 3DGS- baaeedd-dedddeeeee methods, namely GOF [53), Spec-GS [51), Mini-Splatting 7 and DashGaussian [5] on the DeepBlending [13] dataset. To provide more comprehensive evaluation, we have expanded the comparison table to include rendering speed (FPS), training time (Time), peak GPU memory usage (Memory), and model storage size (Size) across state-of-the-art methods.
A.7 Additional Comparison Specular Scenes
we have conducted additional quantitative comparisons using the anisotropic synthetic dataset from Spec-GS [51), which features 8 object-centered scenes with strong specular highlights. Compared with 3DGS, HyRF achieves significantly better rendering quality (11.58 dB PSNR) while using 82% less memory. The improved performance highlights the benefits of using MLPs over SH coefficients for modeling high-frequency view dependennd effects.
Table 10: Comparison with recent 3DGS-based methods.
PSNR
SSIM
L.IPS
FPS
Time (min)
Memory (GB)
Size (MB)
3DGS
29.41
0.903
0.243
112
14.4
5.54
676
GOF
30.42
0.914
0.237
96
20.3
6.62
721
Spec-GS
30.57
0.912
0.234
107
17.8
5.79
765
MiniSplatting2
30.08
0.912
0.240
136
2.75
3.65
155
DashGaussian
30.02
0.907
0.248
132
2.62
4.32
465
HyRF
30.37
0.910
0.241
114
12.5
1.83
34
22

---

Table 11: Comparison the Spec-Gs, Spec-GS dataset.
PSNR
SSIM
LPIPS
Size (MB)
3DGS
33.83
0,966
0.062
47
HyRF (Ours)
35.41
0.970
0.053
8.2
A.8
Additional Qualitative Comparisons
In Fig. 9 show the Additional qualitative comparisons of our method against previous approaches on standard real-world datasets.
Ground Truth
Instant-NGP
3D-55
Scaffold-GS
Ours
Figure 9; Additional qualitative comparisons of our method against previous approaches on standard real-world datasets.
A.9 Per-scene Metries
Table.
12
15
present per-scene metrics for MipNeRF360 B3. Tanks & Temples [6] and Deep
Blending 13
datasets. Table. [6 and [7 provide per-scene metrics for the per-scene metrics
for NeRF Synthetic dataset [26] Finally, Table. [8 lists per-scene metrics for Mill19 44 and
Urbanscene3D 200 datasets.
23

---

Table 12: PSNR scores for scenes in Mip-NcRF360 [3] Tanks & Temples 16 and Deep Blend-
ing 13 datasets.
Mig-ecFFlet
Tanbs&Teemple
Deep Blending
Beyyle
ganden
stump
cuunter
truck
Inin
dejolunar
playromm
NeR1
21-2
Scuflol--SS
24.50
25.45
21.56
27.54
22,89
31.98
29.4
32.01
27.78
25,92
22.12
34-02
29.72
30,37
Table 13: SSIM scores for scenes Mip-NeRF360 3 Tanks Temples 16 and Deep Blending 1K
datasets.
NeRt-10 mnd wechill
Blending
11
bicycle
Bowes
parien
504432r
kitchen
brnsi
t003
trsic
plays001:
19222
Pleacnels
0.866
0.431
0.543
0.523
0.518
0.842
0.249
0.648
0.814
01026
0.774
0.63
0.11
0.757
0,812
01735
83
0.317
0.338
0.655
0.745
0.631
0.901
10GN [5
0:771
0.905
0.775
04588
03055
0132
01879
0.800
0141
0.900
01913
Sc
0.305
0.607
0.8-2
0.784
0.530
0.925
0.914
0.928
0.946
0.905
0.883
0.822
0.53
0.911
0.904
0.905
Ours
0.762
8.11
0.854
0.755
0.601
0.930
0.915
0.277
0.950
0.866
0.83
0.886
0344
0,94
8,916
0,91
Table 14: LPIPS scores for scenes in Mip-NeRF360 B Tanks & Temples 16 and Deep Blend- ing 13 datasets.
detaeet
Mip-NokF'oi X
Tanksd/Temples
Derp Bllnding
hicycle
gandm
stump
Tnochill
room
crutter
kinchmn
muzn
tracc
dijhhnenn
playroom
muzn
Plenooels
0.505
M-N:RF
0.339
300S
0.305
0.317
0.220
Scaffold
0.305
0.264
0.346
0.202
0.191
0.125
0.250
0.54
Our
0:337
0.301
0.144
0.236
0.328
0.189
6.179
0.124
0.167
0.211
0,140
0.312
0.176
0.242
0.239
0.241
Table 15: Model size (MB) for scenes in Mip-NeRF360 Blending 13 datasets.
S Tanks & Temples 16 and Deep
NeRI7300 [
Tanks&TTemples
Deep Blending
[+]
bicycle
flowers
panden
treehill
kitchen
bunaii
drjohnoon playroom
3DGS
1291
1045
1268
1034
872
327
261
414
281
634
57%
240
411
715
515
676
GS
33
248
271
493
209
191
173
258
87
69
66
Ours
68
54
51
61
39
38
38
49
39
48
36
34
Table 16: PSNR scores for scenes in Synthetic NeRF dataset [26].
Table 17: Model size for scenes in Synthetic NeRF dataset (26].
Mic
Char
Skyp
Macrab
Logo
Draso
Faxs
Honin
Mic
Chur:
Skip
Mauriaa
Lago
Doune
Ticts
muss
1003 [3]
21:26
2533
2330
30a
35.18
36.8
57.32
13:12
17.55
15.68
1117
15.0
95.11
10.33
13.08
13
16
18
13
15
11
8
Ouea
$5.91
35.11
11.6
3015
36.8
36.4
35,00
38.19
3832
Table 18: Per-scene metrics on Mill19 44 dataset.
Sunte
PSNR
SSIM!
LPIPS)
Sizel
PSNR!
SSUM"
LPIPS
Size
PSNR
SSIM!
LPIPS
Size
PSNR!
SSIM'
LPPPSi
Stzek
300S
24:21
0.595
0.35)
1566
20.6
0,67)
0,540
1424
21.84
0.901
0.279
500
0.728
0.295
1273
22
0.56.
0.342
19.07
0.3167
0.763
0.186
903
0,685
0.303
567


Reviews:
[{"review_id": "V9nLYDmaQC", "reviewer": "Reviewer_7B5r", "summary": "The paper combine explicit 3D Gaussians and neural fields for efficient neural rendering. The decomposition of geometry and appearance field helps to compress the parameters needed for view-dependent effects, compared to per Gaussian spherical harmonics parameters. The proposed method reduces the computational overhead by decomposing the foreground and background. It achieves 20X compression rate compared to original 3DGS and maintain real-time performance.", "questions": "1. The paper proposes to represent the anisotropic information with MLP and integration of neural field and explicit Gaussian, instead of high rank per Gaussian spherical harmonics parameters. However, ablation is missing: The memory footprint comparison between \"MLP and integration of neural field and explicit Gaussian\" and \"high rank per Gaussian spherical harmonics parameters\" as in original 3DGS.\n2. Environment decomposition is common in street scenes, would be great to evaluate the proposed method in street scenes.", "limitations": "See weakness above.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 2, "originality": 2, "strengths_and_weaknesses": "Strength:\n\n1. The results in supplementary shows a good novel-view synthesis comparison between baseline.\n\n2. The decomposition of Geometry and Radiance/Appearance-related field is effective and is qualitatively verified in Figure 5.\n\n3. The application of representing the view-dependent information with MLP instead of spherical harmonics for compression seems interesting to me.\n\nWeakness:\n\n1. The core contribution in line 68-70. The concept of geometry and rich appearance decomposition shares a similar idea with [1]. Would be great to have discussions.\n2. The core contribution in line 70-71 is not entirely new in this domain, especially in GS for autonomous driving, for example [2,3,4] uses sky environment decomposition in their scene modeling. This might reduce the novelty from some extents.\n3. The ablation of explicit gaussian in Line 259 is little confusing. It seems that the explicit  gaussian is essential to provide \"position, explicit scale, explicit opacity, explicit color\" in Figure2. How to inference the decomposed field if there is no explicit gaussian to provide \"position\"? \n4. Related work on 3DGS compression is not compared, for example [5].\n\n[1] https://arxiv.org/pdf/2003.09852\n[2] https://arxiv.org/pdf/2311.18561\n[3] https://arxiv.org/pdf/2401.01339\n[4] https://arxiv.org/pdf/2405.18416\n[5] https://arxiv.org/abs/2403.14530", "was_revised": true, "final_justification": "Thanks for addressing most of my concerns. I will change to positive rating.", "rebuttal": "> The core contribution in line 68-70. The concept of geometry and rich appearance decomposition shares a similar idea with \\[1\\]. Would be great to have discussions.\n\nWe would like to clarify the differences between \\[1\\] and our work in both motivation and model designs for the geometry-appearance decomposition: \n\n1. **Different motivations.** \\[1\\] decouples geometry and appearance primarily for *efficiency*, as their geometry network is queried repeatedly using sphere tracing to get surface positions, before evaluating BRDF using the appearance network. In contrast, our decomposition aims to improve accuracy, as we empirically observed that a single network struggles to jointly learn Gaussian geometry and appearance attributes, likely due to their weak correlation (see L123-126 in our paper).  \n2. **Different model designs.** \\[1\\] models a signed distance function (SDF) for continuous surface modeling in the geometry network and a BRDF representation in the appearance network. In contrast, our method models per-Gaussian properties: opacity, scale, and rotation via the geometry network, and per-Gaussian colors via the appearance network.\n\n---\n\n> The core contribution in line 70-71 is not entirely new in this domain, especially in GS for autonomous driving, for example \\[2,3,4\\] uses sky environment decomposition in their scene modeling. This might reduce the novelty from some extents.\n\nWe appreciate the reviewer bringing up these relevant comparisons. Our background decomposition technique differs from prior sky modeling approaches in three key aspects:\n\n1. **Memory Efficiency.** Unlike \\[2,3\\] that require storing cube maps (adding memory overhead), our appearance field predicts backgrounds without storage costs.  \n2. **No resolution limit.** While \\[2,3\\] are limited by cube map resolution, our neural field provides continuous background prediction.  \n3. **Improved Formulation.** In contrast to \\[4\\] which treats the background as points at infinity, our method instead constructs a background sphere and computes ray-sphere intersections as background points, which enhances rendering performance.\n\nThe attached table shows a comparison of the three methods on Tanks & Temples dataset (*train* scene), where *Cube Maps* denotes the cube map approach in \\[2, 3\\], *Infinite Points* denotes the points-at-infinity approach in \\[4\\] and *Ours* denotes the background rendering method in our work. Our method achieves similar performance with the cube map approach \\[2,3\\] without introducing additional storage and offers higher visual quality compared with the points-at-infinity approach \\[4\\].\n\n|  | PSNR | SSIM | LPIPS | Size (MB) |\n| :---- | :----: | :----: | :----: | :----: |\n| Cube Maps | 22.01 | 0.803 | 0.213 | 48 |\n| Infinite Points | 21.87 | 0.792 | 0.221 | 37 |\n| Ours | **22.12** | **0.806** | **0.212** | 37 |\n\n---\n\n> The ablation of explicit gaussian in Line 259 is little confusing. It seems that the explicit gaussian is essential to provide \"position, explicit scale, explicit opacity, explicit color\" in Figure2. How to inference the decomposed field if there is no explicit gaussian to provide \"position\"?\n\nTo clarify, in our ablation experiment (Line 259), we maintain the explicit Gaussian positions as they are indeed necessary for spatial querying of the neural fields, while removing all other explicit properties (scale, opacity, and color) to evaluate the effectiveness of learning these properties through the neural fields only.\n\nWe acknowledge this wasn't sufficiently clear in the original text and will revise Line 259 to explicitly state: \"In Table 5, we evaluate the impact of removing all explicit Gaussian properties except positions, which are retained as they are required for neural field queries.\" We hope this revision can improve the manuscript's clarity.\n\n---\n\n> Related work on 3DGS compression is not compared, for example \\[5\\].\n\nWe thank the reviewer for pointing this out. We have actually included comparisons with \\[5\\] in Table 4 (denoted as Chen et al.) of our manuscript. We will make this comparison and discussion clearer in our revision.\n\n---\n\n> The paper proposes to represent the anisotropic information with MLP and integration of neural field and explicit Gaussian, instead of high rank per Gaussian spherical harmonics parameters. However, ablation is missing: The memory footprint comparison between \"MLP and integration of neural field and explicit Gaussian\" and \"high rank per Gaussian spherical harmonics parameters\" as in original 3DGS.\n\nWe thank the reviewer for this important suggestion. We provide an ablation study that compares two approaches (*SH Coefficients* for \"high rank per Gaussian spherical harmonics parameters\" and *Hybrid* for \"MLP and integration of neural field and explicit Gaussian\") for view-dependent appearance modeling:\n\n|  | PSNR | SSIM | LPIPS | Size (MB) |\n| :---- | :----: | :----: | :----: | :----: |\n| SH Coefficients | 30.12 | 0.908 | 0.243 | 267 |\n| Hybrid (Ours) | **30.37** | **0.910** | **0.241** | **34** |\n\nOur hybrid approach not only achieves significant reduction in model size, but also achieves slightly better visual quality compared with using SH coefficients. This comparison demonstrates that our hybrid approach provides a compact and more powerful way in modeling view-dependent appearance.\n\n---\n\n> Environment decomposition is common in street scenes, would be great to evaluate the proposed method in street scenes.\n\nTo evaluate HyRF's performance in street scenes, we conducted experiments on the KITTI \\[6\\] dataset (2011\\_09\\_26\\_drive\\_0002 sequence), with the following results:\n\n|  | PSNR | SSIM | LPIPS | Size (MB) |\n| :---- | :----: | :----: | :----: | :----: |\n| 3DGS | 19.37 | 0.665 | **0.272** | 472 |\n| HyRF (w/o background) | 19.42 | 0.660 | 0.273 | 36.7 |\n| HyRF (full) | **19.56** | **0.667** | 0.273 | **36.4** |\n\nOur method achieves similar visual quality compared with 3DGS while being over 10 times smaller in model size. After adding the background rendering technique, our complete method shows consistent quality improvements, particularly for distant objects and sky regions. This quantitative comparison, together with additional qualitative comparisons on the street scenes, will be added to our revised version.\n\n---\n\n\\[1\\] Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance. NeurIPS 2020\\.  \n\\[2\\] Periodic Vibration Gaussian: Dynamic Urban Scene Reconstruction and Real-time Rendering. ArXiv.  \n\\[3\\] Street Gaussians: Modeling Dynamic Urban Scenes with Gaussian Splatting. ECCV 2024\\.  \n\\[4\\] 3D StreetUnveiler with Semantic-aware 2DGS \\-- a simple baseline. ICLR 2025\\.  \n\\[5\\] HAC: Hash-grid Assisted Context for 3D Gaussian Splatting Compression. ECCV 2024\\.  \n\\[6\\] Vision meets Robotics: The KITTI Dataset. IJRR 2013\\."}, {"review_id": "fXU9667R1k", "reviewer": "Reviewer_Lw1w", "summary": "This paper introduces a hybrid scene representation that combines neural fields and explicit Gaussians to achieve memory-efficient and high-quality novel view synthesis. Specifically, each Gaussian point contains only a few explicit properties (8 dimensions): 3D position, 3D diffuse color, isotropic scale, and opacity. Neural fields are applied to query the neural properties for each point, such as neural rotation, neural anisotropic scale, neural opacity, and neural color. Finally, the explicit properties and neural properties are added and activated to obtain the final properties of the Gaussian points. This approach significantly reduces the number of parameters for each Gaussian, thereby lowering memory requirements and achieving a memory-efficient scene representation.\n\nAlthough the introduction of neural fields' query operations increases computation, the method incorporates visibility pre-culling to accelerate rendering. It also leverages a common technique, background rendering, used in NeRF-based unbounded scene novel view synthesis, to enhance rendering quality in distant areas. \n\nExperiments demonstrate that this method pushes the boundaries of rendering quality and memory efficiency while maintaining high rendering speed. Moreover, if this representation is further compressed, it achieves better rate-distortion performance compared to representative works in 3DGS compression.", "questions": "1. **Activation function on scale**: It is not common to use a sigmoid function for the scale value in 3DGS. Could the authors explain why they choose a sigmoid function instead of the original exponential function used in 3DGS?\n    \n2. **Report of the number of explicit Gaussians**: The reviewer believes that when discussing memory-efficient scene representation, it is important not only to report the final memory size but also to include the number of explicit Gaussians. This would allow readers to understand whether the proposed method reduces memory usage primarily by decreasing the data per Gaussian or also by reducing the number of points used for scene representation.\n\nIf the authors provide clear explanations for my questions and promise to revise areas of the paper where the writing is not polished, I will consider upgrading my evaluation from borderline reject to borderline accept.", "limitations": "1. **Inefficiency on web platforms or low-end consumer GPUs**: The authors have made efforts in methodology and engineering to enable HyRF to achieve FPS comparable to vanilla 3DGS on an Nvidia 3090 GPU. However, since HyRF relies on Neural Fields during rendering, and Neural Fields tend to have low execution efficiency on web platforms or low-end GPUs, it is likely that HyRF's rendering efficiency on such devices will be significantly lower than that of vanilla 3DGS. This is a potential issue but does not detract from the fact that this is a well-rounded and meaningful research paper. This point is simply raised to highlight potential challenges in practical applications.", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "## Strengths\n1. **Well-rounded solution and strong performance**: The proposed solution is complete, covering areas from representation design to rendering acceleration, background rendering quality improvement, and even model compression. It achieves a well-balanced result in rendering quality, memory efficiency, and rendering speed. Compared to the two important baselines, 3DGS and Scaffold-GS, it also demonstrates advantages in training convergence speed and model size.\n    \n2. **Interesting representation design**: Although the reviewer considers the concept of querying attributes of Gaussian in neural fields to be somewhat trivial, this work still introduces some interesting and reasonable design ideas. For instance, the use of decoupled neural fields—geometry and appearance neural fields—to extract different attributes. Additionally, each Gaussian is initialized with an isotropic scale and then refined by the geometry neural field to describe the specific shape of the Gaussian.\n\n## Weakness\n1. **Limited novelty or technical contribution**: While the overall solution is very comprehensive, most of the techniques, aside from Decoupled Neural Fields and Aggregation with Explicit Gaussians, are relatively common or lack significant innovation. As a result, the impression on the reviewer is that this is a complete but somewhat lacking in novelty work. This might be a subjective bias of the reviewer and could be further discussed with the AC and other reviewers.\n2. **Limitations in formula expression.**：The reviewer places great emphasis on the accuracy of formulas. However, this paper repeatedly contains typos or parts that are not easy to follow. For example, the \"isotropic scale\" on Line 120 is evidently a scalar and should not be in bold. Similarly, in Eqn (2), the symbol p′p'p′ has not appeared earlier and is not explained in the surrounding text. In Eqn (3), it is clear that the outputs fradf_{\\text{rad}}frad​ and fgeof_{\\text{geo}}fgeo​ should be vectors. The introduction of Eqn (10) is overly casual; although the reviewer can infer the intended meaning, such writing issues should not appear in a NeurIPS paper.", "was_revised": true, "final_justification": "I appreciate the authors' thorough and careful rebuttal, which has addressed most of my concerns. Regarding the novelty issue, since more than one reviewer has recognized this paper's contributions, I've decided not to be overly strict on this point. However, I still want to highlight a limitation: since this work involves neural fields as a key component for scene representation, it cannot achieve efficient real-time rendering on web platforms or low-end consumer GPUs. I hope the authors will clearly state this limitation in the final version.\n\nFinally, I've decided to raise my rating from borderline reject to borderline accept.", "rebuttal": "> **Limited novelty or technical contribution**: While the overall solution is very comprehensive, most of the techniques, aside from Decoupled Neural Fields and Aggregation with Explicit Gaussians, are relatively common or lack significant innovation. As a result, the impression on the reviewer is that this is a complete but somewhat lacking in novelty work. This might be a subjective bias of the reviewer and could be further discussed with the AC and other reviewers.\n\nWe appreciate the reviewer's thoughtful assessment and would like to highlight three key aspects of our contribution:\n\n1. **Novel framework.** Our core innovation lies in the novel decomposition framework that decomposes Gaussian properties between (1) compact per-Gaussian properties and (2) spatially-queryable neural fields, which conceptually enables significant memory reduction over prior art.  \n2. **Novel techniques.** As recognized by the reviewer, we develop several technical advances, including decoupled neural fields, visibility pre-culling, and background rendering. These techniques synergistically support this framework, leading to performance improvements in terms of quality and efficiency. \n3. **Strong performance.** Our comprehensive experiments demonstrate significant advantages over existing methods in memory efficiency while maintaining similar or superior rendering quality and rendering speed.\n\nAs noted by Reviewers HRRA and C7xm, our approach establishes a new paradigm for efficient neural rendering that achieves capabilities beyond prior art. We believe our novel combination and systematic framework represents a conceptual and practical advance in the field. \n\n---\n\n> **Limitations in formula expression.**：The reviewer places great emphasis on the accuracy of formulas. However, this paper repeatedly contains typos or parts that are not easy to follow. For example, the \"isotropic scale\" on Line 120 is evidently a scalar and should not be in bold. Similarly, in Eqn (2), the symbol p′p'p′ has not appeared earlier and is not explained in the surrounding text. In Eqn (3), it is clear that the outputs fradf\\_{\\\\text{rad}}frad and fgeof\\_{\\\\text{geo}}fgeo should be vectors. The introduction of Eqn (10) is overly casual; although the reviewer can infer the intended meaning, such writing issues should not appear in a NeurIPS paper.\n\nWe sincerely appreciate the reviewer's careful reading and valuable feedback regarding the mathematical presentation. We will make the following revision of our manuscript to improve clarity:\n\n1. Isotropic scale will be properly denoted as scalar $s$ (non-bold).  \n2. In Eqn (2),  $p'$ will be replaced with consistent notation $p_i$.  \n3. Vector outputs $f_{rad}$ and $f_{geo}$ will be bolded in Eqns (3) and (6).  \n4. Eqn (10) will be properly introduced with detailed explanation: “The final background color $C_{bg}$ combines the background point color $c_s$ with remaining visibility after accumulating the foreground Gaussians: $C_{bg}=\\prod_{i=1}^N (1-\\alpha_i) c_s, $ where $\\alpha_i$ represents the opacity of the i-th Gaussian along the ray.”  \n5. We will conduct a thorough review of the entire manuscript to correct all typographical errors, including but not limited to the instance noted in Line 242.\n\n---\n\n> **Activation function on scale**: It is not common to use a sigmoid function for the scale value in 3DGS. Could the authors explain why they choose a sigmoid function instead of the original exponential function used in 3DGS?\n\nWe chose the sigmoid activation for scale values primarily for **numerical stability**. While the exponential function used in standard 3DGS is unbounded above and can grow rapidly, the sigmoid provides bounded outputs that are more suitable for neural field optimization. This is particularly important in our framework where scale predictions come from learned neural fields rather than being directly optimized. We observed experimentally that the exponential activation can lead to training instability, while sigmoid activation maintained stable optimization. We also note this design choice aligns with Scaffold-GS, which similarly employs sigmoid activation for scales.\n\n---\n\n> **Report of the number of explicit Gaussians**: The reviewer believes that when discussing memory-efficient scene representation, it is important not only to report the final memory size but also to include the number of explicit Gaussians. This would allow readers to understand whether the proposed method reduces memory usage primarily by decreasing the data per Gaussian or also by reducing the number of points used for scene representation.\n\nWe thank the reviewer for this valuable suggestion. The significant memory savings of HyRF come from both decreased per-Gaussian storage and reduced number of Gaussians. As stated in the paper, HyRF only stores 8 parameters per-Gaussian, in contrast to 59 parameters as in 3DGS. Moreover, HyRF naturally converges to fewer Gaussians while maintaining quality.\n\nAs shown in the attached table, HyRF achieves a 24-45% reduction in the number of explicit Gaussians compared to 3DGS on three dataset (MipNeRF360, Tanks\\&Temples and DeepBlending), without additional pruning techniques. We hypothesize this reduction of number of Gaussians stems from two key factors:\n\n1. Faster convergence during training, reducing the need for aggressive densification.  \n2. The neural field's ability to represent view-dependent effects without requiring excessive Gaussians.\n\n| Dataset | MipNeRF360 | Tanks\\&Temples | DeepBlending |\n| :---- | :----: | :----: | :----: |\n| 3DGS | 3.31M | 1.84M | 2.81M |\n| HyRF | 2.52M | 1.01M | 1.74M |\n\nThese combined optimizations enable HyRF's significant memory savings. We will include this analysis in our revision to better demonstrate our method's advantages.\n\n---\n\n> **Inefficiency on web platforms or low-end consumer GPUs:** The authors have made efforts in methodology and engineering to enable HyRF to achieve FPS comparable to vanilla 3DGS on an Nvidia 3090 GPU. However, since HyRF relies on Neural Fields during rendering, and Neural Fields tend to have low execution efficiency on web platforms or low-end GPUs, it is likely that HyRF's rendering efficiency on such devices will be significantly lower than that of vanilla 3DGS. This is a potential issue but does not detract from the fact that this is a well-rounded and meaningful research paper. This point is simply raised to highlight potential challenges in practical applications.\n\nWe appreciate the reviewer's insightful observation regarding computational requirements. Indeed, the neural field components in HyRF currently benefit from high-end GPUs for high rendering speed. While recent advances like \\[1\\] have demonstrated efficient neural field rendering on mid-range GPUs (e.g., RTX 3060), achieving comparable efficiency on web platforms or integrated graphics remains an open challenge for the community. We hope future research in this direction can further bridge this efficiency gap, as it would significantly enhance the practical applicability of neural field based approaches.\n\n---\n\n\\[1\\] City-on-Web: Real-Time Neural Rendering of Large-Scale Scenes on the Web. ECCV2024."}, {"review_id": "LlYNeFJyqQ", "reviewer": "Reviewer_HRRA", "summary": "The paper introduces HyRF, a hybrid explicit–neural Gaussian-splatting scheme that stores minimal per-Gaussian properties explicitly, deferring richer encoding of properties to a neural field queried only at visible Gaussians. This design yields comparable rendering speeds to 3DGS, improves compression over Scaffold-GS, and simplifies the pipeline by avoiding anchor points or other compressed encodings of Gaussian positions.", "questions": "1. Why is Scaffold-GS the only compact or reduced memory Gaussian splatting method that is compared against in the majority of experiments? It may help to justify or explain this choice briefly in the “baselines” section.\n\n2. One of the claims of the paper is that only visible Gaussians need to query the neural field, which reduces the rasterization time. Does this benefit apply at all to object centered scenes where most Gaussians are within the view frustum?\n\n3. What limitations if any does storing the rotation entirely within the neural field impose? This seems to be distinct from all other Gaussian parameters which are entirely (e.g. position) or partially (e.g. scale, opacity, color) represented by an explicit Gaussian property.\n\nHaving a better understanding of the choice for baselines and the limitations of the method would increase my score for clarity.", "limitations": "The paper could benefit from more detailed and in-depth discussion of the limitations, which currently are limited to a single somewhat general sentence at the end of the paper. I'm generally positive about the method, but understanding the method’s shortcomings or limitations--even if minor--would be helpful for future researchers or practitioners who want to build on this method.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 4, "strengths_and_weaknesses": "strengths:\n1. HyRF outperforms Scaffold-GS in both model compression and render speed, while removing the need for anchor points or predicted Gaussian positions.\n2. Clear, well-motivated exposition with extensive ablations.\n3. Empirical results show higher PSNR than state of the art neural renderers with runtime similar to 3DGS and smaller model size than Scaffold-GS.\n4. Decomposing Gaussian properties into a lightweight component stored per-Gaussian and a more expressive component queried spatially is an original and elegant approach. The closest comparison in prior works are methods like ([14] Lee et al. 2024) which use separate representations but only for entirely separate properties (e.g. explicit Gaussians for geometric properties and neural field for appearance). \n\nweaknesses:\n1. The paper only briefly states that limitations mirror those of 3DGS; more details on when HyRF breaks down or what it struggles with would be useful.", "was_revised": true, "final_justification": "The paper presents a clear and well-motivated hybrid explicit–neural Gaussian splatting approach that achieves strong compression, competitive rendering speed, and improved PSNR over state of the art, while simplifying the representation by removing anchors and explicit Gaussian positions. The decomposition of Gaussian properties into lightweight explicit storage and neural-field-predicted components is elegant and original. My main questions--on baseline selection, benefits in object-centered scenes, and the implications of storing rotation in the neural field--were addressed in the rebuttal with convincing clarifications. With these points resolved, I maintain my positive recommendation for acceptance.", "rebuttal": "> The paper only briefly states that limitations mirror those of 3DGS; more details on when HyRF breaks down or what it struggles with would be useful.\n\nWe appreciate the reviewer's suggestion to elaborate on HyRF's limitations. Our method currently has several potential limitations that warrant discussion:\n\n1. Like 3DGS, our current formulation doesn't directly produce accurate surfaces. Future adaptations could incorporate surfel-based representations (e.g.  2DGS \\[1\\]) with our work.  \n2. The rendering pipeline currently does not incorporate anti-aliasing, which could be improved through similar techniques like MipSplatting \\[2\\].  \n3. We employ a consistent hash size for all standard datasets, which yields satisfactory results. However, the optimal hash size may vary depending on the per-scene scale and complexity. This may slightly increase the complexity of hyper-parameter tuning compared with the original 3DGS. \n\nWe will expand our limitations discussion in the revised manuscript to include these important considerations, along with their potential mitigation strategies.\n\n---\n\n> Why is Scaffold-GS the only compact or reduced memory Gaussian splatting method that is compared against in the majority of experiments? It may help to justify or explain this choice briefly in the “baselines” section.\n\nWe focus our primary comparisons (e.g. Table 1\\) on 3DGS and Scaffold-GS as they represent the two mainstream paradigms in Gaussian splatting: point-based (3DGS) and anchor-based (Scaffold-GS) approaches. These two paradigms encompass most current Gaussian splatting variants, making them ideal baselines for evaluating our novel hybrid representation. *We include additional comparisons with specialized compression methods in Table 4 to demonstrate our advantages over Gaussian compression approaches as well.*\n\nWe note that many compression techniques from these compression methods (e.g., quantization, pruning, or entropy coding) could potentially be combined with our method for further memory reduction. However, our current experiments focus on evaluating the core contributions of our hybrid neural field approach relative to these fundamental baselines. We will clarify this rationale in the \"baselines\" section as suggested.\n\n---\n\n> One of the claims of the paper is that only visible Gaussians need to query the neural field, which reduces the rasterization time. Does this benefit apply at all to object centered scenes where most Gaussians are within the view frustum?\n\nThe reviewer raises a valid point regarding object-centered scenes. Indeed, the performance benefit from visibility pre-culling is less pronounced for such cases (e.g., NeRF Synthetic dataset) where most Gaussians remain within the view frustum. However, for real-world 360° scenes, which represent our primary target scenario, visibility pre-culling provides substantial efficiency gains, as typically only 5-10% of Gaussians are visible in any given view. As evidenced by our ablation studies in Table 5, this optimization is crucial for maintaining real-time performance in real-world environments. We will clarify this distinction between object-centered and 360° scenes in our revised manuscript.\n\n---\n\n> What limitations if any does storing the rotation entirely within the neural field impose? This seems to be distinct from all other Gaussian parameters which are entirely (e.g. position) or partially (e.g. scale, opacity, color) represented by an explicit Gaussian property.\n\nWe appreciate the reviewer's insightful question about our design choice for rotation representation. Our decision to encode rotations entirely within the neural field rather than storing them explicitly was based on the following reasons:\n\n1. **Performance Impact:** Our experiments show that explicit rotation storage provides only marginal quality improvements (see the attached table), suggesting that the neural field effectively learns to predict rotations without significant accuracy loss.  \n2. **Memory Efficiency:** Representing rotations explicitly would require storing 4 additional parameters per Gaussian (for quaternion representation), increasing memory usage by approximately 30% with minimal benefit.  \n3. **Design Consistency:** This approach maintains consistency with our overall framework where anisotropic effects like rotations are handled by the neural fields, while isotropic properties remain explicit.\n\nThe attached table compares our full model (denoted as *HyRF*) and an alternative which additionally stores a per-Gaussian rotation property (denoted as *HyRF \\+ Rot*.). The comparison demonstrates that the neural-field-predicted rotations achieve similar rendering quality while maintaining superior memory efficiency. We believe this represents a favorable trade-off for most practical applications.\n\n|  | PSNR | SSIM | LPIPS | Size (MB) |\n| :---- | :----: | :----: | :----: | :----: |\n| HyRF | 30.37 | 0.910 | 0.241 | 33.9 |\n| HyRF \\+ Rot. | 30.38 (+0.01) | 0.910 (-0) | 0.240 (-0.001) | 45.5 (+11.5) |\n\n---\n\n\\[1\\] 2D Gaussian Splatting for Geometrically Accurate Radiance Fields. SIGGRAPH 2024\\.  \n\\[2\\] Mip-Splatting: Alias-free 3D Gaussian Splatting. CVPR 2024\\."}, {"review_id": "zrFoy3grnZ", "reviewer": "Reviewer_C7xm", "summary": "The paper introduces Hybrid Radiance Fields (HyRF), a novel scene representation that combines explicit 3D Gaussians with grid-based neural fields to enable real-time, high-fidelity novel view synthesis while achieving a 20× reduction in memory usage compared to traditional 3DGS. Key contributions include a decoupled neural architecture for separate geometry and appearance prediction, and a hybrid rendering strategy that fuses Gaussian splatting with neural background projection. This design effectively addresses the slow rendering of NeRF-based methods and the memory inefficiency of 3DGS. Extensive experiments are conducted to validate the superiority of HyRF, demonstrating both high-quality rendering and high efficiency.", "questions": "Since 3DGS falls short in modeling specular regions, it would be valuable to assess how HyRF performs in such scenarios. The current evaluation only includes quantitative comparisons on reflective cases such as the Materials scene in the NeRF Synthetic dataset. Including more qualitative comparisons on specular scenes would strengthen the evaluation and provide better demonstration of the model’s ability in handling specular scenes.", "limitations": "The authors have adequately addressed the limitations in the paper. Limitations include the aliasing issue and inaccurate surface reconstruction.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The proposed HyRF is a novel representation that achieves high-quality rendering while minimizing memory overhead. The approach achieves a substantial model compression of 12–20× compared to 3DGS while maintaining real-time rendering performance.\n2. The decoupled neural fields for geometry and appearance prediction effectively mitigates the challenges of joint learning, improving the modeling of view-dependent appearance and background. This design choice is validated by ablation studies showing a 0.29 PSNR drop when reverting to a unified field.\n3. The proposed neural background projection addresses the issue of blurry distant objects in 3DGS. Both quantitative and qualitative comparisons demonstrate the effectiveness of this implementation.\n\nWeaknesses:\n1. The overall pipeline largely builds upon existing components like Instant-NGP (grid-based) and 3DGS (gaussians), with several practical implementation enhancements. While this integration somewhat limits the novelty of the method, the idea of fusing grid-based neural fields with 3DGS is conceptually novel and has been explored in a thoughtful and effective manner. In addition, the resulting performance is impressive, and the design choices are effective and well justified.\n2. It is recommended that the authors include a detailed comparison of memory consumption across different methods to provide a more comprehensive evaluation.\n3. To strengthen the experimental evaluation, it is recommended to include more recent 3DGS-based methods such as GOF, Spec-GS and Mini-Splatting2, DashGaussian, which offer complementary strengths in rendering quality and efficiency correspondingly.", "was_revised": true, "final_justification": "The authors addressed all my concerns during the rebuttal period, so I would maintain my positive rating.", "rebuttal": "> The overall pipeline largely builds upon existing components like Instant-NGP (grid-based) and 3DGS (gaussians), with several practical implementation enhancements. While this integration somewhat limits the novelty of the method, the idea of fusing grid-based neural fields with 3DGS is conceptually novel and has been explored in a thoughtful and effective manner. In addition, the resulting performance is impressive, and the design choices are effective and well justified.\n\nWe sincerely appreciate the reviewer's recognition of the novelty and contributions of our work. \nTo provide a further illustration, we believe our core innovation lies in the novel framework that decomposes Gaussian properties between compact per-Gaussian storage and spatially-queryable neural fields, which enables new trade-offs between memory, quality, and speed that weren't previously achievable. Extensive experiments demonstrate that our method achieves superior rendering quality, reduces model size by 20 times compared to 3DGS, and maintains real-time performance.\n\n---\n\n> It is recommended that the authors include a detailed comparison of memory consumption across different methods to provide a more comprehensive evaluation.\n\nWe thank the reviewer for this valuable suggestion. We provide a detailed model storage size (MB) breakdown comparing HyRF with 3DGS on the DeepBlending dataset in the attached table. HyRF replaced the memory-consuming view-dependent colors and anisotropic shapes in 3DGS with compact neural fields, which led to a significant reduction in the model storage size.\n\n|  | Pos. | Opa. | Sca. | Rot. | Col. | Rad. Field | Geo. Field | Total |\n| :---- | :----: | :----: | :----: | :----: | :----: | :----: | :----: | :----: |\n| 3DGS | 34.4 | 11.5 | 34.4 | 45.8 | 550.4 | \\- | \\- | 676 |\n| HyRF | 10.5 | 3.5 | 3.5 | \\- | 10.5 | 3.7 | 1.8 | 34 |\n\n---\n\n> To strengthen the experimental evaluation, it is recommended to include more recent 3DGS-based methods such as GOF, Spec-GS and Mini-Splatting2, DashGaussian, which offer complementary strengths in rendering quality and efficiency correspondingly.\n\nWe conduct comparison experiments with GOF \\[1\\], Spec-GS \\[2\\], Mini-Splatting2 \\[3\\] and DashGaussian \\[4\\] on the DeepBlending dataset, which will be included in our revised version.\n\n* Compared with GOF and Spec-GS, our method offers slightly lower visual quality (within 0.2 PSNR) with more than 20 times smaller model size. Notably, their enhancement techniques (GOF's surface modeling, Spec-GS's ASG encoding) could potentially be integrated into our framework.  \n* Compared with MiniSplatting2 and DashGaussian, HyRF outperforms Mini-Splatting2 and DashGaussian in both visual quality and model compactness, demonstrating the advantages of our hybrid representation approach. Their training acceleration techniques also have potential to be integrated into our framework.\n\n|  | PSNR | SSIM | LPIPS | Size (MB) |\n| :---- | :----: | :----: | :----: | :----: |\n| GOF | 30.42 | **0.914** | 0.237 | 721 |\n| Spec-GS | **30.57** | 0.912 | **0.234** | 765 |\n| MiniSplatting2 | 30.08 | 0.912 | 0.240 | 155 |\n| DashGaussian | 30.02 | 0.907 | 0.248 | 465 |\n| HyRF | 30.37 | 0.910 | 0.241 | **34** |\n\n---\n\n> Since 3DGS falls short in modeling specular regions, it would be valuable to assess how HyRF performs in such scenarios. The current evaluation only includes quantitative comparisons on reflective cases such as the Materials scene in the NeRF Synthetic dataset. Including more qualitative comparisons on specular scenes would strengthen the evaluation and provide better demonstration of the model’s ability in handling specular scenes.\n\nWe appreciate the reviewer's valuable suggestion regarding evaluation on specular scenes. While we are not allowed to include new visual results in this rebuttal, we have conducted additional quantitative comparisons using the anisotropic synthetic dataset from \\[2\\], which features 8 object-centered scenes with strong specular highlights.   \nCompared with 3DGS, HyRF achieves significantly better rendering quality (↑1.58 dB PSNR) while using 82% less memory. The improved performance highlights the benefits of using MLPs over SH coefficients for modeling high-frequency view-dependent effects. This quantitative comparison, together with additional qualitative comparisons on specular scenes will be added to our revised version.\n\n|  | PSNR | SSIM | LPIPS | Size (MB) |\n| :---- | :----: | :----: | :----: | :----: |\n| 3DGS | 33.83 | 0.966 | 0.062 | 47 |\n| HyRF | **35.41** | **0.970** | **0.053** | **8.2** |\n\n---\n\n\\[1\\] Gaussian Opacity Fields: Efficient Adaptive Surface Reconstruction in Unbounded Scenes. SIGGRAPH ASIA 2024\\.  \n\\[2\\] Spec-Gaussian: Anisotropic View-Dependent Appearance for 3D Gaussian Splatting. NeurIPS2024.  \n\\[3\\] Mini-Splatting2: Building 360 Scenes within Minutes via Aggressive Gaussian Densification. ArXiv.  \n\\[4\\] DashGaussian: Optimizing 3D Gaussian Splatting in 200 Seconds. CVPR2025."}]

Author rebuttal:

Meta-review: This paper initially received mixed feedback, but after the rebuttal and discussion phase, the majority of reviewers shifted to a positive stance. The method was generally well-received for its clarity and potential impact, and the consensus among reviewers is to recommend acceptance.

One point that emerged during the AC–reviewer discussion concerns missing citations and positioning. In particular, the paper does not mention or discuss a critical related work, Loco-GS:

*“Locality-aware Gaussian Compression for Fast and High-quality Rendering,” Shin et al., ICLR 2025,*

which has been publicly available since early this year and presents conceptually related ideas. While the proposed HyRF approach includes differences, such as decoupling geometry and color via separate iNGPs, visibility pre-culling, and background rendering, there is also clear methodological overlap in how instant-NGP is combined with 3DGS. The authors should explicitly acknowledge this prior work and clarify the distinctions for the camera-ready.
Decision: Accept (poster)