HQGS: High-Quality Novel View Synthesis with Gaussian Splatting in Degraded Scenes
Authors: Xin Lin
Shi Luo
Xiaojun Shan
Xiaoyu Zhou
Chao Ren
Lu Qi
Ming-Hsuan Yang
Nuno Vasconcelos
Venue: ICLR 2025 Poster (2025)
Keywords: 3D Reconstruction
3D Gaussian Splatting
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: 3D Gaussian Splatting (3DGS) has shown promising results for Novel View Synthesis. However, while it is quite effective when based on high-quality images, its performance declines as image quality degrades, due to lack of resolution, motion blur, noise, compression artifacts, or other factors common in real-world data collection. While some solutions have been proposed for specific types of degradation, general techniques are still missing. To address the problem, we propose a robust HQGS that significantly enhances the 3DGS under various degradation scenarios. We first analyze that 3DGS lacks sufficient attention in some detailed regions in low-quality scenes, leading to the absence of Gaussian primitives in those areas and resulting in loss of detail in the rendered images. To address this issue, we focus on leveraging edge structural information to provide additional guidance for 3DGS, enhancing its robustness. First, we introduce an edge-semantic fusion guidance module that combines rich texture information from high-frequency edge-aware maps with semantic information from images.  The fused features serve as prior guidance to capture detailed distribution across different regions, bringing more attention to areas with detailed edge information and allowing for a higher concentration of Gaussian primitives to be assigned to such areas. Additionally, we present a structural cosine similarity loss to complement pixel-level constraints, further improving the quality of the rendered images. Extensive experiments demonstrate that our method offers better robustness and achieves the best results across various degraded scenes. Source code and trained models are publicly available at: \url{https://github.com/linxin0/HQGS}.

Published as a conference paper at ICLR 2025
HQGS: HIGH-QUALITY NOVEL VIEW SYNTHESIS WITH GAUSSIAN SPLATTING IN DEGRADED SCENES
Xin Lin Lin
Shi Lu
Xiaojun Shan'
Xiaoyu Zhou
Chao Ren
Lu Qi
Ming-Hsuan Yang"
Nuno Vasconcelos'
UCCD
1nstaa660 Research
Sichuan University
"Peking University
UC Merced
ABSTRACT
3D Gaussian Splatting (3DGS) has shown promising results for Novel View Syn- thesis. However, while quite effective when based on high-quality images. its performance declines as image quality degrades, due to lack of resolution, mo- tion blur, noise, compression artifacts, or other factors common in real-worke data collection. While some solutions have been proposed for specific types of degra- dation, general techniques are still missing. To address the problem, we propose robust HQGS that significantly enhances the 3DGS under various degradation scenarios. We first analyze that 3DGS lacks sufficient attention some detailed regions in low-quality scenes, leading the absence of Gaussian primitives in those areas and resulting in loss of detail in the rendered images. To address this issue, focus on leveraging edge structural information to provide addi- tional guidance for 3DGS, enhancing its robustness. First, we introduce an edge- semantic fusion guidance module that combines rich texture information from high-frequency edge-aware maps with semantic information from images. The fused features serve as prior guidance capture detailed distribution across dif- ferent regions, bringing more attention to areas with detailed edge information and allowing for a higher concentration of Gaussian primitives to be assigned such areas. Additionally, we present structural cosine similarity loss to comple- ment pixel-level constraints, further improving the quality of the rendered images. Extensive experiments demonstrate that our method offers better robustness and achieves the best results across various degraded scenes. Source code and trained models are publicly available at: https: //gittttt githhub.... com/ / 1inxin0 /HQGS.
INTRODUCTION
Novel view synthesis advanced significantly in recent years, with the introduction of Neural Radi- ance Fields (NeRF) and 3D Gaussian Splatting (3DGS), benefiting applications such as augmented reality (AR) and virtual reality (VR) (Bian al., 2016; Dawood, 2009; Farshid al., 2018; Fassi et al., 2016). Nevertheless, existing methods assume high-quality images captured with precise camera parameters. When faced with images of low resolution, with motion blur, compression artifacts, noise, or other degradations common in real-world imaging, they often struggle. Some NeRF (Bahat al., 2022; Ma et al., 2022; Zhou al., 2023b; Pearl et al., 2022; Wu et al., 2024) and 3DGS (Feng et al., 2024) variants attempt to address the problem by incorporating various strate- gies or constraints, such as degradation kernels or super-sampling networks. However, these models are tailored for specific types of degradation and show poor generalization across degradation type and strength, even collapsing for severely degraded imagery. This illustrated Figure I, where NeRFLix and SRGS, two methods designed to address blur and low-resolution scenes, respectively, fail to handle each other's 's scenarios effectively. When considered over the range of degradation conditions considered in the figure, their performance is not superior those of the original NeRF and 3DGS models.
Correspondence to Qi: qglu1992@@@ecm wevieggalllooo Lin and equal contribution.

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Published as a conference paper at ICLR 2025
Blur
LPIPS
PSNR
Blur
0.09
0.09
Compression
9.20
Mixed
Compression
0.23
NeRF
NeRFLix
SRGS
3DGS
1OQGS
Low resolution
Noise
Low resolution
Noise
Figure I: Results existing methods (Mildenhall et al., 2021; Kerbl et al., 2023; Zhou et al., 2023b; Feng et al., 2024) and our HQGS on five degradation scenes. HQGS performs well against others in these different degradation types.
To improve performance of 3DGS under a range of challenging conditions, first consider the commonalities among the various types of image degradation of Figure 2 on the stages of 3D recon- struction and rendering. For reconstruction, our preliminary experiments (Figure 2(a)) show that, under degraded conditions, the distribution of recovered Gaussian primitives becomes too sparse to allow the capture of fine scene details, especially for small objects. To address this, we propose an Sdge-Seemantic Fusion Guidance (ESFG) module that explicitly encourages the reconstruction to direct attention to these details. ESFG is implemented by complementing the stream of scene views with a stream of views that capture the high-frequency image details, computed with an edge detection operator. A pair of semantic-an aware and edge-aware feature representations are then ex- tracted from these streams and fused by coss-attention.. These features finally used to refine the parameters of the Gaussian primitives extracted by 3DGS.
For rendering, our preliminary experiments (Figure 2(b)) confirm the findings of (Dong al., 2023; Lin et al., 2023b), showing that the low-frequency information derived from the global scene struc- ture is essential for high quality rendering. We thus introduce global Structural Cosine Similarity (SCS) loss to encourage consistency of global (low-frequency) structure between rendered and tar- get images. The ESFG module and SCS loss finally combined with the 3DGS model to produce a High-Quality Gaussian Splatting (HQGS) rendering model.
Extensive experimental results, under various types of degradation (low resolution, JPEG compres- sion, noise, blur, and mixed degradation). demonstrate the benefits of both the ESFG module and the SCS loss, as well the superiority of HQGS over previous NeRF and 3DGS approaches, for novel view synthesis from degraded imagery. Notably, HQGS performs robustly on more highly degraded images. Overall, this work makes the following contributions:
We formulate the problem of improving the robustness of novel view image rendering under various types of degradation, including low resolution, noise, blur, JPEG compression, and their combinations.
We hypothesize that solutions to this problem can benefit from explicitly reasoning in terms of the frequency content of input views, namely by encouraging the rendering model to pay attention to high frequency details during reconstruction and consistency of global (low- frequency) information during rendering.
We introduce new framework, HQGS, for the solution of this problem that maps this hypothesis into pair of mechanisms: the ESFG module, which combines and edge tour prior and semantic information to modulate the sensitivity of the model to finegrained scene details, and the SCS loss, which encourages consistency of global structures between rendered and target images.
We present evidence from comprehensive experiments confirming the validity of the hy- pothesis and showing that HQGS has significantly better robustness to image degradation than existing approaches.
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Blur
Noise
Low resolution
Compression
3DGS+ESFG
3DGS
3DGS-ESPG
JDGS
SUGS-ESPG
31XGS
3DGS
Comparison Gaussian primitives and rendered images 3DGS with proposed edgo-semantic fusion guidance (ESPG) module under various degraded scenes.
(a) 3DGS
(b) NeRFLix
(c) SRGS
(d) HQGS
(b) Comparison of the difference map hetween the rendered image and the clean version, which contains high and low frequency information.
Figure 2: (a) The visualization of the distribution of Gaussian primitives in trained 3DGS under multiple degradation scenes, along with the rendered 2D images. Due edge-semantic fusion guidance module providing priors for detailed regions, can distribute more Gaussian primitives some detailed areas. a result, the rendered novel view images contain richer details, particularly in elements like power lines and colorful flags. (b) We compare the difference map between the rendered image from existing methods and its corresponding clean one. A lot of high-frequency details and low-frequency areas involve some structures that bright, which means these both low- low-frequency and high-frequency regions.
not learned well. contrast, HQGS achieves better performance in
2 RELATED WORKS
Novel View Synthesis. Mildenhall et al. (Mildenhall et al., 2021) introduce the neural radiance field (NeRF) to implicitly represent static 3D scenes and synthesize novel views from multiple images with known poses. Building on this foundation, numerous NeRF-based models (Xu al., 2022: Deng et al., 2022; Chen et al., 2022; Fridovich-Kan et al.. 2022; Garbin et al., 2021; Reiser et al., 2021: Chen al., 2023) have been developed. Point-NeRF (Xu al., 2022) and DS-NeRF (Deng et al., 2022) integrate sparse 3D point cloud and depth information to resolve the geometric ambigui- ties inherent in NeRFs, leading to more accurate 3D point sampling and improved rendering quality. On the other hand, Plenoxels (Fridovich- Keithh et al., 2022), TensoRF (Chen et al., 2022), FastNeRF (Garbin et al., 2021), KiloNeRF (Reiser et al., 2021) and MobileNeRF (Chen et al., 2023) focus on employing various advanced techniques to accelerate the training and inference processes.
Most recently, 3D-GS (Kerbl et al., 2023), based on point cloud rendering, facilitates real-time novel view synthesis. While these methods have made notable progress in rendering high-quality scenes, they can still produce artifacts with low-quality images and imprecise camera poses.
Novel View Synthesis in Degraded Scenes. Several NeRF- and 3DGS-based approaches (Zhou et al., 2025; Huang et al., 2022: Bahat et al., 2022; Feng et al., 2024: Pearl et al., 2022: Zhou et al., 2023b) synthesizing high-quality novel views from degraded scenes using paired clean and degraded images. NeRF-SR (Wang et al., 2022) enhances output resolution through sub-pixel sam- pling, though this approach demands more computational resources and extends training times. On the other hand, NVSR (Bahat et al., 2022) trains a NeRF super-resolution (SR) network with multi- view data, leveraging the triplane structure to perform SR on low-resolution planes, thereby improv-
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frqgunryy Edge mmps
Rendcred
Edge Desetiin
ESFG
L
L SCS
Low-quality images
Initializution
Ts
Cam,
Target
Figure 3: HQGS framewok. The eigh-frequency edge-aware maps and low-quality images are jointly fed into the learnable ESFG module for feature modulation and fusion. The fused features guide the training process of HQGS, providing reliable detail priors. During training, employ pixel-level Li loss and global structural cosine similarity loss Lscs optimize the model.
ing the overall NeRF resolution. (Huang al., 2022), ReISR-NeRE introduces a specialized SR module that incorporates high-resolution reference images, which can lead to longer training and inference times. To achieve high-quality rendering results in the noisy scene, NaN (Pearl et al., 2022) adapts the feed-forward IBRNet view synthesis method to achieve competitive burst denois- ing results. Instead focusing on 3D learning, NeRFLiX (Zhou et al., 2023b) and NeRFLiX++ (Zhou et al., 2023a) learn general 2D viewpoint mixer via simulated image degradation. However, the distribution the rendering artifacts shifts from the simulated data, the performance degrades. Drantal-Neke (Yang et al., 2024) employs diffusion brattdsioo-bbeeddd image quality enhancement model to create higher-quality image pairs for training a NeRF model. Recently, SRGS (Feng et al., 2024) has emerged 3DGS-basee model capable of achieving super-resolution rendering using paired low- and high-quality images.
Image Quality Enhancement and Restoration. Image restoration aims enhance the quality of degraded images affected by various types and levels of degradation. This challenging task encom- passes denoising (Lin et al., 2023b; Ren al., 2021; 2022), deblurring (Fang et al., 2023; Sun al., 2023: Pan et al., 2023), and general restoration (Zamir et al., 2021: 2022a; Lin et al., 2023a; 2024). Restormer (Zamir et al., 2022a) incorporates transformers for low-level restoration performance and computational costs, and MIRNerv2 (Zamir et al., 2022b) restores images through a novel feature extraction network. On the other hand, Kong et al. (Kong et al., 2023) present Transformer- based method for high-quality image deblurring, by utilizing frequency-doman erties to simplify scaled dot product attention and alleviate the need for complex matrix multipli- cation. Recently, MRLPFNet (Dong et al., 2023) proposes simple yet efficient low low-pass filtering network, and demonstrate the importance of repairing low-fi www-frequency regions to improve image qual- ity. SCPGabNet (Lin et al., 2023b) and SCP"GAN (Lin et al., 2024) non-paired unsupervised restoration methods that exploit the importance of low-frequency constraints and introduce back- ground consistency loss (BGM) specifically for low-frequency areas.
balance the
prop-
PROPOSED METHOD
Figure 3 shows the overall pipeline of the proposed HQGS that receives multi-view low-quality images E R Nx x3 as the input. The N. H and W are the number, height and width of images. Using these images, first generate the initial point cloud and corresponding camera
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Published as a conference paper at ICLR 2025
Semmmicosware famure
Sigmoid
Det peoduct
Dowasampling
Lowegalry
Cross product
Add
Concatenate
Sallmer
Fse
Pacte
Edge-swanc faturee
Figure 4:
The ESFG module separately learns semantic-aware feature and edge-aware feature, and then jointly guides the training of HQGS.
views through the classical COLMAP method. Meanwhile, we detect the boundaries of each image to construct the high-frequence edge-aware maps. Next, train atructure-assistee 3DGS model to leverage structural information both the reconstruction and rendering stages. On the one hand, propose edge-semantic fusion guidance (ESFG) module that utilizes high-frequency edge- aware maps as a condition signal for reconstructing 3DGS. On the other hand, we utilize structural cosine similarity loss (Csas) to constrain the global structure when rendering Gaussian primitives into an image within view.
In the following subsections, first introduce the various degradation types designed in method. Then, we will elaborate on the ESFG module and Lsos, for robust HQGS.
3.1 DEGRADATION TYPES
To account for the range of adverse conditions found in real-world conditions, we consider five types of image degradation. These are simulated by applying the following operations to the sets of high-quality images commonly used the literature:
Low Resolution: 4x downsampling is applied create low-resolution images.
JPEG Compression: Images are compressed using JPEG with setting the quality level 10.
Blur: Blurred images are generated using a Gaussian blur kernel with a radius ranging from 10 to
20, and the blur angle is randomly selected within the range [0, 2mr]
Noise: Gaussian noise with a standard deviation 10 is added the images.
Mixed Degradation: combination of the degradations above, applied in the following order: low resolution, JPEG compression, blurring, and noise.
Following 3DGS (Kerbl et al., 2023), we input set of low-quality scene images with the corre- sponding cameras calibrated by COLMAP, producing a sparse point cloud as a side effect. Fig- ure 2(a) compares low-quality and high-quality scenes with their corresponding point clouds. The low-quality images provide less visual information, creating a sparser point cloud than the high- quality scene.
3.2 SEGE-SEMANTIC FUSION GUIDANCE MODULE
Motivation. Since degradation affects the edge details of some objects in the scene, making them difficult capture. Figure 2(a) shows Gaussian primitives distributed these areas, such as power lines and colorful flags, become sparse. This causes these objects to be omitted in the rendered 2D images, reducing the quality the rendered novel views. To address this problem, we leverage specific edge contour priors as a 'reminder' to inform the model that particular objects in these regions must be generated. This encourages it to cover these areas with more Gaussian primitives during reconstruction.
Solution. We propose the Edge- Semantic Fusion Guidance (ESFG) module to enhance semantics from lua-iuality images by the high- figh-frequency edge-aware maps. To extract global high-frequene edge-aware maps (F), the Sohel operator calculate the gradient maps V7 E N
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Published as a conference paper at ICLR 2025
of the low-quality images I that contain critical edge information. We then normalize V/ to the gradient masks V/ along with height and width dimensions:
VI VVI
VI
(1)
Maz - Min(VI))
where Max(.) and Min(-) is the maximum and minimum value in VL. Lastly, we obtain the high- frequency edge-aware map E by
E-VIOL.
where @ represents matrix multiplication. The E E RN*Hxxx3 highlights rich image textures and details crucial for image quality enhancement and novel view synthesis. The difference between VI' and E is presented in the appendix.
In the ESFG module, we down-sample them to I' and E' 2x as the input for computational effi cieney where I' and E' E RN* x to the camera order, respectively. To match the position parameters μu RMx of the Gaussian primitives, we perform scale transformation to obtain Ts and E' R x3 using MLPs, where M represents the number of Gaussian primitives.
2x 4 x3 Then, we concatenate the images of I' and E' according
The ESFG module contains branches in Figure 4. In the upper branch, employ cross- attention mechanism where Esr acts as the query, and the I' serves as the key and value. This cross-attention between the low-quality images and the high-frequency edge-aware maps connects local high-frequency details with global semantics, producing more comprehensive guided features, i.e. semantic-aware features. edge information, resulting in the edge-aware feature. The output features from both branches are then concatenated to form the final fused feature F followed by sigmoid non-linear operation. Finally, F, is used to modulate the original position parameters l E R of the Gaussian primitives to obtain new position parameters Unews and then HQGS models as G(z):
the lower branch, a learnable MI.P layer is used to better interpret
Pnew = Sigmoidd (F ④ P + ,1, G(x) el
(3)
(6- 3 [xx "
((x (s-Paew)).
where @ represents matrix multiplication, and E 33x3 an anisotropic covariance matrix, which is factorized into scaling matrix S and rotation matrix R as E = RSS R.
3.3 STRUCTURAL COSINE SIMILARITY LOSS
While high-frequency information is essential for enhancing image quality, low-frequency informa- tion should not be overlooked, as it corresponds to smooth areas and colors closely related to global structural information (Dong al., 2023; Lin et al., 2023b).
We propose a Structural Cosine Similarity Loss (Escs) to enhance the global structure of low- frequency regions and improve overall rendering quality. This loss emphasizes directional consis- tency the low-frequency feature space, allowing it to better capture the image's global structure and thereby achieve closer match its overall characteristics.
In the optimization stage, besides the original Li between the rendered image (ft) and its corre- sponding target image (7'). We add our Lscs as low-frequency-wwar loss. Specifically, first obtain the lwwfrreuency structure-an aware map (S) of the R and T:
Rs = (1 v O R, Ts = V1)
(5)
We then compute structural cosine similarity loss Lscs:
ΣN R2g T
Lscs
(6)
Rsll2 ||ss|22
where is the total number of pixels, and represents the i-th pixel. The overall loss function is:
L X1C + XeCsoss
where D and X2 denote weight parameters.

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Published as a conference paper at ICLR 2025
Low resolution
Compression PSNR* LPIPS],
Motion Blurry
Gaussian Noisy
Mixed
Methods
Ren time (s)
PSNR1
LPIPS
PSNR LPIPS., PSNR LPIPS]
PSNR
LPIPS!
NeRF
25.02
0.106
24.72
0.122
24.78
0.109
26.33
0.115
24.18
0.102
-4.7
NeRF based
Nan
23.73
0.257
20.75
0.349
21.92
0.192
21.27
0.476
20.97
0.541
100.
NVSR
25.31
0.117
25.42
0.157
25.13
0.097
26.52
0.129
25.61
0.096
~4.
NeRFLiX
25.58
0.093
25.71
0.112
25.53
0.088
26.69
0.097
26.04
0.064
~7.5
3DGS
25.19
0.096
25.31
0.113
25.68
0.075
26.20
0.119
24.88
0.085
~0.2
3DGS
SRGS
25.96
0.087
25.54
0.091
25.92
0.076
26.47
0.086
26,30
0.074
~0,2
based
HQGS
26.48
0.065
26.19
0.077
26.55
0.056
27.20
0.071
27.27
0.057
~022
Table I: Results of both NeRF-based and 3DGS-based methods on LLFF dataset (Ma et al., 2022). Ren time denotes the rendering time for each frame. The red color indicates the best results, and the blue color indicates the second-best results.
Nan
3DGS
NVSR
NeRFLix
SRGS
HQGS (Ours)
Nan
3DGS
NVSR
NeRFLix
SRGS
HQGS (Ours)
Nan
3DGS
NVSR
NeRFLix
SRGS
HQGS (Ours)
Nam
JDGS
NVSR
NeRILix
SRGS
IIQGS (Ours)
Figure 5: Visualization of the low-resolution, compression, blurry and noisy scenes LLFF (Mildenhall et al., 2019).
4 EXPERIMENTS
We first describe the utilized datasets and present the implementation details. Next, we provide comprehensive analysis of the experimental results, qualitatively and quantitatively. Finally, conduct ablation studies to validate the effectiveness of the proposed modules and the robustness of HQGS compared to other approaches.
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4.1 EXPERIMENTAL SETUP
Datasets. We evaluate the proposed HQGS pipeline on two datasets: (1) The LLFF dataset (Milden- hall et al., 2019), which contains real-world images from eight distinct scenes, with each scene com- prising 20 to 62 images. Of these, 1/4 are reserved for testing, while the remaining are used for training. (2) A synthetic dataset derived from the Blender scenes used in DeblurNeRF (Ma et al., 2022), where 1/8 of the data is used for testing and the other 7/8 for training.
Implementation Details. Our implementation is based on the 3DGS (Kerbl et al., 2023) framework. The learning rate for the leamable parameters of 3D Gaussians follows the official settings, while the learning rate for the ESFG module set to le-6. We evaluate our method using various metries, including PSNR, SSIM, and LPIPS, following previous work (Zhou et al., 2023e; Kerbl et al., 2023; Feng et al., 2024). All experiments are conducted on single Nvidia GeForce RTX 3090 GPU. Following previous methods, we use the trained restoration network to get the target image for the results comparisons. The A and Ag in equation 7 are set to 1 and 5, respectively.
Evaluated Methods. We evaluate methods that provide code and pre-trained models for fair and comprehensive comparisons. For NeRF-based methods, we choose NeRF (Mildenhall et al., 2021), Nan (Pearl et al., 2022), NVSR (Bahat et al., 2022), and NeRFLiX (Zhou et al., 2023b). In terms of 3DGS-based approaches, 3DGS (Kerbl et al., 2023) and SRGS (Feng et al., 2024) are used for comparison. We retrain all methods using low and restored high-quality pairs. The pre-trained IVM is used in NeRFLiX. To comprehensively compare the efficiency and performance of the networks, all 3DGS- models are trained for 50,000 iterations. Meanwhile, we also provide a comparison of the training time in the following section.
4.2 RESULTS AND COMPARISONS.
Novel View Reconstruction on LLFF (Mildenhall et al., 2019). Table compares HQGS to all NeRF (Mildenhall et al., 2021; Pearl et al., 2022; Bahat al., 2022; Zhou et al., 2023b) and 3DGS (Kerbl et al., 2023; Feng et al., 2024) baselines on the LLFF dataset, showing that HQGS achieves the best results across all degradation conditions. In the low-resolution setting, it achieves gains of 1.46 1B/0.041. 0.90 dB/0.028, 1.29 dB/0.031, and 0.52 dB/0.022 in PSNR/LPIPS over NeRF, NeRFLiX, 3DGS, and SRGS, respectively. For JPEG compression conditions. it has gained 0.48 dB/0.035 and 0.65 dB/0.014 over NeRFLiX and SRGS. It can also be seen that NeRF-based methods, such as NeRFLiX, require 7.5 seconds for rendering vs. only 200 milliseconds for HQGS. Figure 5 compares images synthesized by the different methods under low resolution, compression, blurry, and noisy conditions. The images generated by HQGS contain richer high-frequency details and more precise visual effects.
Novel View Reconstruction on De- blurNeRF (Ma et al., 2022). Ta- ble 2 presents the average PSNR and LPIPS of different methods under five degradation conditions. HQGS achieves the best performance under all metrics. Compared to the NeR- FLix (Zhou al.. 2023b), has gain of 0.79 dB/0.008/0.016 on PSNR/SSIM/LPIPS HQGS also de-
Methods
PSNR
SSIM t
LPIPS.
NeRF
26.16
0.894
0.067
Nan
21.87
0.738
0.105
NVSR
26.45
0.895
0.072
NeRFLiX
26.95
0.896
0.061
3DGS
26.27
0.895
0.072
SRGS
26.82
0.898
0.063
HQGS (Ours)
27.74
0.904
0.045
Table 2: Results on DeblurNeRF dataset (Ma al., 2022).
livers substantial improvements across all metrics over SRGS (Feng et al., 2024), which is designed for resolution enhancement.
4.3 ABLATION STUDIES ON THE PROPOSED MODULES.
In this section, conduct ablation studies on the proposed ESFG module and Lscs. We use the ground truth as the target image. Additional analyses can be found in the appendix.
Effectiveness of the ESFG Module. In Table 3, we validate the effectiveness of the proposed ESFG module. VI represents the original 3DGS. V2 extends V1 by incorporating a semantic-wwan feature (SAF) from low-quality images, while V3 uses edge-aware features (EAF) extracted from
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Methods
SAF
EAF
Concat CA
PSNR (dB)t
SSIMT
LPIPS
VI
27.63
0.893
0.067
V2
28.05
0.895 10.002
0.066
V3
28. 10.98
0.90570:012
0.05810.009
V4
/
28.42
0.901 20.008
0.06249.000
V5
28.9211.29
0.913
0.05240.015
V6
29.26 1-66
0.914 10.021
0.049 10.018
Table Effectiveness of the proposed ESFG module.
Methods
Lr
LRCM
Esp
Lscs
PSNR (dB)f
SSIM1
LPIPS
VI
d
29.26
0.914
0,049
V
29.42
0.916
0.045
V3
V
V
29.39h0.15 10.13
0.917 1.917
0.043 104310.006
V4
29.91
0.919
0.031 10.018
Table 4: Ablation studies on the proposed Lscs. The experiments are conducted on the 'Wine' scene with blurry degradation from the DeblurNeRF dataset.
Method
3DGS
3DGS
3DGS 3DGS+ESFG 3DGS+ESIG 3DGS+ESFG
Sobel
Gaussian
Laplace
PSNR(dB)t 28.61
28.27
28.41
29.26 0.914
28,86 0.908
28.95
SSIM 0.905
0.902
0.904
0.908
LPIPS 0.058
0.066
0.063
0.049
0.056
0.055
Table Ablation studies on different guidance. experiments are conducted on 'Wine' scenes with blurry degradation from the DeblurNeRF dataset.
high-frequency edge-aware maps to guide 3DGS. V4 enhances the framework by applying cross- attention (CA) between SAF and EAF. V5 fuses SAF and EAF by concatenating, and V6 represents complete framework with all four components. The specific structures of these variants shown in detail in the appendix.
Using SAF slightly improves PSNR from 27.63 dB to 28.05 dB. Replacing SAF with EAF (V3) improves PSNR by 0.56 dB compared to V2. However, V4 with the cross-attention mechanism (CA) further leads 0.37 dB improvement in a 0.31 dB/0.008/0.000 and 0.87 dB/0.008/0.014 gain in PSNR/SSIM/LPIP over using SAF or EAF individually. Finally, incorporating all components (V6) achieves the best results, with 1.63 1B/0.021/0.011 improvement in PSNR/SSIM/LPIPP over VI.
V2. Combining both SAF and EAF results
Effectiveness of LScs. We assess the effectiveness of the Lscs Table 4. VI denotes the 3DGS with the ESFG module trained using only L.. V2 builds upon VI by incorporating a structure- aware pixel-level loss (Lsp), which calculates the L, between structural elements in the rendered and target images. V3 introduces the LBGM (Lin et al., 2023b), and V4 replaces the Lsp with our structural cosine similarity loss (Lscis). We observe that adding the LEGM to the L1 provides slight improvement (approximately 0.1 dB in PSNR). Similarly, LsP results in marginal improve- ment. However, substituting local structural constraints with the global Lscs yields significant 0.65 dB improvement formation is more effective at capturing overall image consistency than pixel-level constraints.
the original L1 loss, indicating that global low-frequency structural in-
Comparison of Other Methods for Obtaining High- FrequeFry Maps. We compare the results of different operators in both the 3DGS framework (V3 in Table 3) and the 3DGS with our ESFG module (V6 in Table 3). The visualization of these maps is shown in the appendix. Table shows that frehurryy information from the Gaussian filter and Laplace operator can improve performance. However, it is still lower than higu-fryy edge-aware feature in both V3 and V6 frameworks.

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Table 6: Robustness validation two progressive degradation test sets (Ma et al., 2022). indicates the best results, and the blue color indicates the second- best results.
The red color
Gaussian noise
Low resolution
Methods
0
10
25
50
X
2 X
X
x
PSNR LPIPS PSNR LPIPS PSNR LPIPS PSNR LPIPS PSNR LPIPS PSNR LPIPS PSNR LPIPS PSNR LPIPS
NeRF
30.42 0.072 29.11 1 0.079 26.78 0.091 23.04 0.143 29.83 0.113 29.71 0.128 28.83 0.138 28.16 0.148
3DGS
30.21 0.043 29.19 0.052 27.46 0.077 22.78 0.109 30.25 0.091 30.18 0.094 29.25 0.092 28.63 0.111
NeRFLix
30.86 0.054 29.47 0.063 27.04 0.071 23.32 0.112 31.42 0.069 30.87 0.069 30.12 0.076 29.66 0.096
SRGS
30.71
0.036 29.17 0.045 27.63 0.061 23.21 0.126 31.36 0.078 30.93 0.065 30.54 0.061 30.11 0.087
HQGS
31.32 0.018 30.41
0.029
28.63
0.043
26.31
0.067
32.08
0.031
31.85
0.033
31.61 0.038
31.37 0.051
28:79
PSNR (80)
26:33
45.45
23
Taxe Oil))
Tixe (Mix)
Figure 6: Training time vs quality for blurry scenes, HQGS performs well against 3GDS and SRGS under both the PSNR and LPIPS metries.
4.4 ANALYSIS ON ROBUSTNESS OF OUR HQGS.
So far, we have considered mild image degradations. In this section, we investigate the robustness of HQGS across degradation levels. We construct two types of progressive degradation test sets: (1) Noise: Gaussian noise with variances of 0 (clean), 10, 25, and 50, and (2) Low resolution: images downsampled to 1x (clean), 2x, 4x, and 8x scales. Table 6 shows that the gains of HQGS over existing methods increases with the degradation strength and are quite large for the strongest degradations for both types of degradation. Especially as noise becomes severe (variance increasing from 25 to 50), all methods exhibit sharp drop, for example, SRGS from 27.63 dB to 23.21 dB and NeRFLix from 27. .404 dB to 23.32 dB. making high-qualit rendering challenging. In contrast, HQGS has a much smaller decline and maintains a robust performance 26.31 1 dB, which achieves 3.10 dB and 2.99 dB improvement compared to SRGS and NeRFLix. A similar trend is observed in low-resolution scenarios, where our method consistently demonstrates more robust performance than others. For instance, under the 8 setting, our method achieves 1.26 dB/0.036 and 1.71 dB/0.045 increase over SRGS and NeRFLix on PSNR and LPIPS.
4.5 TRAINING TIME VS QUALITY.
In general, there is trade-off between rendering quality and time, as higher quality rendering can be achieved by considering more views of the scene. Figure 6 compares the performance different 3DGS based approaches as a function of training time. It is clear that HQGS achieves better trade- off than 3DGS and SRGS. For instance, a 5-minute reconstruction by HQGS has a 1.22 dB (0.018) gain in PSNR (LPIPS) over the 3DGS results for 9 minutes.
CONCLUSION
In this paper, we propose a robust Gaussian variant HQGS, which performs favorably under various degradation conditions and exhibits strong robustness the degradation level increases. Building upon the limitations of the existing 3DGS in rendering scene details due to degradation, we propose an edge-semantic fusion guidance module to guide the positional parameters of 3DGS. Furthermore, during optimization, we introduce a global structural cosine similarity loss (LsCs) to complement the pixel-level L, loss. Extensive experimental results demonstrate the effectiveness and robustness of the proposed HQGS.
Acknowledgements. This work was partially funded by NSF award IIS-2303153.
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A SPECIFIC STRUCTURE OF VARIANTS IN SECTION 4.3 OF THE MAIN PAPER
Five comparative variants are tested to verify the influence of the proposed Edge Fusion Guidance (ESFG) module. Since the experimental results have been reported in sub-scction 4.3, mainly deseribe their specific structures in this section. As the V1 in Table 3 of the main paper is the 3DGS without learnable modules, only provide details for the remaining variants. As shown in Figures. 7, 8, and 10.
Low-quality images
Down
MIP
Semantic-aware feature
Figure 7: The structure of V2 in Table of the main paper.
High-frequency Edge-aware maps
E'
M
Down
MLP
Edgc-aware feature
Figure 8: The structure of V3 in Table of the main paper.
B VISUALIZATION RESULTS OF OTHER METHODS FOR OBTAINING FIEHUFFREQENNYY MAPS.
Our goal is to capture contour regions representing the image's high-freyuency content. We first show the V and E Section 3.2 in the main paper in Figure 11. After that, as shown in Figure 12, explore other methods for extracting high- figh-freeuency content, such using Gaussian high-pass filter and Laplace operator to obtain high-frequency responses.
C ROBUSTNESS VALIDATION ON EDGE MAPS.
We leverage the well-known fact (T., 2009) that edges have sufficient frequency information and can be obtained by an edge detection operator even from degraded images. We provide visualizations of edge detection results obtained using the Sobel operator under progressively challenging condi- tions, as shown in Figure 13 in the submitted Supplementary Material. Additionally, we quantified the results by calculating the PSNR metrics of the images and edge maps under various condi- tions compared to the clean ones. Then, we presented the PSNR variance of the images and edge maps: 10.18/0.285 This further demonstrates that the edge maps are more robust under progres- sively degraded conditions. Thus, edge detection performs well in extracting edge information to considerable extent, even under relatively challenging conditions.
D ABLATION STUDIES ON THE DOWNSAMPLING PARAMETERS IN THE ESFG MODULE.
We introduce downsampling in the ESFG module to reduce dimensionality and save computational resources. To further investigate its impact, supplement additional experiments under two
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V
Semantic-aware feature
I'
Low-quality images
Down
K
Softmas
Hieb-frequency Edge-aware maps
E
E'
M
attention
Down
MLB
Figure 9;
The structure of V4 in Table of the main paper.
l's
Low-quality images
Down
Semantic-aware feature
Fusion feature
High-Sequency
E'
M
Edge-awarc
Down
Edge-aware feature
Figure 10: The structure of V5 in Table 3 of the main paper.
figurations: Without Downsampling and 4 x Downsampling in Table Therefore, we choose the to balance the training time and the performance.
E ADDITIONAL QUALITATIVE RESULTS.
This section presents additional results to underscore the advanced capabilities of our HQGS over other leading NeRF-based and 3DGS based techniques. Illustrated Figures. 14, our methods consistently deliver superior quality of rendered image.
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7
Image
E
Figure 11: Visualization the V and E.
Image
Gaussian Filter
Laplace
Sobel
Figure 12: Visualization the map from different operators.
Images
Edge maps
2 x
x
8 x
Figure 13: Visualization edge maps on progressive degradation scenes.
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Methods
1
x
PSNR(dB)
31.79
31.70
31.42
Time(Minn)
16.4
13.3
11.2
Table 7: Ablation Studies on Downsampling Parameters the ESFG Module.
Nan
3DGS
NVSR
NectLix
SRGS
HQGS (Ours)
Nan
3DGS
NVSR
NerfLix
SRGS
HQGS (Ours)
Figure 14: Visualization results the LLFF datasct.
17


Reviews:
[{"review_id": "wcff7jcDI5", "reviewer": "Reviewer_rTVv", "summary": "This paper presents a novel view synthesis method called HQGS, specifically optimized for low-quality images, such as those with low resolution, blur, and noise. HQGS employs an Edge-Semantic Fusion Guidance (ESFG) module to enhance the detail-capturing ability of 3D Gaussian splatting and introduces a Structural Cosine Similarity Loss (LSCS) to further improve global consistency in image rendering. Experimental results show that HQGS demonstrates stable performance across various degraded scenarios, outperforming other NeRF and 3DGS-based methods in metrics like PSNR, SSIM, and LPIPS.", "questions": "1. I am curious about the rationality of generating edge maps from low-quality images. Since edge maps are generated from low-quality images, can they still effectively capture key edge information in severely degraded scenes? Can the author provide edge maps with different degrees of visual degradation and the impact of failed edge map visualization on the results? Furthermore, in severely degraded scenes, is it possible to use a pre-trained image restoration model to generate high-quality images before extracting edge maps?\nThe paper mentions that low-quality images result in sparse point clouds but does not clarify whether the ESFG module impacts the density distribution of Gaussian elements. Can the ESFG module improve the density of the point cloud while maintaining the total number of Gaussian elements? Is there a densification strategy or explanation of how the ESFG module affects 3DGS densification to better handle the sparse point clouds generated by low-quality images?\n\n2. The authors mention that the method combines high-frequency and low-frequency features. Could you provide a visualization of the number of the Gaussian elements across high- and low-frequency regions within an image to show how the method effectively handles these different areas?\n\n3. Figure 7 shows only the differences between HQGS and 3DGS. Could the authors supplement this with a robustness comparison to SRGS for a more comprehensive evaluation of HQGS’s performance?\n\n4. Could the authors provide a comparison of training times across different methods, especially discussing the impact of the ESFG module on training time?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 2, "presentation": 3, "contribution": 3, "strengths": "1. This paper combines edge-awareness and semantic awareness through the ESFG module, providing essential high-frequency edge information to improve 3D Gaussian splatting (3DGS) reconstruction on low-quality images. The introduction of LSCS further enhances the global structural consistency of rendered images, which is an innovative design.\n2. The experiments cover a wide range of common degradation conditions (e.g., low resolution, JPEG compression, blur, and noise) and compare the performance of HQGS against other state-of-the-art methods. The results demonstrate that HQGS not only outperforms these methods in image quality but also maintains efficiency in rendering time.", "weaknesses": "1. This approach heavily relies on high-frequency edge maps. For severely degraded images, using the Sobel operator to generate edge maps may result in significant detail loss. Given the instability of edge information in low-quality images, it is questionable whether ESFG can reliably extract edge information under various levels of degradation. There is a lack of robustness experiments on edge maps to verify the applicability of this approach.\n2. The paper mentions that low-quality images produce sparse point clouds, which can negatively impact reconstruction quality. However, the paper does not clarify whether ESFG influences the density or number of Gaussian elements. If the point cloud density is insufficient, simply adjusting the distribution might not achieve optimal results.\n3. Although the paper mentions that the method combines high and low-frequency information, it does not present the actual distribution of Gaussian elements in high- and low-frequency regions of the images. A lack of intuitive visualization makes it difficult to verify the practical effectiveness of ESFG and LSCS in these regions.\n4. While Figure 7 demonstrates that HQGS exhibits greater robustness compared to 3DGS, it lacks a direct comparison of robustness with SRGS (e.g., in noisy or low-resolution scenarios). This omission limits the understanding of HQGS's robustness relative to other 3DGS optimization methods.\n5. The paper mentions only the total training iterations but does not provide specific data on training time. Given that the addition of the ESFG module may increase training costs, the paper should ideally compare training efficiency, particularly in terms of the impact of ESFG on training duration.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "lpP5RHa3cc", "reviewer": "Reviewer_cE6C", "summary": "The authors identify that 3DGS performs poorly with low-quality images due to insufficient attention to detailed regions, leading to a lack of Gaussian primitives and loss of detail.\n\nTo improve this, this paper presents an approach named HQGS, including Edge-Semantic Fusion Guidance Module and Structural Cosine Similarity Loss.\n\nEdge-Semantic Fusion Guidance Module: Combines high-frequency edge-aware maps with semantic information to guide the distribution of Gaussian primitives, enhancing detail in rendered images.\n\nStructural Cosine Similarity Loss: Complements pixel-level constraints by focusing on structural similarities, further improving image quality.\n\nExperimental results demonstrate that HQGS enhances robustness and performance in various degraded scenes.", "questions": "How were the experiments for Table 3 and Table 4 conducted? In which scenes were they performed? What types of degradation were used?\n\nFrom the input in Figure 2a, it is impossible to see the presence of the \"power lines\". I am curious whether it is really possible to reconstruct the clear \"power lines\" in Figure 2b from such low-quality input views. How can this phenomenon be explained? Shouldn't 3D Gaussians be unable to imagine and reconstruct features that are not present (or almost completely blurred) in the input views?\n\nI noticed that the model was trained for 50,000 iterations, which is more than the number used for vanilla 3D-GS. Would this have an impact? If the model is trained for 50,000 iterations, would all other parameters remain unchanged, including those for densification? If so, do the additional 30,000+ iterations seem redundant, or are they used to mainly for the optimization of the MLPs?\n\nAre the weights of the MLP optimized individually for each scene, or are they generalized after pre-training?\n\nRegarding lines 531-532, since you have added an MLP and trained for 50,000 iterations, the training time for HQGS would at least be longer, right?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "The proposed HQGS framework effectively addresses the challenges of degraded images in novel view synthesis by introducing an Edge-Semantic Fusion Guidance (ESFG) module and a Structural Cosine Similarity Loss (LSCS).\n\nThe ESFG module enhances the distribution of Gaussian primitives and improves detail generation, while LSCS ensures global low-frequency structure consistency, leading to higher quality rendered images.\n\nExtensive experiments demonstrate superior robustness and performance in various degradation scenarios, outperforming state-of-the-art methods.", "weaknesses": "The method relies heavily on high-quality edge and semantic information, which may be challenging to obtain in extremely degraded or noisy images.\n\nThe computational complexity introduced by the ESFG module and LSCS could increase training and inference times, potentially limiting real-time applications.\n\nThe presentation of the paper is not optimal in several aspects:\nFigure 1 suffers from color blending issues, making it difficult to distinguish between different color regions corresponding to various methods.\nFigure 2 is mentioned before Figure 1 in the text, which can be confusing for readers.\nTables 1 and 2 present similar results but use different formatting (one with colored text and one without), leading to inconsistency and potential confusion.\nFor Figure 5, the effectiveness of the method cannot be understood due to the lack of visualized input views.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "2wMYh6QVvB", "reviewer": "Reviewer_s4XL", "summary": "This work is concerned with the improvement of 3D Gaussian Splatting-based radiance fields computed for images that have quality issues. In particular, blur, reduced resolution, compression artifacts, and noise. The authors present a proposed method with two key modifications over the prior art. The first modification is an edge fusion guidance module that merges semantic information with edge information to favor the representation of fine details in the final radiance field overcoming issues with the above distortions. The second key modification is the introduction of a structural cosine similarity loss that acts on the low frequency areas of the rendered images to ensure better representation of low texture areas of the radiance field.", "questions": "Q1: In the abstract of the work, the author's state: \"The fused features serve as prior guidance to capture detailed distribution across different regions, bringing more attention to areas with a higher concentration of Gaussian primitives.\". I find this sentence to be confusing. Later in the work it becomes clear that the ESFG module emphasizes edge related information in the input images in order to adapt the layout and properties of Gaussian's to better capture key information during training. In other words, the ESFG module guides the training of the Gaussian primitives by bringing more attention to key areas of the input images. It does not bring \"more attention to areas with a higher concentration of Gaussian primitives.\" as this implies that the ESFG module is concerned with drawing attention to the density of Gaussians in the radiance field, which is not the case. It draws attention to key features of the input images and this in turn effects the density of the Gaussian primitives. I suspect this is what the authors meant, but the language is vague and admits the other interpretation. I suggest the following rewording of this sentence: \"The fused features serve as prior guidance to capture detailed distribution across different regions, bringing more attention to areas with higher semantic meaning, such as edges, in turn allowing for higher concentration of Gaussian primitives to be assigned to such areas.\".\nQ2: On line 48, the authors state \"Our preliminary experiments (Figure 2(b)) show that, for reconstruction, the distribution of reconstructed Gaussian primitives becomes too sparse to allow the capture of fine scene details. \" Which distortions are the authors referring to here? Noise? Low resolution? Blur? Compression artifacts? All distortions? Please clarify what is being referred in this text? Please state clearly whether this observation refers to specific types of distortions (for example if it refers solely to blur) or whether this statement refers to all types of distortions.\nQ3: Figure 4 has a spelling error, \"Position paprameter in Gaussians\" should be  \"Position parameter in Gaussians\". In addition, in the caption, the authors state \"It separately learns semantic-aware feature and edge-aware feature, and\nthen jointly guides the training of HQGS.\" Please avoid the usage of vague terms like \"It\". What is \"It\" precisely? For example a potential better sentence is: \"The ESFG module learns semantic-aware features and edge-aware features, and...\".\nQ4: Equation (2) introduces a notation for matrix multiplication that is not explained until after equation 4. Please explain notation at the point at which it is introduced.\nQ5: Line 275, \"then HQGS model it as G(x)\" should be \"then HQGS models it as G(x)\".\nQ6: Line 352, \"methods that provide codes and\" should be \"methods that provide code and\".\nQ7: Figure 7 is a pastel, set of 3D overlapping bars with partial transparency that make the plot overly artistic and hard to read. A simple set of non-overlapping groups bars would have provided the same information and been clearer.\nQ8: Figure 8 contains pastel colored, semi-transparent overlaid plots with some form of fill gradient transitions. The pastel colors are very similar and hard to differentiate in the plot. Please simplify and remove the unnecessary additional graphics. Key information like the numbers on the graphs are overlapping making them difficult to read.\nQ9: In section 3.1, the authors state that the JPEG Compression will only be studied at a quality level of 10. Please explain why this particular value was chosen and why only a singular value was chosen for this parameter. In addition, only one value of Low Resolution was selected (4x downsampling). Why was this number chosen? Please provide additional text to describe the justification of the choice of JPEG quality level and downsampling factor. In addition please consider the testing of a wider range of these parameters (for example JPEG quality settings higher and lower than 10 as well as downsampling factors of 2x and 8x). If it is not appropriate to test a wider variety of values for JPEG Compression and downsampling, please state the rationale clearly.", "limitations": "", "rating": 8, "confidence": 4, "soundness": 4, "presentation": 4, "contribution": 4, "strengths": "This paper is very well written. The authors supply sufficient detail on the proposed method to allow it to be correctly understood both on it's own and in the context of the prior art. The contribution is quite novel and although the edge fusion guidance module is motivated by the prior art, it is certainly not a trivial increment on the prior art and represents a new way of looking at the problem of low quality input images to radiance field training. The experimental section is quite strong, with comprehensive comparisons to the prior art and convincing improvements. The ablation studies are quite thorough, showing that the authors have put a lot of thought into the study and gone to considerable efforts to explore the work. The results of the ablation studies support the inclusion of each aspect of the two proposed modifications clearly. Conclusions are well-founded and justified by the experimental results.", "weaknesses": "Overall the work is strong, but there are a couple of areas of improvement. I found that certain figures contained unnecessary details or were difficult to read, while certain aspects of the explanations are unclear or seem contradictory. I also found that the analysis of compression artifacts was somewhat limited. In the \"Questions\" section of this review, I list these areas specifically and make suggestions for improvements.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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": "H61RNKs3Gs", "reviewer": "Reviewer_enTq", "summary": "The authors proposed a novel training strategy that can help to reconstruct 3D scenes from low-quality images.", "questions": "1. How to apply the colmap on low quality images. I think it's not accurate.\n2. What is M in line 265.\n3. Why do you downsample the I and E by 2?\n4. In Eqn. 3, authors used F'M, while in the above contents, authors used F'. What's the difference of them?\n5. No other layers after the fusion features but before the sigmoid?\n6. I guess the M represents the number of points, then how to get fusion features in dimension M?\n7. In the original 3DGS, there is a loss called D-SSIM loss. Does it help to emphasizes directional consistency in the low-frequency feature space? Why do you change it with SCS loss?\n8. The number of points is not fixed. 3DGS will split and clone points. How to you know how many M do you need?", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The proposed method performs better than other compared SOTAs on 3D reconstruction form low-quality images. And the idea that learning a modulation for the position is good.", "weaknesses": "I think the description of the paper is not clear. Please see my following questions.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "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:

Decision: Accept (Poster)