Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting
Authors: Hongbi Zhou
Zhangkai Ni
Venue: ICML 2025 poster (2025)
Keywords: Novel View Synthesis
3D Gaussian Splatting
Adaptive Density Control
Human Visual System
Primary area: applications->computer_vision

Abstract: 3D Gaussian Splatting (3DGS) has emerged as a powerful technique for novel view synthesis. However, existing methods struggle to adaptively optimize the distribution of Gaussian primitives based on scene characteristics, making it challenging to balance reconstruction quality and efficiency. Inspired by human perception, we propose scene-adaptive perceptual densification for Gaussian Splatting (Perceptual-GS), a novel framework that integrates perceptual sensitivity into the 3DGS training process to address this challenge. We first introduce a perception-aware representation that models human visual sensitivity while constraining the number of Gaussian primitives. Building on this foundation, we develop a perceptual sensitivity-adaptive distribution to allocate finer Gaussian granularity to visually critical regions, enhancing reconstruction quality and robustness. Extensive evaluations on multiple datasets, including BungeeNeRF for large-scale scenes, demonstrate that Perceptual-GS achieves state-of-the-art performance in reconstruction quality, efficiency, and robustness. The code is publicly available at: https://github.com/eezkni/Perceptual-GS

Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting
Hongbi Zhou'
Zhangkai Ni
Abstract
3D Gaussian Splatting (3DGS) has emerged as powerful technique for novel view synthesis. However, existing methods struggle to adaptively optimize the distribution of Gaussian primitives based on scene characteristics, making chal- lenging balance reconstruction quality and effi- ciency. Inspired by human perception, we propose scene-adaptive perceptual densification for Gaus- sian Splatting (Perceptual-GiS) a novel frame- work that integrates perceptual sensitivity into the 3DGS training process to address this chal- lenge. We first introduce a perception-awes resentation that models human visual sensitiv- ity while constraining the number of Gaussian primitives. Building this foundation, we de- velop perceptual sensitivity-adaptive distribu- tion to allocate finer Gaussian granularity to vi- sually critical regions, enhancing reconstruction quality and robustness. Extensive evaluations on multiple datasets, including BungeeNeRF for large-scale scenes, demonstrate that Perceptual- GS achieves state-of-the-art performance in construction quality, efficiency, and robustness. The code is publicly available at: https:// gi thub. www.co/oeeeeeo. eezknizknni.... hwtpr///eeaeeaaeeeeee
Ours (0.114, 5.30)
Pixel-GS (0.155, 8.08)
Mini-Splating- (0.552, 5.99)
GT
Figure 1. The quality-effiiieny trade-off and robustness in large- scale scenes of Perceptual- -S are quantified by LPIPS and the number of Gaussians (millions).
rep-
sian primitives in 3DGS dynamically evolves during training through adaptive density control, using the average position gradient of Gaussians to determine the need for additional primitives, enhancing the model's capacity to capture fine details in local regions. While this strategy improves overall performance, it struggles with efficiently distributing Gaus- sians, leading to blurred regions from too few primitives or redundancy from too many.
To enhance the densification capabilities of 3DGS in local regions, numerous studies (Mallick al., 2024; Xu et al., 2024; Deng et al., 2024; Lyu et al., 2024; Zhang et al., 2025; Liu et al., 2025) have proposed various strategies for improving its performance. Many approaches refine calculating average gradients to more effectively identify Gaussians requiring densification, while others introduce additional metrics for the same purpose. However, these methods often struggle to balance reconstruction quality and computational efficiency, since the number of Gaus- sian primitives is closely tied to perceptual metrics such as LPIPS (Fang & Wang, 2024). To address this challenge, our research explores whether 3D scenes can be represented with higher perceptual quality using a constrained number of Gaussians. Specifically, we integrate insights from human perception into the training process of 3DGS, adaptively distributing Gaussian primitives according to perceptual sensitivity across different local regions of the scene.
1. Introduction
Novel view synthesis, which generates images from new viewpoints based on known multi-view images, has been a long-standing focus in computer vision and is further driven by increasing demand from applications such as VR/AR and digital twins. Recently, 3D Gaussian Splatting (3DGS) (Kerbl al., 2023) has gained significant attention for its exceptional performance, explicitly representing 3D scenes collections of ellipsoidal Gaussian primitives. Un- like traditional deep learning models, the number of Gaus-
School of Computer Science and Technology, Tongji Uni- versity, Shanghai, China. Correspondence to: Zhangkai <zkni cein@toonj..euu.c...
The human visual system (HVS) exhibits characteristics such as contrast sensitivity (Campbell & Robson, 1968), masking effects (Ross Speed, 1991), and just noticeable
Proceedings of the 42nd International Conference Machine Learning. Vancouver, Canada. PMLR 267, 2025. Copyright 2025 by the author(s).

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Perceptual-GS: Seene-adaptive Perceptual Densification for Gaussian Splatting
differences (JND) (Shen et al., 2020), which have been ex- tensively utilized in various computer vision tasks. Several studies have integrated certain human perceptual properties with 3DGS (Franke et al., 2025; Lin et al., 2025a), primarily focusing on foveated rendering by leveraging the reduced acuity of HVS in peripheral vision to adjust the precision of different regions dynamically. These methods improve ren- dering efficiency, but they achieve this by selecting appropri- ate Gaussian primitives during rendering rather than refining their distribution, inheriting the limitations of vanilla 3DGS. Inspired by the Structural Similarity (SSIM) index (Wang et al., 2004), which suggests that human perception evalu- ates image quality primarily through local structures (Xue et al., 2014; Ni et al., 2016), we compute gradient magnitude images from various viewpoints of a 3D scene to capture perceptually sensitive local structures. This guides the train- ing process by adaptively distributing Gaussian primitives to represent perceptually sensitive scene details better.
while enhancing robustness across diverse scenes.
Extensive experiments demonstrate that our proposed Perceptual-GSS achieves sfate-of-the-art performance with fewer Gaussian primitives and can be effectively integrated with other EDGS-baseds methods, maintain- ing excellent performance even in large-scale scenes.
2. Related Work
2.1. -DGG- Novel View Synthesis
Novel view synthesis generates images from unseen view- points using known views of a 3D scene. Early meth- ods, such as NeRF (Mildenhall et al., 2020) and its vari- ants (Turki et al., 2022; Poole et al., 2023; Ni et al., 2024; Xie et al., 2024), employ neural networks for implicit 3D representation but are constrained by the slow volume ren- dering. Recently, 3DGS (Kerbl et al., 2023) introduces an efficient method that explicitly represents scenes using ellip- soidal Gaussian primitives for real-time rendering, gaining attention in various applications (Liu et al., 2024; Zhou et al., 2024; Yu et al., 2024b; Lee et al., 2025; Ren et al., 2025). However, 3DGS struggles to distribute the Gaus- sian primitives efficiently, resulting in redundancy in sim- ple areas and blurriness in texture-rich regions with sparse initial point clouds. Although subsequent quality- methods (Fang & Wang, 2024; Zhang et al., 2025) improve the reconstruction quality, they often increase storage re- quirements and reduce efficiency, while efficiency-focusel methods (Lu et al., 2024; Lin et al., 2025a) lower model complexity at the cost of visual fidelity. As a result, bal- ancing quality and efficiency remains a key challenge in 3DGS.
In this paper, we present Perceptual-GS, a novel framework that integrates multi-view perceptual sensitivity into the training process to optimize the distribution of Gaussian primitives. We first enable a perception-aware represen- tation of the scene by precomputing multi-view sensitiv- ity maps using perceptual sensitivity extraction and mak- ing each Gaussian perception-aware through dual-branch rendering, constrained by RGB and sensitivity loss dur- ing training. Building upon this, we propose a percep- tual sensitivity-adaptive distribution, including perceptual sensitivity -uidedd densification, which enables sufficient number of Gaussians to represent perceptually critical and poorly learned regions and scene-adaptive depth reinitializa- tion, which further improves performance on scenes with sparse initial point cloud. As shown in Figure I, Perceptual- GS achieves superior perceptual quality with fewer Gaus- sians, effectively balancing quality and efficiency. We con- duct experiments across multiple datasets, and the results consistently demonstrate a state-of-thhear trade-off between visual fidelity and model complexity, with notable improve- ments in perceptual metrics and a significant reduction in parameter count even in large-scale scenes. Additionally, Perceptual-GS can be integrated with other JDGGS-baeed works to enhance performance further, showcasing its gen- eralizability. In summary, our contributions are as follows:
2.2. 3DGS Densification
Unlike conventional neural networks with fixed parameter counts, 3DGS initializes Gaussian primitives from point clouds generated through proctuurefroo-Motiien (SfM) and employs adaptive density control to refine local regions. Standard 3DGS uses the average position gradient of Gaus- sians across all viewpoints to decide primitives to densify, which often leads to blurred details due to insufficient prim- itives or redundancy from excessive ones. To address this, many methods optimize densification (Du et al., 2024; Li et al., 2024; Yu et al., 2024a; Mallick et al., 2024; Fang & Wang. 2024: Kheradmand al., 2024; Liu et al., 2025) and most of them refine the metrics used to select Gaussian primitives to be densified. Some enhance the calculation of position gradient with the scaling of Gaussians and fre- quency domain information (Zhang et al., 2024b; 2025), while others propose additional metrics considering average color gradients and coverage of Gaussian primitives (Kim et al., 2024; Fang & Wang, 2024). However, these methods
We design a perception-awas aware representation that al- lows each Gaussian primitive to adapt to perceptual sensitivity across different spatial regions efficiently, capturing human perception of the scene in addition to conventional geometry and color.
We introduce a perceptual aunittvity-adaptive dis- tribution that dynamically allocates Gaussian primi- tives based on perceptual sensitivity in different areas, achieving a balance between quality and efficiency
2

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Perceptual-CG Scenc-adaptive Perceptual Densification for Gaussian Splatting
Perceptual Sensitiiity-ddaativ Distribution
Perception-aware Representation
RGB
Camera
Projection
Rendered
$1 f. (1 45) 4 do
G
Adaplive Density Control
Differentiable Tile Rasterizer
Perceptual Sensitivity Extraction
1l
51
Perceplual Sensitivily-
guided Densification
Scone-adaptive Depth Reinitialization
Operation Flow Gradient Flow
Rendered
Figure 2. Overview of the proposed Pereeptual-GS. We first construct a perception-awan representation of the scene, enabling each Gaussian primitive to adapt to the perceptual sensitivity of its represented region while constraining the number of Gaussians through perceptual sensitivity extraction and flaalbrrach rendering. Subsequently, propose perceptual sensitivity distribution. allocating more Gaussians to perceptually critical areas to enhance reconstruction quality and robustness through perceptual sensitivity- guided densification and scene-adaptive depth reinitialization.
often fail to simultaneously consider both visual fidelity and model complexity when selecting Gaussian primitives for densification, making challenging to achieve a proper balance.
wf(u) ( II (1-o,Cy(u)), (4)
where w/ and (.)() respectively calculate the weight and geometric shape of the elliptical projection of the 3D Gaussian primitive G, under viewpoint
3. Methodology
3.1. Preliminaries
3.2. Overview
3DGS renders 2D images from specific viewpoints by pro- jecting 3D Gaussian primitives into 2D space, sorting them according their distance to the camera, and applying a- blending to produce the final image. The set of Gaussian primitives G is expressed
Motivation. In this paper, we aim to enhance the perfor- mance of 3DGS while addressing the following challenges:
(a) Balancing quality and efficiency: The balance be- tween model quality and efficiency is often neglected when distributing Gaussians, making it challenging achieve high- high-fidelity reconstruction without largely increasing rendering overhead, the number of Gaus- sians is closely tied to perceptual quality.
g = (9:(), E,, SH, 11=1,... N},
(1)
where Gú is the i-th Gaussian primitive, parameterized by its center coordinates μú, covariance matrix E, opacity as, and spherical harmonic coefficients SH, to determine its geometry and color.
(b) Limited utilization of human perception: Relying di- rectly on edge maps to assess the perceptually sensitive regions is influenced by response magnitudes, often overlooking subtle structures and reducing accuracy.
Given μi and Σ, of a Gussian primitive G,, its geometric shape (ar) can be defined as:
(c) Robustness across different scenes: Current ap- proaches lack robustness across diverse scenes, par- ticularly in large-scale ones, as they fail to effectively adapt densification scene-specifie properties.
G₂(z) = e (e-- ' (x-))),
where is coordinate in 3D space. The color of Gaus- sian primitive G; for viewpoint v. denoted as C can be computed using its spherical harmonic coefficients SH,
We aim to improve quality and efficiency by prioritizing the densification of Gaussian primitives in high-sensitivity re- gions to human perception and constraining their generation in low-sensitivity areas, thereby enhancing the perceptual quality of the scene while using fewer Gaussians. Next, we present the framework and detail our four key modules.
After depth-sorting all Gaussian primitives, the rendered RGB color R. (u) at pixel u for viewpoint is determined by the rendering function:
N
RC = Σ of (u)
(3) Framework. Our Perceptual-GS utilizes the high percep- tual sensitivity of the human eye local structures (Xue
1m1
3

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Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting
al., 2014) adaptively identify regions requiring more Gaussian primitives for improved perceptual quality. The pipeline of our proposed method is illustrated in Figure 2, and it can be divided into four individual modules:
eye while discarding absolute response values, as shown in Figure 3.
Specifically, we use the Sobel operator to extract the local structure of the original RGB image I, and the horizontal and vertical gradient convolution kernels G2 and Gy are defined as:
(a) Perceptual Sensitivity Extraction: Local structures are extracted using traditional edge detection, followed by perception-oriented enhancement and smoothing to generate binary sensitivity maps.
2
G
2 1
G
0
(5)
1
1
(b) Dual-branch Rendering: A novel perceptual sensi- tivity parameter e; is added to each Gaussian primitive in 3D space. Subsequently, the dual-branch rendering strategy is employed to map 2D perceptual sensitivity to 3D primitives while limiting the number of Gaus- sians in structurally simple regions.
Perception-oriented Enhancement
(c) Perceptual Sensitivity-guiidd Densification: Gaus- sian primitives with high or medium perceptual sensi- tivity are selectively densified. High-sensitivity regions correspond to areas visually critical to the human eye, while medium medium-sensitivin sensitivity regions require more Gaus- sians for better accuracy.
D
D
D
(d) Scene-adaptive Depth Reinitialization Scenes with sparse initial point cloud derived from Structure-from- Motion (SfM) are identified based the learning of Gaussian perceptual sensitivity, and depth reinitializa- tion is applied to refine the distribution of Gaussian primitives and enhance reconstruction.
Local Structure Extraction
Perception--orientel Smoothing
Figure 3. Pipeline of Perceptual Sensitivity Extraction. An accu- rate and more prone-to-lent binary sensitivity map that reflects human visual perception can be extracted through this module.
The final edge response map G' is computed as:
3.3. Perceptual Sensitivity Extraction
G G= C &
G,)
(6)
Image distortion often assessed by analyzing local struc- tures, as human perception is particularly sensitive dis- tortions in these areas (Wang et al., 2004). In 3DGS, using local image structures to guide density control is also ex- plored (Mallick et al., 2024; Jiang et al., 2024; Xiang et al., 2024; Lin et al., 2025b). While directly using the derived edge response values can improve reconstruction quality, it has limitations due to large differences in response intensi- ties across various perceptually sensitive regions and may overlook areas that also require densification. For example, in the middle row of Figure 3, the texture of leaves has lower response values compared to the more prominent edges in the first row. These subtle structures, though distinguishable to the human eye, do not promote densification as effec- tively as the more pronounced edges. To address this, we first capture the human perception of different regions by extracting gradient magnitude maps (Xue et al., 2014) and then apply perception-orienter enhancement to model the thresholding nature of human perception (Lubin, 1997) and perception-oriente smoothing according to the result of eye-tracking studies (Gu et al., 2016). This process retains binary information about pixel perceptibility to the human
where denotes the convolution operation. After obtaining the gradient magnitude map, we enhance better align with human perception. By setting an enhancement thresh- old Te, every pixel value G(u) at pixel in the response map G' is binarized to retain only binary information:
G= = I(G(u) > TT):
where T(-) is the indicator function and G is the pixel value of the enhanced map at pixel u. We further smooth the binary map using average pooling with threshold T.. resulting in the final perceptual sensitivity map for the scene.
3.4. Dual-branch Rendering
With the 2D perceptual sensitivity maps extracted, mapping them onto 3D Gaussian primitives is essential make the model perception-aware. A straightforward approach is to accumulate pixel values within the areas covered by the 2D projections of Gaussians from multiple viewpoints (Mallick et al., 2024; Rota Buld et al., 2025). However, this strategy fails constrain the sensitivity of different pixels covered

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Perceptual-GS: Seene-adaptive Perceptual Densification for Gaussian Splatting
by a single primitive to remain consistent, which undermines the effectiveness of subsequent perceptual rensitivity-uuided densification. To efficiently capture human perception from 2D sensitivity maps, we propose a dual-branch rendering framework. In this approach, besides the original RGB branch which renders RGB images of the scene, we in- troduce a sensitivity branch to render sensitivity maps by associating each Gaussian primitive G3 with an additional learnable parameter 6, representing the perceptual sensi- tivity of Gassians in spatial regions. To ensure consistency and scalability, the sensitivity values are constrained to the range [0, Il using a sigmoid activation function. This allows sensitivity maps to be rendered similarly to RGB images:
their sensitivity to incorrect intermediate values balance discrepancies. These Gaussians need be split into smaller primitives, as a single primitive cannot adequately capture the complex information within the region. We identify such primitives 9m using thresholds The and TY:
g m (9, & (T7, AelAAii [1,]].
(12)
To prevent excessive densification of Gaussian primitives within objects, we impose weight constraints by a threshold Th on selected primitives based on their sensitivity eval- uation, and the Gaussian primitives requiring perceptual sensitivity-guided densification GD are defined as:
gD = = w > 7" A E (1, Ug) (13)
(u) Σ (4)
wman MAX Σ wF(u) l (14)
where R is the value of rendered sensitivity map at pixel u in view u, and (.) represents the sigmoid function.
nEpia,
where M-X(-) selects the maximum element in the set, pix. denotes all pixels in view e and wi is the maximum weight of Gaussian Gj across all views V of the scene. This ensures that only the most essential Gaussians are densified.
To optimize the framework, we integrate losses from both branches. For the RGB branch, we follow the loss function C as the vanilla 3DGS, which is the weighted sum of L1 and D-SSIM (Wang et al., 2004) loss of view v. For the sensitivity branch, the Binary Cross-Entropy (BCE) loss BBE is employed to align the rendered sensitivity map R with the ground truth Z,5, and the sensitivity loss 2 of view v is defined as:
The vanilla 3DGS employs split and clone operations during densification based on the scaling of Gaussian primitives. Our experiment finds that the split can better capture scene details. Therefore, we only apply the clone operation in perceptual sensitivity- guided densification to 9, when the scene sensitivity 3 falls below a threshold TB, indicating scenes with fewer perceptually sensitive regions. Otherwise, we split the selected Gaussians regardless of their scaling. Specifically, the scene sensitivity B can be defined as the average pixel sensitivity across all views V:
5 CBCE R.).
(9)
The overall loss function L. for viewpoint is defined as a weighted sum of the RGB and sensitivity loss:
L. -1 (1 (1 As
(10)
G Evev aeg.
(15)
where Äs is the weight for the sensitivity loss.
Σ Enerio, v(u)
avgz
(16)
3.5. Perceptual Sensitivity-guided Densification
ppiæ.,
Following the vanilla 3DGS, we initiate the perceptual sensitivity-guided densification after 500 iterations of warm- up. which allows each Gaussian primitive to learn a coarse approximation of geometry, color, and sensitivity, form- ing a foundation for subsequent densification. To better fit the binarized perceptual sensitivity maps, the well-learned sensitivity of each Gaussian primitive should be close to O or 1. Primitives with sensitivity close to 1 are assumed to represent regions with rich local structures, necessitating additional Gaussians for finer detail representation. These Gaussians 9n, are selected using a threshold Th:
where v(u) is the sensitivity value at pixel u and aug. denotes the average sensitivity of view e.
3.6. Scene-adaptive Depth Reinitialization
While perceptual sensitivity guided densification effectively enhances the perceptual quality of reconstruction, in scenes with sparse initial point clouds, excessive densification of large Gaussians may result in inaceurate distributions. To ad- dress this, inspired by (Fang & Wang, 2024), we adaptively apply depth reinitialization on scenes with sparse initial point clouds. The proportion of large Gaussian primitives with medium sensitivity after warm-up y is defined as an indicator of whether the initial point cloud is sparse:
G ( 4 > TA AiE [I]
(11)
For Gaussian primitives with significant sensitivity varia- tions across viewpoints, the training process often converges
?
(17)
9
5

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Perceptual-GS: Seene-adaptive Perceptual Densification for Gaussian Splatting
(18) where sqma represents the scaling of the longest axis of Gi. Smar denotes the set of the longest axis scaling of all Gaussians, and Q2 represents the third quartile, identifying the top 25% largest Gaussian primitives gt. Finally, for scenes where 7 exceeds a predefined threshold Ty, depth reinitialization is applied to enhance performance.
9 = (9,
8 mas Q3(Smee) A [I,
('(a) 0, indicating that smaller opacities are reduced more than larger ones.
In our experiments, we adopt the power function xb as OD(-). To determine the optimal value for k. we test var- ious exponents and find that larger values of & effeetively reduce the number of Gaussian primitives, but may also lead to performance degradation, as shown in Table 1. Although property (c) is not satisfied when A = 1.0, we still include this value in the table, indicating that the opacity of the spa- tial region represented by any primitive remains unchanged after cloning. Ultimately, we select k = 1.2 to strike a balance between reconstruction quality and efficiency.
3.7. Opacity Decline for Clone Operation
In the clone operation of vanilla 3DGS, newly added Gaus- sian primitives inherit all parameters from the densified primitives, leading to an increase in the opacity of the spa- tial regions they represent. However, these cloned Gaussian primitives are typically small and insufficiently trained, mak- ing them prone to redundancy. As their opacities increase, becomes more challenging to prune these potentially re- dundant Gaussians, thereby reducing overall efficiency.
Table I. The effect of various values. All metries are evaluated on the Mip-NeRF 360 dataset and averaged across scenes.
k
PSNRT
SSIM
LPIPS
#G1
1.0
28.02
0.839
0.172
2.74M
To mitigate the impact of cloning redundant Gaussians model efficiency, we propose the opacity decline for the clone operation, which reduces the opacity of the spatial regions represented by the cloned Gaussians, thereby facil- itating the pruning of redundant Gaussian primitives. Ac- cording to the tiphosccmpoiiiing logic, when two Gaussian primitives with opacity of & overlap, the opacity of the corresponding spatial region A can be expressed as:
1.2
28.01
0.839
0.172
2.69M
1.5
27.96
0,838
0.173
2.66M
2.0
27.99
0.838
0.174
2.60M
4. Experiment
4.1. Experiment Setup
Datasets. We evaluated the effectiveness of our method across 21 scenes, including 9 scenes from Mip-NeRF 360 (Barron et al., 2022), 2 scenes from Deep Blend- ing (Hedman et al., 2018), scenes from Tanks & Tem- plcs (Knapitsch et al., 2017), and scenes from BungeeN- eRF (Xiangli et al., 2022).
A (1 â) x.
(19)
Assuming the opacity of the spatial region, i.e., the opacity of the cloned Gaussian primitive, is a before the clone operation, we aim to reduce the opacity A of this region after cloning. Specifically, we apply transform OD(-) to to decline the spatial opacity. The opacity å of the two Gaussian primitives after cloning is determined by solving the equation:
Baselines. We select quality-focused related works that enhance 3DGS performance through optimizing densifica- tion strategies, similar to Perceptual- GS, for comparison to validate the effectiveness of our proposed method. Specif- ically. we chose state-of-the-ar methods, including Pixel- GS (Zhang et al., 2025), Mini-Splatting-D (Fang & Wang, 2024), Haming-3DGs (Mallick et al., 2024), and the vanilla 3DGS (Kerbl et al., 2023) baselines.
à (1
- à)) x à OD(a).
(20)
which yields â = 1 VI-O((a)))
When selecting the OD(-). we aim to apply greater reduc- tions to smaller opacities, encouraging them be pruned, while applying less reduction to higher opacities to avoid removing important Gaussians. Specifically, we require OD(z) to be monotonically increasing for æ E [0 and satisfy the following properties:
Since 3DGS and Pixel-GS did not provide metrics for the number of Gaussian primitives in their original papers, we retrain both models to obtain these values, denoted as 3DGS* and Pixel-GS*. Additionally, to ensure a fair com- parison of FPS and avoid discrepancies caused by testing on different devices, we re-evaluated the rendering speed of various methods. As none of the baselines report results on BungeeNeRI, although it is commonly used to evaluate other 3DGS based methods (Lu et al., 2024; Ren et al., 2025; Chen et al., 2025), we retrain all models on this dataset and use the data from the original papers for all other metrics.
(a) OD(x) S æ, indicating that the transformed value is no larger than the original one.
(b) OD(0) 0, OD(1) = 1, indicating that the trans- formed value is still range [0.1].
(c) a S 0.5. where a represents the unique stationary point of f(x) = x (D()) satisfying its first derivative
Implementation Details. We align the experimental setup
6

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Perceptual-GS: Seene-adaptive Perceptual Densification for Gaussian Splatting
Table Quantitative results on reconstruction quality, comparing our method with state-of-the methods in terms of PSNR1. SSIM
and LPIPS The best
second-best, and third-best results are highlighted.
Mip-NeRF 360
Tanks & Temples
Deep Blending
BungeeNeRF
Method
PSNR1
SSIM
LPIPS
PSNR1
SSIM
LPIPS
PSNR
SSIMI
LPIPS|
PSNR1
SSIMI LPIPS
3DGS
27.71
0,826
0.202
23.61
0,845
0.178
29.54
0,900
0.247
27.64
0,912 0.100
Pixel-GS*
27.85
0.834
0.176
23.71
0.853
0.152
28.92
0.893
0.250
OOM in scene
Mini-Splatting D
27.51
0.831
0.176
23.23
0.853
0.140
29.88
0.906
0.211
25.58
0,861 0.149
Taming-3DGS
27.79
0.822
0.205
24.04
0.851
0.170
30.14
0.907
0.235
OOM in 2 scenes
Ours
28.01
0.839
0.172
23.90
0.857
0.151
29.94
0.907
0.231
27.86
0.918 0.095
Ground Truth
Ours
Taming-3DGS 3DGS
Table 3. Quantitative results on reconstruction efficiency, compar-
ing our method with stale-of-the-ar methods terms of the num- ber of Gaussian primitives (#G) and rendering speed (FPS)
Mip-NeRF 360 Tanks & Temples Blending
BungeeNeRF
Method
HG.
FPS
aG.
FPSt
#GL
FPSt
AG.
FPSt
3DGS
3.14M
193
L83M
247
2.81M
194
6.92M
69
Pixel-G8*
5,23M
105
4.49M
101
4.63M
0OM
seenc
Mini Splatting
D4.69M
120
4.28M
115
4.63M
159
6,08M
86
Pixel-GS
Mini-Splattng-D1
3DGS
Taming- CDGSg
3.31M
122
1.84M
149
2.81
OOM scenes
Ours
2.69M
166
1.72M
218
2.86M
178
4.97M
89
Figure 4. A qualitative comparison of Bilbao in BungeeNeRF.
fectively reducing scene blurriness. As shown in Figure 4, our proposed Perceptual-Gr accurately reconstructs roads, buildings, and grassland at scene boundaries, avoiding ar- tifacts seen in other methods. Similarly, in Figure 5, our method captures ground textures more faithfully, while other methods tend to produce more artifacts in these regions.
with the baselines, and the settings for the newly introduced hyperparameters in Perceptual-GS are provided in the Ap- pendix. To achieve a better balance between quality and efficiency, we use different weight thresholds rd for high- and redium--sensitiee respectively. All training and testing are conducted on single NVIDIA RTX4090 GPU with 24GB of memory.
Gaussians, denoted as T/t' and T
Efficiency Comparison. Table 3 provides quantitative analysis of model complexity and rendering efficiency. Comparing with other quality-focssed methods, Perceptual- GS demonstrates a significant improvement in efficiency and the quality-efficiency balance, rendering high-fidelity novel views with faster speed and fewer Gaussian primi- tives.
Metrics. To evaluate the performance of different methods, we use common metrics including PSNR, SSIM (Wang et al., 2004), and LPIPS (Zhang et al., 2018). Besides, we consider the number of Gaussian primitives (#G) in millions (M) and rendering speed (FPS). These metrics highlight the superior trade-off between quality and efficiency achieved by our approach.
4.3. Ablation Study
Effectiveness of Perceptual Sensitivity Extraction. We evaluate the impact of enhanced sensitivity maps for guid- ing densification by comparing them to edge response maps derived from the Sobel operator. Since scene-adaptive depth reinitialization does not function properly without perception-oriemtel enhancement, we exclude depth reini- tialization for all scenes during the experiments and apply only split in perceptual sensitivity- guidded densification. As shown the "w/o PE" results in Table 4, the synthesized novel views exhibit similar reconstruction quality to vanilla 3DGS without perception-oriente enhancement (PE) since the inaccurate learning of sensitivity.
4.2. Comparisons with State-of-the-art
Quantitative Comparison. Table 2 shows the quanti- tative comparison of Perceptual-GS with state-of-the-art methods in novel view synthesis on reconstruction qual- ity. Across four datasets, our proposed Perceptual-GS achieves superior reconstruction quality, particularly ex- celling SSIM and the perceptually relevant LPIPS met- ric. Unlike Pixel-GS and Taming-3DGS which face CUDA out-of-memory (OOM) issues due excessive Gaussians in large-scale scenes, Perceptual-GS adaptively distributes primitives based on the perceptual sensitivity of different regions, achieving superior quality-efficiency trade-off.
Effectiveness of Perceptual Sensitivity- Densifica- tion. Perceptual sensitivity- -guided densification is a key component of our method. To assess the individual contribu-
Qualitative Comparison. The proposed Perceptual-GS allocates more Gaussians to object details and edges, ef-
7

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Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting
Ground Truth
Ours
Taming-3
Pixel-GS
Mini-Splatting-D
3DGS
Stump
Treehill
Figure 5. A qualitative comparison of Perceptual-GG with other methods on Stump and Treehill in Mip-NeRF 360.
ness in removing redundant primitives.
Table 4. Ablation studies various modules of Perceptual-GS. All metrics are evaluated on the Mip-NeRF 360 dataset and averaged across all scenes.
4.4. Analysis
Integrating with existing works. In Table 5 and Table 6, we integrate our proposed framework with vanilla 3DGS and PixeI-GS, denoted as w/ Ours, further demonstrating its effectiveness. The proposed method achieves significant improvements across all quality metrics on both baselines, while also reducing the number of Gaussian primitives in most datasets, thereby enhancing efficiency.
PSNRT
SSIM
LPIPS
#G
FULL
28.01
0.839
0.172
2.69M
3DGS*
27.71
0.826
0.202
3.14M
w/o PE
27.74
0.825
0.204
2.09M
w/ HD
27.74
0.826
0,204
2.02M
w/o MD
27.86
0.831
0.179
2.56M
w/o SDR
27.93
0.832
0.176
2.68M
w/o OD
27.99
0.839
0.172
3.25M
It is worth noting that our method remains effective even under sparse-view settings. In Table 7, we integrate the proposed method with CoR-GS (Zhang et al., 2024a) and conduct quantitative comparison on the 24-view Mip-NeRF 360 dataset, which also demonstrates a significant perfor- mance improvement. Since the original paper did not pro- vide the 24-view dataset, we retrain the model, denoted as CoR-GS* using dataset reconstructed according to the instructions in the official released code. The versatility of our method enables its integration with other approaches to achieve even better performance.
tions of densifying high- and mediumm-sensiiivee Gaussians, we conduct ablation studies by separately removing their densification processes. The results, presented as "w/o HD" in Table 4, show that excluding high-sensitivityy Gaussian densification (HD) reduces Gaussian primitives significantly, causing the performance to degrade to levels comparable vanilla 3DGS. Similarly, removing pediium-sensitvvigy Gaussian densification (MD) impairs the accurate recon- struction of detailed regions. However, shown in "w/o MD" in Table 4, it still achieves notable improvements over vanilla 3DGS thanks to the dual-branch rendering which reduces the proportion medium medium--ensitivvtty Gaussians.
Effectiveness dual-branch rendering. In addition to mapping perceptual sensitivity, our experiments reveal that dual-branch rendering (DBR) also reduces the number of Gaussian primitives. As shown in Table 8, we compare the performance and efficiency of the vanilla 3DGS, 3DGS with OD, and with both DBR and OD to demonstrate its effect. The results indicate that DBR can slightly constrain the number of Gaussians while maintaining comparable quality since low-sensitivity Gaussians with well-learned sensitivity exhibit lower sensitivity loss. After weighting, their total loss is reduced, preventing them from reaching the position gradient threshold and thereby suppressing densification.
Effectiveness of Scene-ddaptive Depth Reinitialization To verify that the improvement of the Perceptual-GS is not perform
solely attributed depth reinitialization, ablation study the scene-adaptive depth reinitialization (SDR). The results in "w/o SDR" in Table 4 show that our method achieves improved outcomes even without depth reinitialization, maintaining a balance between quality and efficiency to achieve state-of-the-art performance.
Effectiveness of Opacity Decline. Our proposed Opacity Decline (OD) mechanism for clone operation in densifica- tion encourages the removal of redundant Gaussian prim- itives while preserving similar visual quality. As shown in Table 4, with Opacity Decline applied, Perceptual-CG achieves comparable performance in quality metrics using significantly fewer Gaussians, demonstrating its effective-
Rendering quality in low-sensitive regions. Although the bral-b rendering strategy suppresses the densification of Gaussian primitives in low-sensitivity regions, does not compromise the reconstruction quality in these areas. In Table 9, the perceptual sensitivity map mask
8

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Perceptual-GS: Seene-adaptive Perceptual Densification for Gaussian Splatting
Table The quantitative result of the proposed method based on different models on Mip-NeRF 360, Tanks & Temples, and Deep Blending. Metrics are averaged across the scenes. The improvements and reductions in the metrics are highlighted.
Mip-NeRF 360
Tanks Temples
Deep Blending
Method
PSNR
SSIM
LPIPS.
#GL
PSNR
SSIM
LPIPS
#G.
PSNR
SSIM
LPIPS
#GL
3DGS*
27.71
0.826
0.202
3.14M
23,61
0.845
0.178
L83M
29.54
0,900
0.247
2.81M
w/ Ours
28.01
0.839
0.172
2.69M
23,90
0.857
0.151
1.72M
29.94
0,907
0.231
2.86M
+0.30
+0.013
-0.030
-0.45M
+0.29
+0.012
-0.027
-0,11M
+0.40
+0.007
-0.016
+0.05M
Pixel-GS*
27.85
0.834
0.176
5,23M
23.71
0.853
0.152
4.49M
28.92
0.893
0.250
4.63M
w/ Ours
28.01
0.841
0.167
3.37M
23,95
0.859
0.142
2.96M
29.71
0.901
0,233
3.59M
A
+0.16
+0.007
-0.009
-1.86M
+0.24
+0.006
-0,010
-1.53M
+0.79
+0.008
-0.017
-1.04M
Table The quantitative result of the proposed method is based on different models on BungceNeRFF, We present metries averaged on the dataset and from three single scenes.
BungeeNeRF
Pompidou
Chicago
Amsterdam
Method
PSNR
LPPP) #G.
SSNR
#GL
SSIRY
#GL
PSNR
LPIM
#G
3DGS*
27.64 0.912
0.100 6.92M
27.00
0.916
0.095
9.11M
27.97
0.927
0.086
6.32M
27.60
0.913
0.100
6.19M
w/ Ours
27.86
0,918
0.095
4.97M
27.18
0,922
0.089
6.12M
28.39
0.933
0.081
4.48M
27.89
0.922
0.087
4.96M
A
+0,22
+0.006 -0,005 -1.95M
+0.18
+0.006
-0.006
-2.9M
+0.42
+0.006
-0.005
-1844M
+0.29
+0.009
-0.013
-1.23M
Pixel-GS**
OOM in scene
OOM
27.52
0.921
0.090
9.76M
27.76
0.916
0.095
10.26M
w/ Ours
27.64
0.913
0.100
5.92M
27.01
0.918
0.092
7.39M
28.36
0.930
0.081
5.58M
27.98
0.922
0.085
6.60M
A
+0.84 +0.009 -0.009
4818M +0.22 +0.006
-0.010 -3.66M
Table 7. The quantitative result of the proposed method is based on
Table 9. Quantitative comparison between Perceptual-GSS and the vanilla 3DGS on rendering results masked by the perceptual sensi- tivity map.
CoR-GS on 24-view Mip-NeRF 360. Mctrics are averaged across the scenes.
PSNR1
SSIMt
LPIPS
PSNR
SSIM
LPIPS
CoR-GS*
22.26
0.664
0.341
3DGS* Ours
40.28
0.990
0.014
w/Ours
22.42
0.681
0.281
40.72
0.991
0.014
A
+0.16
+0.017
-0.060
Table 8. Effect of dual-branch rendering on constraining the num- her of Gaussians.
5. Conclusion
In this paper, we introduce Perceptual-GG leveraging scene- adaptive perceptual densification to achieve superior per- ceptual quality with constrained Gaussian primitives. The proposed method promotes the densification of Gaussians in gigh-sensitivtt regions according to human perception while suppressing it in low-sensitivity areas, achieving bal- ance between reconstruction quality and efficiency. Specifi- cally, we first extract local scene structures using gradient magnitude maps and enhance them based on the character- istics of human perception. During training, a stual-branch rendering strategy maps 2D sensitivity onto 3D Gaussians and constrains the number of primitives. In addition to the adaptive density control in vanilla 3DGS, we densify high- and
PSNR1
SSIM
LPIPS!
#G.
3DGS*
27.71
0.826
0.202
3.14M
+OD
27.74
0,825
0,207
2.22M
+OD +DBR
27.69
0.822
0.212
1.94M
to retain only the low-sensitive pixels and compare our method with the vanilla 3DGS. The results validate that Perceptual-GS achieves comparable rendering performance in low-sensitivity regions.
Effectiveness on large-scale scenes. As shown in Fig- ure 1, Table 2 and Table 3, our method demonstrates great robustness in large-scale scenes, avoiding introducing exces- sive Gaussians like Pixel-GS and the reconstruction failures in Mini-Splatting-D D. This is largely attributed to our dual- branch rendering and perceptual ausitivittyguuide densifi- cation, which limit the number of densified Gaussians while adaptively identifying regions requiring more primitives.
Gaussians to improve reconstruc- tion quality. Finally, acene-ad depth reinitialization is applied for better performance. Extensive experiments on multiple datasets demonstrate that our method effectively balances reconstruction quality and efficiency, achieving offte-of-the-art performance in novel view synthesis tasks.

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Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting
Acknowledgement
Image-based Rendering. ACM Transactions on Graphics, 37(6):1-15, 2018.
This work was supported in part by the National Natural Science Foundation of China under Grant 62201387 and in part by the Fundamental Research Funds for the Central Universities.
Jiang, H., Xiang, X., Sun, H., Li, H., Zhou, L.,. Zhang, X., and Zhang, G. Densssierr, Geometry-Texture Aware Densification for Seightuality Photorealistic 3D Gaussian Splatting. arXiv preprint arXiv:2412. 168009 2024.
Impact Statement
This paper focuses on advancing 3DGS-based Novel View Synthesis, guiding the training process of 3DGS with hu- man perception for a better trade-off' between quality and efficiency. While our work may have potential societal implications, none require specific emphasis at this time.
Kerbl, B., Kopanas, G., Leimkühler, T., and Drettakis, G. 3D Gaussian Splatting for Real-Time Radiance Field Ren- dering. ACM Transactions on Graphics, (4):1-14, 2023.
Kheradmand, S., Rebain, D., Sharma, G., Sun, W., Tseng, Y.-C., Isack, H., Kar, A., Tagliasacchi, A., and Yi, K. M. 3D Gaussian Splatting Markov Chain Monte Carlo. In Proceedings of the Annual Conference on Neural Infor- mation Processing Systems, pp. 80965-80986, 2024.
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Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting
Appendix
A. Implementation Details
We adopt the default settings of 3DGS and show the additional hyperparameters introduced in Perceptual-G5 in Table 10.
Table 10. Definition and value of hyperparameters introduced in Perceptual-GS.
H.P. [Definition
value
Te
perception-oriented enhancement threshold
0.05
T,
perception-oriente smoothing threshold
0.3
As
sensitivity loss weight
0.1
Iters
high-sensitivity Gaussians densification interval
1000
Item
medium--ensittiityy Gaussians densification interval
1500
Th
high-sensitivity Gaussians threshold perceptual sensitivity
0.9
72
low-sensitivity Gaussians threshold of perceptual sensitivity
0.3
7%
high-sensitivity Gaussians threshold of weight
25
Tm
ceddium-sensitivii Gaussians threshold of weight
10
Ts
high-sensitivity scenes threshold
0.85
Ty
scenes with sparse initial point cloud threshold
0.55
B. Additional Qualitative Comparisons with State-of-the-art
We provide additional qualitative comparisons in this section to further showcase the superior visual quality, efficiency, and balance achieved by Perceptual-GS. As shown in Figure 6, the proposed method excels in reconstructing intricate details, such as the complete shadow on the crosswalk in Amsterdam. Figure 7 demonstrates that our method generates novel views with fewer blurred regions while achieving better efficiency in both storage and rendering speed. Besides, our method achieves better performance in depth rendering, as illustrated in Figure Compared to Pixel-GS, the proposed method reconstructs scene geometry with higher accuracy. These results underline the robustness and effectiveness of Perceptual-Gr particularly in large-scale scenes.
We also compare the qualitative results of integrating proposed method with different existing approaches, shown in Figure 9, Figure 10, and Figure 11, where our method is respectively integrated with the vanilla 3DGS, the quality- Pixel-GS, and CoR-GS designed for sparse- -iewe settings, denoted as w/ Ours. Our method significantly reduces blurriness in the scenes and is able to reconstruct some texture details more clearly.
To better demonstrate the effect of the perceptual sensitivity map, we present the distribution of Gaussian primitives in regions with different sensitivity Figure 12 and rendered sensitivity maps during training Figure 13. Furthermore, Figure 14 shows the rendered results with the perceptually sensitive regions masked and compares our method with the vanilla 3DGS. The results indicate that there is no compromise in reconstruction quality in low-sensitive regions, though our dual-branch rendering strategy reduces the number of primitives in these areas.
C. Visualization of Ablation Studies
To visually demonstrate the impact of each module on Perceptual-GS, we provide the visualization of our ablation studies. As shown in "w/o PE". "w/o HD" and "w/o MD" Figure 15, compared with the full model, they exhibit more blurriness in detailed areas, with noticeable decline in reconstruction quality. In contrast, without scene-adaptive depth reinitialization and opacity decline, the model still maintains similar visual effect to the full one, demonstrating the effectiveness of our proposed perceptual sensitivity-guide densification, illustrated in "w/o SDR" and "w/o OD" in Figure 155.
D. A blation on Hyperparameters
We conduct ablation studies on several key hyperparameters to assess their impact on our proposed method.
13

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Perceptual-Gas Scene-adaptive Perceptual Densification for Gaussian Splatting
Ground Truth
Ours
Taminng3 3DG
Pixel-GS
Mini-Splatting-D
3DGS
Amsterdam
Ground Truth
Ours
Taming-3DGS
Pixel-GS Mini-Splatting-D
3DGS
(b) Rome
Figure 6. A qualitative comparison of Perceptual-GS with other methods on Amsterdam and Rome in BungeeNeRF.
Ground Truth
Ours
Pixel-GS
3DGS
LPIPS: 0.189
Ground Truth
Ours (3.89, 162)
LPIPS: 0.215
L.PPS: 0.191
3DGS (5.78, 100)
Pixel-GS (8.46, 59)
Figure 7. Qualitative efficiency results on Bicycle in Mip-NeRF 360 show that our approach achieves superior visual quality compared to the quality- quauity-focused focused method Pixel-GS, using less than half the number of Gaussian primitives and more than doubling the rendering speed. The number of Gaussians (in millions) and FPS are shown as (Number, FPS).
Weight of Sensitivity Loss As: The balance between the contributions of the rendering branches the final optimization can be adjusted by modifyyng Àş. As shown in Table 11, increasing the weight of the sensitivity branch effectively reduces the number of Gaussian primitives while maintaining relatively high perceptual quality comparing with the vanilla 3DGS. However, for better reconstruction quality, we adopt a lower value for Àg-
(b) Threshold of Weight T and T We evaluate the performance with different values of the weight thresholds Tox and TW in Table 11. Since high-sensitivity Gaussian primitives represent more complex structures, lower threshold increases their densification. As the threshold decreases, perceptual quality improves slightly. but more Gaussian primitives are introduced. Similarly, T affects quality and efficiency, though to a lesser extent, because dual-brande rendering drives the sensitivity of more Gaussian primitives toward or 1, resulting in fewer medium- sensitivivy Gaussians. Therefore, we select relatively higher value for Th to achieve a better trade-off and a lower value for Ti
14

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Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting
Scene
Ours
Pixel-GS
Scene
Ours
Pixel-GS
Figure 8. qualitative comparison the rendering depth between Perceptual-GS and Pixel-GS on Mip-NeRF 360.
GT
3DGS
w/ Ours
GT
3DGS
w/ Ours
Figure 9. The qualitative result of the proposed method is based on the vanilla 3DGS on Mip-NeRF 360.
15

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Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting
GT
PixcI-GS
w/ Ours
GT
Pixel-GS
w/ Ours
Figure 10. The qualitative result of the proposed method based on Pixel-GS on Mip-NeRF 360.
GT
CoR-GS
w/ Ours
GT
CoR-GS
w/ Ours
Figure 11. The qualitative result of the proposed method is based on CoR-GS on 24-view Mip-NeRF 360.
prioritize quality.
(c) Densification Interval Her, and Iler To determine the optimal densification intervals, we experiment with different values of [tery, and Iterm, as shown in Table 11. The densification intervals for high- and medium-sensstivity Gaussians, like T/ and The also influence the model's quality and efficiency. We find that their effects are similar, so we use smaller Iter, to improve reconstruction quality while selecting a slightly larger Iterm to identify medium cediuum-ensitivity Gaussian primitives during optimization better.
16

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Perceptual-GS. Scene-adaptive Perceptual Densification for Gaussian Splatting
High
Medium
Low
Rendered
Sensitivity Map
Primitive Distribution
Figure The visualization of the effect perceptual sensitivity map in different spatial regions. Perceptual- GS distributes more primitives to perceptually sensitive regions.
Iteration 5000
Iteration 10000
Iteration 15000
Itcration 5000
Iteration 10000
Iteration
GT
GT
15000
Figure 13. The visualization of perceptual sensitivity maps rendered during the training process.
E. Per Scene Quantitative Comparisons with State-of-the-art
We present per-scene quantitative comparisons with existing methods to further illustrate the improvements in quality, efficiency, and their balance achieved by Perceptual-GS, as shown in Table 12, Table 13, Table 14, Table 15, Table 16, Table 17, Table 18, Table 19, Table 20, Table 21, Table 22, Table 23. To evaluate the overall performance of the model in
17

---

Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting
Sensitivity Map
Full
Non-sensitive
Sensitivity Map
Full
Non-sensitive
SDGS
3DGS
Ours
3DGS
Ours
SinO
SDGS
3DGS
Ours
SIIO
3DGS
2DGS
Ours
SmnO
SpOS
Figure Qualitative comparison of reconstruction quality low-sensitive regions between 3DGS and proposed method.
terms of both efficiency and perceptual quality, we introduce a new metric, QEB:
100 x #G x LPIPS
QEB
(21)
FPS
which jointly considers rendering quality and efficiency, and serves as reference for balancing the trade-off between reconstruction fidelity and speed. The proposed method demonstrates notable improvements perceptual metrics such as LPIPS, along with a significant reduction in the number Gaussian primitives. Notably, Perceptual GS achieves a superior quality-efficiency trade-off in large-scale scenes from BungeeNeRF, highlighting its exceptional robustness.
18

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Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting
Ground Truth
full
w/o HD
w SDR
3DGS
W/O PE
W/O MD
W/O OD
Figure 15. Visual results the ablation study, highlighting the impact of cach module on reconstruction quality.
Table Ablation studies on hyperparameters, with adopted settings highlighted. metrics are evaluated on the Mip-NeRF dataset and averaged across scenes.
H.P.
Value
PSNR1
SSIM
LPIPS
#G
0.1
28.01
0.839
0.172
2.69M
As
0,3
27.82
0.835
0.181
2.10M
0.5
27.48
0.823
0.196
1.92M
10
28.05
0.841
0.166
3.61M
15
28.00
0.840
0.169
3.09M
25
28.01
0.839
0.172
2.69M
10
28.01
0.839
0.172
2.69M
Tym
27.98
0.838
0.173
2.65M
25
27.97
0.838
0.174
2.63M
1000
28.01
0.839
0.172
2.69M
Iters
1500
27.95
0.838
0.174
2.57M
2000
27.93
0.837
0.175
2.52M
1000
27.92
0.839
0.172
2.70M
Iter
1500
28.01
0.839
0.172
2.69M
2000
27.98
0.839
0.173
2.66M
F. Per Scene Quantitative Result Integrating the Proposed Method with Existing Works
In this section, we provide per scene quantitative results on additional metrics to highlight the effectiveness of integrating our method with existing approaches. As shown in Table 24, Table 25, Table 26, Table 27, Table 28, Table 29, Table 30, Table 31, Table 32, Table 33, Table 34, Table 35, we integrate our method with 3DGS and Pixel-GS, achieving significant improvements both reconstruction quality and efficiency. While the vanilla Pixel-GS demonstrates a poor quality- efficiency trade-off BungeeNeRF,
method markedly enhances its performance in large-scale scenes, detailed in Table 35. In Table 36, Table 37, Table 38, we integrate method with CoR-GS. Since CoR-GS fails to distribute sufficient number of Gaussian primitives for high-quality reconstruction under sparse-view settings, we only report the results in terms of quality metrics for comparison. Although the original method achieves slightly higher PSNR and SSIM in certain scenes due to blurriness, our method consistently outperforms CoR-GS the perceptual metric LPIPS across all scenes, indicating superior perceptual quality.
19

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Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting
Table 12. Per quantitative results on Mip-NeRE 360, Tanks & Temples and Deep Blending, comparing method with offte-of-the- art methods in terms of PSNR1.
PSNR1
Method
Bicycle Bonsai Counter Kitchen
Room
Stump Garden Flowers Trechill
Train
Truck
Drjohnson Playroom
3DGS*
25.617
32.349
29.144
31.450
31.628
26.913
27.735
21.808
22.736
21,768
25,452
29.139
29.935
Pixel-GS*
25.733
32.649
29.227
31.795
31.783
27.182
27.820
21.885
22.572
21.985
25.438
28.130
29.708
Mini-Splatting-D
25.55
31.72
28.72
31.75
31.41
27.11
27.67
21.50
22.13
21.04
25.43
29.32
30.43
Taming-3DGS
25.47
32.22
29.03
31.74
32.12
26.96
27.64
21.76
23.09
22.23
25.90
29.68
30.44
Ours
25.956
32.730
29.452
32.005
32.220
27.302
27.961
21.798
22.634
22.154
25.637
29.663
30.219
Table 13, Per scene quantitative results on Mip-NeRF 360, Tanks & Temples and Deep Blending, comparing method with ofate-of-the- art methods in terms of SSIM
SSIM
Method
Bicycle Bonsai Counter Kitchen Room Stump
Garden Flowers Treehill Train Truck Drjohnson Playroom
3DGS*
0.778
0.948
0.916
0.933
0.927
0.784
0.874
0.621
0.651
0.810
0.879
0.898
0.902
Pixel-GS*
0.792
0.951
0.920
0.936
0.930
0.797
0.878
0.652
0.652
0.823 0.883
0.886
0.900
Mini-Splatting-D D
0.798
0.946
0.913
0.934
0,928
0.804
0.878
0.642
0.640
0.817 0.890
0.905
0,908
Taming-3Doe
0.78
0.94
0.91
0.93
0,92
0.78
0.87
0.61
0.65
0.81 0.89
0,91
0,91
Ours
0.805
0.953
0.922
0.936
0,936
0.807
0.877
0.654
0.657
0.826
0.888
0.905
0.908
Table 14. Per scene quantitative results on Mip-NeRF 360, Tanks & Temples and Deep Blending, comparing method with state-of-the art methods in terms of LPIPS
LPIPSI
Method
Bicycle Bonsai Counter Kitchen Room Stump
Garden Flowers Treehill Train Truck Drjohnson Playroom
3DGS*
0.205
0.173
0.178
0.113
0.191
0.208
0.103
0.329
0.319
0.209
0.147
0.247
0.246
Pixel-GS*
0.174
0.161
0.162
0.107
0.184
0.181
0.094
0.253
0.269
0.182
0.121
0.256
0.243
Splatting-D
0.158
0.175
0.172
0.114
0.190
0.169
0.090
0.255
0.262
0.181
0.100
0,218
0.204
Taming-3D
0.20
0.20
0.20
0.12
0.21
0.20
0.10
0.34
0.31
0.21
0.13
0.24
0,24
Ours
0.165
0.151
0.157
0.108
0.168
0.175
0.098
0.257
0.273
0.184
0.117
0.230
0.231
Table 15. Per scene quantitative results on Mip-NeRF 360, Tanks & Temples and Deep Blending, comparing method with state-of-the- art methods in terms of the number of Gaussian primitives (#G)..
#GL
Method
Bicycle Bonsai Counter Kitchen Room Stump Garden Flowers Trechill
Train
Truck Drjohnson Playroom
3DGS*
5.78M
1.25M
1.17M
1.75M
1.49M
4.73M
5,07M
3.38M
3.62M
1.08M
2.58M
3.28M
2.33M
Pixel-GS*
8.46M
2.07M
2.50M
3.03M
2.49M
6.46M
7.55M
7.08M
7.47M
3.80M 5.18M
5.51M
3.76M
Mini-SplattingD
6.03M
3.78M
3.75M
3.78M
4.05M
5.41M 5.81M
4.87M
4.86M
3.95M
4.58M
4.91M
4.35M
Taming-3DGS
5.99M
1.19M
1.19M
1.61M
1.55M
4.87M
5.07M
3.62M
3.77M
1.09M
2,58M
3,27M
2.33M
Ours
3.89M
1.58M
1.49M
1.63M
1.74M
3.81M
3.03M
3.55M
3.48M
1.39M
2.05M
3.43M
2.29M
20

---

Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting
Table 16. Per scene quantitative results on Mip-NeRF 360, Tanks & Temples and Deep Blending, comparing method with state-of the art methods in terms of rendering speed (FPS)..
FPS
Method
Bicycle Bonsai Counter
Kitchen Room
Stump
Garden Flowers
Trechill
Train
Truck
Drjohnson Playroom
3DGS
100
310
244
195
235
151
122
200
176
285
208
160
228
Pixel-GS*
59
184
122
113
141
95
76
71
81
111
91
89
138
Mini-Splatring-D
107
135
109
112
137
121
105
127
125
117
113
147
170
Taming-3DG5
137
122
129
122
107
129
120
152
146
111
Ours
162
206
155
154
173
156
181
151
154
210
225
143
213
Table scene quantitative results on Mip-NeRE 360, Tanks Temples Deep Blending, comparing our method with sffte-of-the- art methods in terms of the balance between quality and efficiency (QEB)L.
QEB
Method
Bicycle Bonsai Counter Kitchen Room Stump Garden Flowers Trechill Train Truck Drjohnson Playroom
3DGS*
1.185
0.070
0.085
0.101
0.121
0.652
0.428
0.556
0.656
0.079 0.182
0.506
0.251
Pixel-GS*
2.481
0.182
0.332
0.284
0.323
1.231
0.934
2.503
2.481
0.616 0.694
1.585
0.662
Mini-SplattingD
0.890
0.490
0.592
0.385
0.562
0.756
0.498
0.978
1.019
0.827 0.644
0.728
0.522
Taming-3DGS
1.361
0.170
0.174
0.158
0.252
0.798
0.474
0.954
0.974
0.151
0.230
0.707
0.378
Ours
0.396
0.116
0.151
0.114
0.169
0.427
0.164
0.604
0.617
0.122 0.107
0.552
0.248
Table 18, Per scene quantitative results on BungeeNeRF, comparing our method with state-of-the-as methods in terms of PSNR
PSNR
Method
Amsterdam
Barcelona Bilbao Chicago
Hollywond Pompidou Quchec
3DGS*
27.600
27.379
28.800
27.972
26.150
26.997
28.711
27.535
P
27.756
26.780
28.582
27.524
26.121
OOM
28.245
26.583
Mini-Splatting-D
27.008
26.227
27.990
27.376
26.042
26,046
27.804
16.141
Taming-3DGS
27.553
OOM
28.849
28.292
26.408
OOM
28.900
27.484
Ours
27.887
27.546
29.081
28.390
26.146
27.180
29.013
27.647
Table 19, Per scene quantitative results BungeeNeRF, comparing our method with state-of-the methods in terms of SSIM
SSIM
Method
Amsterdam
Barcelona
Bilbao
Chicago
Hollywood
Pompidou
Quebec
3DGS*
0.913
0.915
0.915
0.927
0.868
0.916
0.931
0.914
Pixel-GS
0.916
0.904
0.912
0.921
0.866
OOM
0.924
0.896
Mini-Splatting-D
0.909
0.894
0.911
0.920
0,865
0,901
0,920
0.568
Taming-3DGS
0.911
0OM
0.918
0.931
0.869
0OM
0.936
0.914
Ours
0.922
0.919
0.922
0.933
0.868
0.922
0.938
0.918
Table 20, Per scene quantitative results on BungeeNeRF, comparing our method with statc-of-the- methods in terms of LPIPS
LPIPS
Method
Amsterdam
Barcelona
Bilbao
Chicago
Hollywood
Pompidou
Quehec
3DGS*
0.100
0.087
0.099
0.086
0.134
0.095
0.094
0.102
Pixel-GS*
0.095
0.101
0.103
0.090
0.138
OOM
0.106
0.129
Mini-Splatting-D
0.102
0.117
0.104
0.097
0.158
0.115
0.111
0.385
Taming-3DGI
0.113
0OM
0.100
0.088
0.150
OOM
0.096
0.112
Ours
0.087
0.084
0.092
0.081
0.140
0.089
0.087
0.098
21

---

Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting
Table 21. Per scene quantitative results on BungeeNeRF, comparing our method with state-of-the-art methods in terms the number
Gaussian primitivex(#G))-
#G
Method
Amsterdam
Barcelona
Bilbao
Chicago
Hollywood
Pompidou
Quebec
Rome
3DGS*
6.19M
8.46M
5.51M
6.32M
7.04M
9.11M
6.01M
6.74M
Pixel-GS*
10.26M
11.04M
7.98M
9.76M
9.88M
OOM
8.37M
8.08M
Mini-Splatting-D
6.65M
6.79M
5,40M
5.65M
5.74M
6.73M
5.72M
5.99M
Taming-3DGS
6.20M
OOM
5.51M
6.30M
7.06M
OOM
6.04M
6.75M
Ours
4.96M
5.97M
3.76M
4.48M
4.88M
6.12M
4.57M
5.03M
Table 22. Per scene quantitative results on BungeeNeRF, comparing our method with taae-of-thee-ae methods in terms of rendering Speed(FPS)t.
FPS
Method
Amsterdam
Barcelona
Bilbao
Chicago
Hollywood
Pompidou
Quebec
Rome
3DGS
70
63
72
67
72
57
76
71
Pixel-GS*
54
43
52
OOM
57
Mini-Splatting-D
80
80
97
93
90
80
85
85
Taming-3DGG
63
OOM
66
70
65
OOM
66
66
Ours
85
83
88
92
97
78
98
87
Table 23, Per quantitative results BungeeNeRF, comparing method with state-of-the-ar methods in terms of the balance between quality and efficiency(QEB)..
QEB
Method
Amsterdam
Barcelona
Bilbao
Chicago
Hollywood
Pompidou
Quchec
Rome
3DGS*
0.884
1.168
0.758
0.811
1.310
1.518
0.743
0.968
Pixel-GS*
2.321
2.323
1.522
2.043
2.622
0OM
1.557
1.681
Mini-Splatting-D
0.848
0.993
0.579
0.589
1.008
0.967
0.747
2.713
Taming-3DGS
1.112
OOM
0.835
0.792
1.629
OOM
0,879
1.145
Ours
0.508
0.604
0.393
0.394
0.704
0.698
0.406
0.567
Table 24, Per scene quantitative result of the proposed method is based on different models on Mip-NeRF 360, Tanks & Temples, and Deep Blending terms of PSNR1. Metries are averaged across the scenes.
PSNR
Method
Bicycle Bonsai Counter Kitchen
Room
Stump Garden Flowers Trechill
Train
Truck
Drjohnson Playroom
*
25.617
32,349
29.144
31.450
31.628
26.913 27.735 21.808
22.736
21.768
25.452
29.139
29.935
w/ Ours
25.956
32.730
29.452
32.005
32.220
27.302 27.961
21.798
22.634
22.154
25.637
29.663
30.219
+0.339
+0.381
+0.308
+0.555
+0.592
+0.389
+0.226
-0.010
-0.102
+0.386
+0.185
+0.524
+0.284
Pixel-GS*
25.733
32.649
29.227
31.795
31.783
27.182
27.820
21.885
22.572
21.985
25,438
28.130
29.708
w/ Ours
25,982
32.746
29.425
32.042
32.204
27.392
27.959
21.867
22.516
22.291
25.604
29,488
29.931
+0.249
+0.097
+0.198
+0.247
+0.421
+0.210
+0.139
-0.018
-0.056
+0.306
+0.166
+1.358
+0.223
22

---

Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting
Table 25. Per scene quantitative result of the proposed method is based on different models on Mip-NeRF 360, Tanks & Temples, and Deep Blending in terms of SSIM
SSIM
Methad
Bicycle
Bonsai Counter Kitchen Room
Stump
Garden Flowers Treehill
Train
Truck
Drjohnson Playroom
3DGS*
0.778
0.948
0.916
0.933
0.927
0.784
0.874
0.621
0.651
0.810
0.879
0.898
0.902
w/ Ours
0,805
0.953
0,922
0.936
0.936
0.807
0.877
0.654
0.657
0.826
0.888
0.905
0,908
+0.027
+0.005
+0.006
+0.003
+0.009
+0.023
+0.003
+0.033
+0.006
+0.016
+0.009
+0.007
+0.006
Pixel-GS*
0.792
0,951
0,920
0.936
0.930
0.797
0.878
0.652
0,652
0.823
0.883
0.886
0,900
w/ Ours
0.809
0.953
0.922
0.936
0.936
0.812
0.880
0.663
0.657
0.832
0.885
0.901
0.900
+0.017
+0.002
+0.002
+0.000
+0.006
+0.015
+0.002
+0.011
+0.005
+0.009
+0.002
+0.015
+0.000
Table 26. Per scene quantitative result of the proposed method is based on different models on Mip-NeRF 360, Tanks & Temples, and Deep Blending in terms of LPIPS..
IPIPS
Method
Bicycle Bonsai
Counter Kitchen Room Stump Garden Flowers
Trechill
Train
Truck
Drjohnson
Playroom
3DGS*
0.205
0.173
0.178
0.113
0.191
0,208
0.103
0.329
0.319
0.209
0.147
0.247
0.246
w/ Ours
0.165
0.151
0.157
0.108
0.168
0.175
0.098
0.257
0.273
0.184
0.117
0.230
0.231
A
-0.040
-0.022
-0.021
-0.005
-0.023
-0.033
-0.005
-0.072
-0.046
-0.025
-0.030
-0.017
-0.015
Pixel-GS*
0.174
0.161
0.162
0.107
0.184
0.181
0.094
0.253
0.269
0.182
0.121
0.256
0.243
w/ Ours
0.158
0.149
0.153
0.106
0.167
0.169
0.092
0.240
0.265
0.171
0.113
0.233
0.233
A
-0.016
-0.012
-0.009
-0.001
-0.017
-0.012
-0.002
-0.013
-0.004
-0.011
-0.008
-0.023
-0.010
Table 27. Per scene quantitative result of the proposed method is based on different models on Mip-NeRF 360, Tanks & Temples, and Deep Blending in terms of the number of Gaussian primitives(#G)).
#G
Method
Bicycle
Bonsai Counter Kitchen
Room
Stump
Garden Flowers
Trechill
Train
Truck
Drjohnson Playroom
3DGS*
5.78M
1.25M
1.17M
1.75M
1.49M
4.73M
5.07M
3.38M
3.62M
1.08M
2.58M
3.28M
2.33M
w/ Ours
3.89M
1.58M
1,49M
1.63M
1.74M
3,81M
3.03M
3.55M
3,48M
1.39M
2.05M
3.43M
2.29M
A
1.89M
+0.33M
+0.32M
-0.12M
+0.25M
-0.92M
-2.04M
+0.17M
-0.14M
+0.31M
-0.53M
+0.15M
-0.04M
*ixel-GS*
8.46M
2.07M
2.50M
3.03M
2.49M
6.46M
7.55M
7.08M
7,47M
3.80M
5.18M
5.51M
3.76M
w/ Ours
4.47M
1.99M
2.06M
2,07M
2.19M
4.36M
3.98M
4.69M
4.53M
2.74M
3.18M
4.34M
2.84M
-3,99M
-0.08M
-0.44M
-0.96M
-0330M
-2.10M
-3.57M
-2.39M
-2.94M
-1,06M
-2.00M
-1.17M
-0,22M
Table 28, Per seene guantitative result of the proposed method is based on different models on Mip-NeRF 360, Tanks & Temples, and Deep Blending in terms of rendering (FPS)
FPS1
Method
Bicycle Bonsai Counter Kitchen Room Stump Garden Flowers Trechill Train Truck Drjohnson Playroom
*
100
310
244
195
235
151
122
200
176
285
208
160
228
w/ Ours
162
206
155
154
173
156
181
151
154
210
225
143
213
A
+62
-104
-89
-41
-62
+5
+59
-49
-22
-75
+17
-17
-15
Pixel-GS*
59
184
122
113
141
95
76
71
81
111
91
89
138
w/ Ours
129
158
116
121
130
133
128
109
120
138
149
106
163
A
+70
-26
+8
-11
+38
+52
+38
+39
+27
+58
+17
+25
23

---

Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting
Table 29, Per scene quantitative result proposed method is hased on different models on Mip-NeRF 360, Tanks & Temples, and
Deep Blending in terms of the balance between quality and efficiency(QEB).
QEB
Method
Bicycle
Bonsai Counter
Kitchen
Room
Stump Garden Flowers Trechill
Train
Truck
Drjohnson Playroom
3DGS
1.185
0.070
0.085
0.101
0.121
0.652
0.428
0,556
0.656
0.079
0.182
0,506
0.251
w/ Ours
0.396
0.116
0.151
0.114
0.169
0.427
0.164
0.604
0.617
0.122
0.107
0.552
0.248
A
-0.789
+0.046
+0.066
+0.013
+0.048
-0.225
-0.264
+0.048
-0.039
+0.043
-0.075
+0.046
-0.003
Pixel-GS*
2.481
0.182
0.332
0.284
0.323
1.231
0.934
2.503
2,481
0.616
0.694
1.585
0.662
w/ Ours
0.547
0.188
0.272
0.181
0.281
0.554
0.286
1.033
1.000
0.340
0.241
0.954
0.406
A
-1.934
+0.006
-0.060
-0.103
-0.042
-0.677
-0.648
-1,470
-1,481
-0.276
-0.453
-0.631
-0.256
Table 30. Per scene quantitative result of the proposed method based on different models on BungeeNeRF of PSNR1. Metries are averaged across the scenes.
PSNR1
Method
Amsterdam
Barcelona
Bilbao
Chicago
Hollywood
Pompidou
Quebec
Rome
3DGS*
27.600
27.379
28.800
27.972
26.150
26.997
28.711
27.535
Ours
27.887
27.546
29.081
28.390
27.180
29.013
27.647
+0.287
+0.167
+0,281
+0.418
-0.004
+0.183
+0.302
+0.112
Pixel-GS*
27.756
26.780
28.582
27.524
26,121
OOM
28.245
26.583
w/ Ours
27.975
27.136
28.938
28.362
25.997
27.010
28.672
26.986
A
+0.219
+0.356
+0.356
+0.838
-0.124
+0.427
+0.403
Table 31. Per scene quantitative result of the proposed method is based different models on BungeeNeRF in terms of SSIM
SSIM
Method
Amsterdam
Barcelona
Bilbao
Chicago
Hollywood
Pompidou
Quebec
Rome
3DGS*
0.913
0.915
0.915
0.927
0.868
0.916
0,931
0.914
w/ Ours
0.922
0.919
0.922
0.933
0,868
0.922
0.938
0.918
A
+0.009
+0.004
+0.007
+0.006
+0.000
+0.006
+0.007
+0.004
PixeI-GS*
0.916
0.904
0.912
0.921
0.866
OOM
0.924
0.896
w/ Ours
0.922
0.912
0.920
0.930
0.863
0.918
0.932
0.905
A
+0.006
+0.008
+0.008
+0.009
-0.003
+0.008
+0.009
Table 32, Per quantitative result of the proposed method is hased different models BungeeNeRFF in terms of I.PPSS].
LPIPS
Method
Amsterdam
Barcelona
Bilbao
Chicago
Hollywood
Pompidou
Quebec
Rome
3DGS*
0.100
0.087
0.099
0.086
0.134
0.095
0.094
0.102
w/ Ours
0.087
0.084
0.092
0.081
0.140
0.089
0.087
0.098
A
-0.013
-0.003
-0.007
-0.005
+0.006
-0.006
-0.007
-0.004
Pixel-GS*
0.095
0.101
0.103
0.090
0.138
OOM
0.106
0.129
w/ Ours
0.085
0.093
0.094
0.081
0.144
0.092
0.094
0.117
A
-0.010
-0.008
-0.009
-0.009
+0.006
-0.012
-0.012
24

---

Perceptual-GS: Scene-adaptive Perceptual Densification for Gaussian Splatting
Table 33, Per quantitative result of the proposed method is based different models BungeeNeRF in terms of the number of Gaussian primitivex(#t)).
#G
Method
Amsterdam
Barcelona
Bilbao
Chicago
Hollywood
Pompidou
Quchec
Rome
3DGS*
6.19M
8.46M
5.51M
6.32M
7.04M
9.11M
6.01M
6.74M
w/ Ours
4.96M
5.97M
3.76M
4.48M
4.88M
6.12M
4.57M
5.03M
A
- -1.23M
-2.49M
-1.75M
-1.84M
-2.16M
-2.99M
-1,44M
-1.71M
Pixel-GS
10.26M
11.04M
7.98M
9.76M
9.88M
OOM
8.37M
8.08M
w/ Ours
6.60M
6.64M
4.57M
5,58M
5.85M
7.39M
5.46M
5.27M
A
-3.66M
-4,40M
-3.41M
-4.18M
-403M
-2,91M
2.81M
Table 34. Per scene quantitative result of the proposed method is based on different models BungeeNeRF in terms of rendering SP
FPSt
Method
Amsterdam
Barcelona
Bilbao
Chicago
Hollywood
Pompidou
Quebec
Rome
3DGS*
70
63
72
67
72
57
76
71
w/ Ours
85
83
88
92
97
78
98
87
A
+15
+20
+16
+25
+25
+21
+22
+16
Pixel-GS*
42
48
54
43
52
OOM
57
62
Ours
76
74
82
64
83
+23
+27
+22
+31
+30
+26
+26
Table 35. Per scene quantitative result of the proposed method is based on different models on BungeeNeRF in terms of the balance hetween quality and efficiency(QEB)).
QEB
Method
Amsterdam
Barcelona
Bilbao
Chicago
Hollywood
Pompidou
Quebec
Rome
3DGS*
0.884
1.168
0.758
0.811
1.310
1.518
0.743
0.968
w/ Ours
0.508
0.604
0.393
0.394
0.704
0.698
0.406
0.567
A
-0.376
-0.564
-0.365
-0.417
-0.606
-0.820
-0.337
-0.401
Pixel-GS*
2.321
2.323
1.522
2.043
2.622
OOM
1.557
1.681
w/ Ours
0.863
0.823
0.565
0.611
1.027
1.062
0.618
0.701
A
-1.458
-1.500
-0.957
-1.432
-1.595
-0.939
-0,980
Table 36. Per scene quantitative result proposed method based on CoR-GS on 24-view Mip-NeRF terms PSNR
Metries averaged across the scenes.
PSNR1
Method
Bicycle
Bonsai
Counter
Kitchen
Room
Stump
Garden
C
20.496
24,905
23.331
21.973
25.376
19.819
19.941
w/ Ours
19.757
25.159
23.557
23.405
25.453
18,996
20.598
A
-0.739
+0.254
+0.226
+1.432
+0.077
-0.823
+0.657
25

---

Perceptual-GS: Scenc-adaptive Perceptual Densification for Gaussian Splatting
Table 37, Per scene quantitative result of the proposed method is based on CoR-GS on 24-vicw Mip-NeRF 360 in terms of SSIM
SSIM
Method
Bicycle
Bonsai
Counter
Kitchen
Room
Stump
Garden
CoR-GS*
0.479
0.834
0.791
0.836
0.858
0.407
0.440
w/ Ours
0.465
0.847
0.797
0.855
0.854
0.399
0.549
A
-0.014
+0.013
+0.006
+0.019
-0.004
-0.008
+0.109
Table 38, Per scene quantitative result of the proposed method is hased on CoR-GS on 24-view Mip-NeRF 360 terms of LPIPS!.
LPIPS
Method
Bicycle
Bonsai
Counter
Kitchen
Room
Stump
Garden
CoR-GS
0.491
0.212
0.213
0.158
0.177
0.605
0.529
w/ Ours
0.406
0.171
0.190
0.147
0.175
0.509
0.368
A
-0.085
-0.041
-0.023
-0.011
-0.002
-0.096
-0.161
26


Reviews:
[{"review_id": "UJcSgdIMh1", "reviewer": "Reviewer_9bnu", "summary": "Perceptual-GS addresses a core limitation of 3D Gaussian Splatting (3DGS) for novel view synthesis by adaptively distributing Gaussian primitives based on human perceptual sensitivity. Traditional 3DGS methods suffer from either insufficient coverage in visually important areas or over-densification in simpler regions. Perceptual-GS tackles this by first modeling perceptual sensitivity from multi-view images, emphasizing scene details that the human eye is most sensitive to. It employs a dual-branch rendering framework, fusing both RGB and sensitivity signals to adaptively refine and distribute Gaussians, thereby boosting reconstruction quality while constraining the total number of primitives. Additionally, a scene-adaptive depth reinitialization mechanism further improves quality in regions with sparse initial geometry.", "questions": "", "limitations": "", "rating": 4, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the thoughtful response to our paper. We provide additional visualizations at https://akfwb.github.io/Perceptual-GS-Rebuttal/ and address specific points below:\n\n**Q1. Concerns about the Generalizability**\n\nThank you for the valuable suggestion. To further demonstrate the generalizability of our approach, especially for sparse-view 3D Gaussian Splatting methods, we simply integrate our perceptual sensitivity-guided densification strategy with CoR-GS [r1] to validate its adaptability under sparse-view settings. Since the paper only provides code without the initial point cloud used for training, we followed the provided instructions to construct a training dataset containing only 24 views. We then trained both the CoR-GS and the version enhanced with our method under the same settings.\n\n|Method|PSNR|SSIM|LPIPS|\n|-|-|-|-|\n|CoR-GS|22.26|0.664|0.341|\n|w/ Ours|22.42|0.681|0.281|\n\nAs shown in the table, our method achieves notable improvements even under sparse-view settings, particularly in the perceptual metric LPIPS. These results further validate the versatility and effectiveness of our approach. Qualitative comparisons are available in the link we provided.\n\n[r1] Zhang, Jiawei, et al. \"CoR-GS: Sparse-view 3D Gaussian Splatting via Co-regularization.\" European Conference on Computer Vision, 2024.\n\n**Q2. Limited Visualization and Small Improvements**\n\nDue to space limitations in the main text, we have included additional visual results in the Appendix. Compared to other related methods, our approach achieves better reconstruction quality, particularly in challenging regions, **effectively reducing issues such as excessive blur and visual artifacts**. More qualitative comparisons are available in the provided link. Following your suggestion, we will incorporate more visual results in our revision.\n\n**Q3. Concerns about the Compromised Quality in Non-sensitive Areas**\n\nSince our method primarily targets perceptually sensitive regions, we do not include comparisons on non-sensitive areas in the experiments. To verify that our approach does not compromise the quality of these regions, we apply the sensitivity maps to mask out sensitive areas in the final renderings on the MipNeRF 360 dataset and evaluate reconstruction quality on the remaining non-sensitive regions. The results confirm that our method **introduces no degradation** in these areas. Additional visualizations are available in the provided link.\n\n|  Method  |  PSNR  |  SSIM  |  LPIPS  |\n|----------|--------|--------|---------|\n|   3DGS   |  40.18 |  0.990 | 0.014   |\n|   Ours   |  40.72 |  0.991 | 0.014   |\n\nBased on your suggestion, we will include the experiment in our revision.\n\n**Q4. Visualization of the Sensitivity Map**\n\nFigures 2 and 3 both visualize the perceptual sensitivity maps. Fig. 2 presents the full image, while Fig. 3 displays a cropped region to better highlight the variations in sensitivity across different areas. Compared to the baseline, our method distributes more Gaussian primitives in perceptually complex regions such as textured grass areas guided by the sensitivity map, resulting in images with higher visual quality as perceived by the human eye. This also helps to reduce noticeable blur and artifacts in the rendered results. Additional sensitivity map visualizations are available in the link we provided. Based on your suggestion, we will include additional visualizations of the sensitivity maps for further illustration in our revision.\n\n**Q5. The Cropped Region Obscures the Full Image Too Much**\n\nThank you for your suggestion. We will revise the cropped region in the visualization accordingly in our revision.", "claims_and_evidence": "Perceptual-GS claims it can be effectively integrated with other 3DGS-based methods, while all of the experiments presented—including those in the supplementary materials—only include the proposed Perceptual module combined with 3DGS and Pixel-GS. It will be helpful if can show additional experiments to incorporate with other GS methods, which can prove its generalization, especially for sparse view GS methods, where the Gaussian distribution problem tends to be more pronounced.\n\nWhile the paper provides a thorough quantitative evaluation, the limited visualizations shown appear to indicate only small improvements. Also as the paper claimed that the proposed method prioritizing the densification of Gaussian primitives in high-sensitivity regions to human perception and constraining their generation in low-sensitivity areas, it remains unclear whether this approach might compromise clarity or reduce quality in other parts of the scene. Therefore, it would be very helpful to include additional visualizations—either in the main paper or the supplementary material—to more clearly illustrate the advantages and potential trade-offs.\n\nPerceptual Sensitivity map is unclear should looks like Fig 2 Sensitivity GT or should looks like Fig3 Perception-oriented Smoothing results, but both seems not very helpful for grass and ground as shown in Fig5.", "methods_and_evaluation_criteria": "Proposed method is make sense for the problem.", "theoretical_claims": "The paper not contain theoretical claim except some describtion about 3D Gaussian Splatting and Perceptual Sensitivity, which is correct.", "experimental_designs_or_analyses": "Please refer Claims And Evidence.", "relation_to_broader_scientific_literature": "Unknown", "essential_references_not_discussed": "No", "other_strengths_and_weaknesses": "The cropped region obscures the full image too much.", "other_comments_or_suggestions": "No", "questions_for_authors": "Please refer Claims And Evidence.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "9eEnh2gDIB", "reviewer": "Reviewer_kxHE", "summary": "The paper introduces Perceptual-GS, a method to improve 3D Gaussian Splatting (3DGS) for novel view synthesis by integrating a perceptual-sensitivity mechanism during training. Concretely, the authors compute gradient-based sensitivity maps to model human perception of local structures, then employ a dual-branch rendering pipeline—one for RGB and one for “sensitivity”—to guide Gaussian densification. This aims to focus resources where the human eye would notice errors, thus achieving higher quality with fewer primitives. Further refinements (like scene-adaptive depth re-initialization) bolster performance in large-scale scenes. Experiments on Mip-NeRF 360, Tanks & Temples, Deep Blending, and BungeeNeRF show improvements in both quality (PSNR/SSIM/LPIPS) and efficiency (#Gaussians, FPS) over baselines.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the thoughtful response to our paper. We provide additional visualizations at https://akfwb.github.io/Perceptual-GS-Rebuttal/ and address specific points below:\n\n**Q1. Concerns about Engineering-driven Incremental Improvements**\n\nTo the best of our knowledge, our work is the first to introduce **explicit human perceptual modeling** into the field of 3D reconstruction, aiming to generate perceptually aligned 3D scenes. The proposed method is simple yet effective, with a clear motivation. Comparing with existing approaches, it shows less blurriness and artifacts, significantly improves perceptual quality. More visualizations can be found in our provided link. Besides, our method uses **less than 60%** of the parameters while **surpassing** the reconstruction quality of other quality-focused approaches, resulting in a significant improvement in overall efficiency. Moreover, our method is **general** and can be seamlessly integrated with various 3DGS-based approaches—such as Pixel-GS and CoR-GS—to further enhance reconstruction quality even under sparse-view settings. The quantitative results are as follows:\n\n|Method|PSNR|SSIM|LPIPS|\n|-|-|-|-|\n|Pixel-GS|27.85|0.834|0.176|\n|w/ Ours|28.01|0.841|0.167|\n\n|Method|PSNR|SSIM|LPIPS|\n|-|-|-|-|\n|CoR-GS|22.26|0.664|0.341|\n|w/ Ours|22.42|0.681|0.281|\n\n**Q2. Concerns about Novelty**\n\nWhile previous works on adaptive sampling, foveated rendering and NeRF variants with perceptual losses are indeed related to the general concept of human perception, Perceptual-GS is the first to **explicitly model the human visual perception** within the 3D reconstruction process. Our method jointly incorporates geometric, color, and perceptual sensitivity models to represent the scene, leading to reconstructions that better align with visual perception. Following your suggestion, we will cite and include a more detailed discussion of these related works in the revision to better highlight our novelty.\n\n**Q3. Connections to Prior Perception-Related Works**\n\nThe concept of visual perception has been extensively studied in the field of image processing. However, in the domain of 3D reconstruction, although some prior works have explored perceptual concepts, they often rely on perceptual loss **without explicitly modeling** the properties of the HVS. Others employ foveated rendering to improve efficiency, but fail to achieve an overall improvement in quality, as the references *Lin et al., 2024* and *Franke et al., 2024* cited in the Introduction of our paper. In contrast, our approach introduces an **explicit perceptual representation** and models multiple key characteristics of the HVS:\n* The HVS is **highly sensitive to regions with rich local structures** [r1]. To capture this characteristic, we employ the Sobel operator to extract gradients, which serve as an effective representation of HVS sensitivity across different regions of the image.\n* Due to **the thresholding nature** of human perception characterized by the Just Noticeable Difference [r2], we enhance the gradient magnitude maps through Perception-oriented Enhancement, effectively suppress the misleading influence of absolute gradient values.\n* **Eye-tracking studies** have shown that adjacent gaze points are often merged into a single fixation, typically representing a region with remarkable difference relative to its surroundings [r3]. To simulate this, we apply Perception-oriented Smoothing to generate a final binary perceptual sensitivity map that is both aligned with human perception and easy to learn.\n\nBased on your suggestion, we will emphasize the connection between the proposed method and key properties of the HVS in our revision.\n\n[r1] Xue, Wufeng, et al. \"Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index.\" IEEE Transactions on Image Processing 23.2 (2013): 684-695.\n\n[r2] Lubin, Jeffrey. \"A Human Vision System Model for Objective Picture Quality Measurements.\" 1997 International Broadcasting Convention IBS 97. IET, 1997.\n\n[r3] Gu, Ke, et al. \"Saliency-guided Quality Assessment of Screen Content Images.\" IEEE Transactions on Multimedia 18.6 (2016): 1098-1110.\n\n**Q4. Comparison with More SOTAs**\n\nAs our method targets a different problem, we do not compare it with efficiency-focused Scaffold-GS or quality-focused Mip-Splatting in the paper. Based on your suggestion, we now include comparisons with them. Since Scaffold-GS does not explicitly store all primitives, its #G metric is omitted.\n\n|Method|PSNR|SSIM|LPIPS|FPS|#G|\n|-|-|-|-|-|-|\n|Scaffold-GS|27.99|0.825|0.207|476|-|\n|Mip-Splatting|27.79|0.827|0.203|125|3.97M|\n|Ours|28.01|0.839|0.172|166|2.69M|\n\n**Q5. Concerns about the Thresholds and Hyperparameters**\n\nDue to space constraints, we refer the reviewer to our response to reviewer aPkn's Q3., which addresses the same issue in detail.\n\n**Q6. Issues about the Supplementary Material**\n\nWe have provided detailed Appendix after the References.", "claims_and_evidence": "Claim: The approach outperforms prior 3DGS variants in both perceptual quality and efficiency.\n\nEvidence: The authors provide side-by-side comparisons showing modest gains in PSNR/SSIM and reduced LPIPS, often with fewer splats. They also include ablations that remove modules (e.g., perceptual densification) to demonstrate each part’s impact.\n\nConcern: While the reported improvements are consistent, they remain incremental over existing 3DGS-based approaches. Gains in absolute image quality are often relatively small, underscoring the primarily engineering nature. Note that some state-of-the-art methods,eg, mip-splatting, scaffold-gs are not included for comparison.", "methods_and_evaluation_criteria": "The method is thoroughly tied to 3DGS: it modifies the distribution of Gaussians via a perceptual branch.\nEvaluation focuses on standard reconstruction metrics (PSNR, SSIM, LPIPS) plus model size (number of Gaussians) and render speed. \nOverall, the methodology is competent for a practical 3D rendering system. However, it offers no major departure in algorithmic or theoretical frameworks beyond applying known perceptual cues to 3DGS densification.", "theoretical_claims": "The paper provides no formal theory—it relies on empirical heuristics for densification. No new mathematical results; the approach is closer to a system-level enhancement than a theoretical innovation.", "experimental_designs_or_analyses": "Experiments are comprehensive and well-documented across multiple datasets, including large-scale scenes where some baselines fail. The improvements generally validate the approach but reinforce that this is an incremental engineering improvement. Although, as mentioned above,  some state-of-the-art methods,eg, mip-splatting, scaffold-gs are not included for comparison.", "relation_to_broader_scientific_literature": "The paper is heavily rooted in 3DGS developments, citing many concurrent methods that tackle densification.\nWhile it references fundamental perceptual concepts (e.g. gradient magnitude, SSIM), it does not connect deeply with prior research on user studies, advanced HVS modeling, or more general ML frameworks.\nThe method’s incremental nature aligns it more with specialized vision/graphics venues.", "essential_references_not_discussed": "Some older works on adaptive sampling or foveated rendering in computer graphics, as well as NeRF variants that use perceptual losses, might be relevant. Without them, the novelty claim is overstated—adaptive, perceptual-driven resource allocation is well-explored historically.", "other_strengths_and_weaknesses": "**Strengths**:\n\n\t1. The paper offers a clear quality-efficiency trade-off improvement.\n\n\t2. The paper has relatively thorough experiments, solid ablations, and stable results in large scenes.\n\n\t3. Potentially easy to integrate with other 3DGS-based pipelines.\n\n**Weaknesses**:\n\n\t1. Limited novelty: mostly extends known densification heuristics with gradient-based sensitivity.\n\n\t2. Engineering-heavy with multiple thresholds and hyperparameters and does not propose a general ML contribution.\n\n        3. Some state-of-the-art methods,eg, mip-splatting, scaffold-gs are not included for comparison.", "other_comments_or_suggestions": "The paper may find a better fit at a vision/graphics venue, where incremental improvements in rendering quality/efficiency are more broadly appreciated.", "questions_for_authors": "Please refers to the Strengths And Weaknesses part.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "xBI3JZqR3O", "reviewer": "Reviewer_wQ47", "summary": "This paper proposes a 3D Gaussian Splatting (3DGS) optimization method that leverages additional perceptual sensitivity. By incorporating visual sensitivity, specifically edge response, the approach enables more fine-grained optimization of Gaussian representations. The perceptual sensitivity-adaptive densification strategy includes handling perceptually poor regions and scene-adaptive depth reinitialization. The experiments follow the existing pipeline.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the thoughtful response to our paper. We provide additional visualizations at https://akfwb.github.io/Perceptual-GS-Rebuttal/ and address specific points below:\n\n**Q1. Discussion of 3DGS-MCMC**\n\nEq. 9 in 3DGS-MCMC aims to **maintain** the opacity of a spatial region before and after cloning. However, as noted in our Appendix, cloned Gaussians are often poorly optimized. To avoid redundancy and mitigate negative effects during training, we apply **opacity decline** instead of preserving the original opacity as in 3DGS-MCMC. We will cite and discuss 3DGS-MCMC in the revision as suggested.\n\n**Q2. Claims about the Novelty**\n\nTo the best of our knowledge, Perceptual-GS is the first approach to incorporate **explicit modeling of human visual perception** into 3D reconstruction, in addition to modeling color and geometry. While it shares certain similarities with prior works such as Taming-3DGS and Mini-Splatting, our method is fundamentally centered around **perceptual sensitivity**, leading to significant different design in both motivation and implementation.\n\nIn Taming-3DGS, complex edges are extracted using a Laplacian filter, and edge complexity is determined by aggregating pixel response values over the projected areas of Gaussian primitives from multiple views. In contrast, our method derives a perceptually representative **sensitivity map**, **learns to** project 2D perceptual cues onto 3D primitives, and uses them to guide densification. Ablation studies (w/o PE) further demonstrate that using a binarized perceptual sensitivity map is more effective in identifying regions that require densification compared to traditional edge detection methods.\n\n|Method|PSNR|SSIM|LPIPS|#G|\n|-|-|-|-|-|\n|3DGS|27.71|0.826|0.202|3.14M|\n|w/o PE|27.74|0.825|0.204|2.09M|\n|Ours|28.01|0.839|0.172|2.69M|\n\nIn Mini-Splatting, depth reinitialization is uniformly applied across all scenes. However, we observe that this strategy may have drawbacks in scenes that are already well reconstructed. To address this, we adaptively decide whether to apply it **based on the sensitivity learning process**. Notably, ablation results (w/o SDR) demonstrate that our method can still achieve significant improvements in reconstruction quality even without adaptive depth reinitialization.\n\n|Method|PSNR|SSIM|LPIPS|#G|\n|-|-|-|-|-|\n|3DGS|27.71|0.826|0.202|3.14M|\n|w/o SDR|27.93|0.832|0.176|2.68M|\n|Ours|28.01|0.839|0.172|2.69M|\n\n**Q3. The Effectiveness of QEB**\n\nYour concern is valid, as the QEB metric used in this paper primarily emphasizes efficiency. However, since both our method and the compared baselines aim to **improve the performance of 3DGS**, and given that the gains in #G and FPS are not as significant as those achieved by purely lightweight approaches, we believe this metric remains reasonable in this context. In addition to QEB, we also report #G, FPS, and LPIPS in the paper to provide a more comprehensive evaluation that accounts for both rendering efficiency and perceptual quality.\n\n**Q4. Differences Across Regions with Varying Perceptual Sensitivity**\n\nTo verify that our approach does not compromise the quality of these regions, we apply the sensitivity maps to mask out sensitive areas in the final renderings on the MipNeRF 360 dataset and evaluate reconstruction quality on the remaining non-sensitive regions. The results confirm that our method **introduces no degradation** in these areas. Additional visualizations are available in the provided link.\n\n|  Method  |  PSNR  |  SSIM  |  LPIPS  |\n|----------|--------|--------|---------|\n|   3DGS   |  40.18 |  0.990 | 0.014   |\n|   Ours   |  40.72 |  0.991 | 0.014   |\n\nBased on your suggestion, we will include the experiment in our revision and provide additional visualizations to better illustrate how the perceptual sensitivity map guides the distribution of Gaussian primitives in different regions, making the underlying mechanism more interpretable.\n\n**Q5. The Effect of Sensitivity Map**\n\nDirectly relying on the absolute response values of gradient maps derived from the Sobel operator can be misleading. Therefore, in our method, we enhance these maps by **simulating multiple characteristics of the human visual system (HVS)**, leading to significant quality improvements, as demonstrated by the results of the w/o PE ablation study presented in Q2.\n\nThe sensitivity map reflects how the HVS perceives different regions. As shown in Fig. 3, smooth sky and complex grass areas exhibit distinct values, leading to different densification strategies. In Fig. 2, most pixel values are 1, indicating a high density of perceptually sensitive regions. Our method distributes more primitives in these areas, including those that **the original 3DGS fails to densify**, resulting in clear differences. More visual examples can be found at the provided link.", "claims_and_evidence": "This paper argues that incorporating perceptual sensitivity into 3DGS can lead to more effective optimization, enabling higher-quality rendering with a limited number of Gaussians. The approach is motivated by the observation that the adaptive density control in conventional 3DGS struggles to perform densification effectively in regions with complex geometry. To address this issue, perceptual sensitivity is explicitly optimized and used to guide the densification process. An ablation study demonstrates that removing the proposed perceptual densification results in a performance drop of approximately 0.3 dB in PSNR.", "methods_and_evaluation_criteria": "This paper introduce four major modules.\n\n1. **Perceptual Sensitivity Extraction**.\nA binary map is generated using edge detection and smoothing. Thresholding is performed with a Sobel filter to extract perceptual sensitivity.\n\n2. **Dual-Branch Rendering**. \nA sensitivity parameter is introduced for each Gaussian. In simple regions, the number of Gaussians is constrained to improve efficiency. To account for multi-view consistency, the sensitivity term is learned, and during rendering, the sensitivity map is estimated through binary classification.\n\n3. **Perceptual Sensitivity-Guided Densification**.\nDensification is adapted based on perceptual sensitivity. The learned sensitivity values are used to determine gradient thresholding levels, ensuring more refined optimization.\n\n4. **Scene-Adaptive Depth Reinitialization**.\nGaussian distributions are adjusted to improve rendering quality. Large Gaussians and those with high sensitivity undergo depth reinitialization using a mini-splatting approach.\n\nThe method is evaluated using rendering metrics such as PSNR, SSIM, and LPIPS, as well as the quality-efficiency balance (QEB) metric to assess trade-offs between rendering quality and computational efficiency.", "theoretical_claims": "No theoretical claims are made.", "experimental_designs_or_analyses": "Experiments are conducted on the Mip-NeRF 360, Deep Blending, Tanks & Temples, and BungeeNeRF datasets. The proposed method is compared with approaches that optimize 3D Gaussian Splatting under limited computational resources.", "relation_to_broader_scientific_literature": "This paper introduces a method that adjusts gradient thresholding based on perceptual sensitivity to modify the optimization of Gaussians. It is related to approaches for optimizing Gaussians under limited computational resources and, more broadly, to methods for compressing the number of Gaussians.", "essential_references_not_discussed": "The opacity decline resembles the cloning function in Eq. 9 of 3DGS-MCMC. A comparison and discussion on this similarity are necessary.\n\n[1] Kheradmand S., et al., \"3D Gaussian Splatting as Markov Chain Monte Carlo\", NeurIPS, 2024.", "other_strengths_and_weaknesses": "There are concerns about novelty since parts of the proposed method overlap with existing approaches. The perceptual sensitivity extraction modifies the score function measure from Taming-3DGS and applies it to thresholding, while depth reinitialization follows the approach used in Mini-Splatting, making the method appear more like an engineered combination of existing techniques.\n\nQEB does not seem to be an intuitive metric. While it aims to represent an overall trade-off, it produces unfavorable conclusions for models like EAGLES[2] and Efficient GS[3], where the number of Gaussians is significantly reduced compared to LPIPS. These models, characterized by a very low number of Gaussians and high FPS, yield a QEB score as low as 0.01, which fails to effectively capture the overall performance trade-off.\n\n[2] Girish S., et al., \"EAGLES: Efficient Accelerated 3D Gaussians with Lightweight EncodingS\", ECCV, 2024.\n\n[3] Lee, J. C., et al., \"Compact 3D Gaussian Representation for Radiance Field\", CVPR, 2024.\n\nThis paper introduces an adaptive densification strategy based on perceptual complexity, but it lacks a detailed analysis of the associated trade-offs. It is unclear how the distribution of Gaussians differs between texture-rich and the other areas, or how performance loss in regions where splitting does not occur effectively compares to the performance gain in perceptually complex areas.", "other_comments_or_suggestions": "No additional comments.", "questions_for_authors": "Perceptual sensitivity ultimately relies on the Sobel filter, raising questions about the actual performance gain in highly complex regions such as grass. Additionally, in Figure 2, the entire image appears masked, making it unclear how the proposed method behaves differently from standard 3DGS in such cases.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "xNME1ZbqJm", "reviewer": "Reviewer_aPkn", "summary": "This paper focuses on improving the perceptual quality of 3DGS by 1) extracting edges to represent perceptual sensitivity and embedding it into the primitives for supervision; 2) introducing additional densification strategy based on the sensitivity. Experiments show that the proposed method achieves SOTA performance on multiple standard datasets while retaining efficiency.", "questions": "", "limitations": "", "rating": 3, "confidence": null, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "We thank the reviewer for the thoughtful response to our paper. We provide additional visualizations at https://akfwb.github.io/Perceptual-GS-Rebuttal/ and address specific points below:\n\n**Q1. Interpretable Visualizations**\n\nThank you for your suggestion. Since our method is designed to optimize rendering quality rather than geometric structure, we do not include depth or normal maps in the original paper. However, based on your valuable feedback, we have provided additional visualizations in the supplementary link, including depth maps, primitive distributions, and rendered perceptual sensitivity maps. These visualizations will also be included in our revision.\n\n**Q2. Discussion about Works with Edge Prior**\n\nAlthough our method uses the Sobel operator, its goal is to identify perceptually sensitive regions, rather than extracting prominent edges. Therefore, we do not discuss 3DGS methods that rely on edge priors in the paper. Based on your suggestion, we will discuss and cite these works in the revision.\n\n**Q3. Concerns about the Thresholds and Hyperparameters**\n\nOur method does involve the use of certain thresholds and hyperparameters, which is a **common practice** among 3DGS-based approaches. Parameters such as loss weights and the interval of specific operations are essential for the proper execution of the method. However, unlike some prior works that require scene-specific manual tuning, our method adopts a **uniform set of hyperparameters across all experiments**, regardless of the dataset or the baseline it is combined with. This demonstrates the **generalizability and robustness** of our approach.\n\nIn addition, some thresholds, such as the high-sensitivity scene threshold and the  scenes with sparse initial point cloud threshold, are determined based on **statistical analysis**. Due to space limitations, these implementation details are not included in the main paper, but they are available in the provided link. Based on your suggestion, we will include these details in the revision.\n\nAblation studies and corresponding explanations, included in the Appendix, further demonstrate that the performance of our model is **not highly sensitive** to the specific values of these hyperparameters, indicating a reasonable degree of robustness. In the following tables, the selected values of each hyperparameter are highlighted in bold.\n\n|$\\lambda_S$|PSNR|SSIM|LPIPS|#G|\n|-|-|-|-|-|\n|**0.1**|28.01|0.839|0.172|2.69M|\n|0.3|27.82|0.835|0.181|2.10M|\n|0.5|27.48|0.823|0.196|1.92M|\n\n|$\\tau^{\\omega}_h$|PSNR|SSIM|LPIPS|#G|\n|-|-|-|-|-|\n|10|28.05|0.841|0.166|3.61M|\n|15|28.00|0.840|0.169|3.09M|\n|**25**|28.01|0.839|0.172|2.69M|\n\n|$\\tau^{\\omega}_m$|PSNR|SSIM|LPIPS|#G|\n|-|-|-|-|-|\n|**10**|28.01|0.839|0.172|2.69M|\n|15|27.98|0.838|0.173|2.65M|\n|25|27.97|0.838|0.174|2.63M|\n\n|$Iter_h$|PSNR|SSIM|LPIPS|#G|\n|-|-|-|-|-|\n|**1000**|28.01|0.839|0.172|2.69M|\n|1500|27.95|0.838|0.174|2.57M|\n|2000|27.93|0.837|0.175|2.52M|\n\n|$Iter_m$|PSNR|SSIM|LPIPS|#G|\n|-|-|-|-|-|\n|1000|27.92|0.839|0.172|2.70M|\n|**1500**|28.01|0.839|0.172|2.69M|\n|2000|27.98|0.839|0.173|2.66M|\n\n**Q4. Demo Video for Better Visualization**\n\nThank you for your suggestion. A demo video is available at the provided link for better visualization.", "claims_and_evidence": "The claims are supported in the evaluations. However, the transparency and interpretability can be improved.", "methods_and_evaluation_criteria": "The method and evaluation are technically suitable.", "theoretical_claims": "No theoretical claims needing proofs are involved in the paper.", "experimental_designs_or_analyses": "Exhaustive experiments and ablation studies were designed to verify the effect of the contributions. However, the reported results mainly focus on the rendering quality, which causes a lack of interpretability. e.g., as in most 3DGS-related works, it's necessary to report more results about the depth, primitive distribution, and even surface normal if convenient, to enhance the transparency and interpretability of the paper. For this paper, the rendered sensitive map can also be visualized.", "relation_to_broader_scientific_literature": "None.", "essential_references_not_discussed": "Though the most related works about the specific track were involved, more 3DGS-related works with edge prior can be included for discussion like:\n\n[1] Xiang, Haodong, et al. \"Gaussianroom: Improving 3d gaussian splatting with sdf guidance and monocular cues for indoor scene reconstruction.\" arXiv preprint arXiv:2405.19671 (2024).\n\n[2] Lin, Xin, et al. \"HQGS: High-Quality Novel View Synthesis with Gaussian Splatting in Degraded Scenes.\" The Thirteenth International Conference on Learning Representations.", "other_strengths_and_weaknesses": "Strengths:\n\n1. The proposed approaches of improving perceptual quality stem from a simple edge extraction, of which the conciseness may bring invaluable new insights to the community. \n2. Detailed experiments and ablations are designed.\n3. Experiment results show the method can achieve SOTA performance while keeping high efficiency. It can also adapt to large-scale scenes like BungeeNeRF.\n\nWeaknesses:\n\n1. The qualitative results somehow have a lack of interpretability. As in most 3DGS-related works, it's expected to report more qualitative \n results about the depth, primitive distribution, and even surface normal if convincing, to enhance the transparency and interoperability of the paper. For this paper, the rendered sensitive map can also be visualized.\n\n2. As summarized in Table 8, many additional hyperparameters are introduced. This raises a concern about the robustness for different scenes, despite some ablation studies conducted in Table 9.", "other_comments_or_suggestions": "For suggestion, it's better to provide a demo video for such a 3D vision task to show the performance more intuitively and comprehensively.", "questions_for_authors": "See the weaknesses. May raise the rating if the concerns can be well solved.\n\n## **Update after rebuttal**\nThe most recent reply addresses my concerns. I'll keep my rating to be positive.", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Meta-review: This paper proposes Perceptual-GS, a 3D Gaussian Splatting method guided by human perceptual sensitivity to improve rendering quality and efficiency. Reviewers appreciated the solid experiments and practical value, though some noted limited novelty and interpretability. The authors’ detailed rebuttal addressed most concerns, and all reviewers gave positive recommendations.
Decision: Accept (poster)