ReSplat: Degradation-agnostic Feed-forward Gaussian Splatting via Self-guided Residual Diffusion
Authors: Youngho Yoon
Kuk-Jin Yoon
Venue: ICLR 2026 Poster (2026)
Keywords: Gaussian Splatting
Diffusion Models
Image Restoration
Primary area: applications to computer vision, audio, language, and other modalities

Abstract: Recent advances in novel view synthesis (NVS) have predominantly focused on ideal, clear input settings, limiting their applicability in real-world environments with common degradations such as blur, low-light, haze, rain, and snow. While some approaches address NVS under specific degradation types, they are often tailored to narrow cases, lacking the generalizability needed for broader scenarios. To address this issue, we propose Restoration-based feed-forward Gaussian Splatting, named ReSplat, a novel framework capable of handling degraded multi-view inputs. Our model jointly estimates restored images and gaussians to represent the clear scene for NVS. We enable multi-view consistent universal image restoration by utilizing the 3d gaussians generated during the diffusion sampling process as self-guidance. This results in sharper and more reliable novel views. Notably, our framework adapts to various degradations without prior knowledge of their specific types. Extensive experiments demonstrate that ReSplat significantly outperforms existing methods across challenging conditions, including blur, low-light, haze, rain, and snow, delivering superior visual quality and robust NVS performance. Code is available at https://github.com/yh-yoon/ReSplat.

Published as a conference paper at ICLR 2026
FEED- FORWARD GAUSSIAN SPLATTING VIA SELF-GUIDED
RESPLAT:
DEGRADATION-AGNOSS
RESIDUAL DIFFUSION
Youngho Yoon Kuk-Jin Yoon Visual Intelligence Lab., KAIST, South Korea (dudgh1733 732, k jyoonn @kaist...........a...ea ac. kr
ABSTRACT
Recent advances in novel view synthesis (NVS) have predominantly focused on ideal, clear input settings, limiting their applicability in real-worid environments with common degradations such as blur, low-light, haze, rain, and snow. While some approaches address NVS under specific degradation types, they are often tai- lored to narrow cases, lacking the generalizability needed for broader scenarios. To address this issue, we propose Restoration-bes based feed-forward Gaussian Splat- ting, named Selala,, novel framework capable of handling degraded multi-view inputs. Our model jointly estimates restored images and gaussians to represent the clear scene for NVS. We enable multi-view consistent universal image restoration by utilizing the 3d gaussians generated during the diffusion sampling process as self- self-guidance This results in sharper and more reliable novel views. Notably, framework adapts to various degradations without prior knowledge of their spe- eific types. Extensive experiments demonstrate that ReSplat significantly outper- forms existing methods across challenging conditions. including blur, low-light, haze, rain, and snow, delivering superior quality and robust NVS performance. Code is available at pettps://gitaee com/ yohyyoon/ReSplal
INTRODUCTION
Novel View Synthesis (NVS) is a task aimed at generating novel views of a scene from a known set of views. NVS strives to accurately estimate the geometry and appearance of a scene, enabling the rendering of realistic images from unseen perspectives. In recent years, Neural Radiance Fields (NeRF) Mildenhall et al. (202)) have revolutionized NVS by utilizing neural networks to repre- sent scenes in continuous volumetric format, producing highly realistic results. However, NeRF's slow rendering speed has limited its practicality, especially in real-time applications. Solutions like InstantNGP Müller et al. (2022) and TensoRF Chen et al. (2022) have addressed these speed limita- tions, and Gaussian Splatting Kerbl et al. (2023). introduced later, further accelerated the rendering process. By representing scenes with Gaussian ellipsoids instead of dense point samples like NeRF, Gaussian Splatting maintains competitive visual quality while enabling faster rendering.
Despite the impressive results of NeRF and Gaussian Splatting, generalizable approaches have be- come a major focus area. Generalizable NeRF aims to synthesize new views without retraining on each new scene, enhancing model flexibility across diverse datasets Waan et al.] (202T): Yu et al. (2021b): Wang et al. (2022b); [Suhail et al. (2022). Similarly, generalizable Gaussian Splatting ex- tends this concept, offering a faster and adaptable solution for unseen scenes Charatan et al. (2023); Chen et al. (2025); Liu al. (2025): Ziwen et al. (2024). However, these methods have primarily been developed to work on clean multi-view images captured from controlled environments.
Against this backdrop, scene reconstruction using corrupted images has gained attention. Some studies Ma et al. (2022); Wang et al.] (2022a); Yoon & Yoon (023); ); Wang et al. (003)); Chen et al. (202bb) designed handle specific types of corruption. GAURA Gupta al. (2024), the other hand, leverages the capacity of feed-forward NVS models to be pre-trained on large multi- view datasets, proposing generalizable NeRF model that operates under variety of degradations.

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Published as a conference paper at ICLR 2026
Residual Diffusion Model
Universal Image Restoration
Various Corruptions
Update
Geometry
Update Radiance
Feed-forware
Ught
Blur
Rain
Haze
Gaussian Splat
Figure/ 1: Proposed degradation aggnostic feed-forward Gaussian Splatting (GS) framework. Our framework achieves perforrmree universal image restoration and novel view synthesis results through mutual information exchange between the universal image restoration model and the gener- alizable GS model.
However, GAURA excludes the image restoration capabilities developed in the 2D domain, which limits its performance potential.
Simply adopting an image restoration model does not fully address this limitation. Universal image restoration is severely ill-posed problem, with countless possible solutions. This has led to various methods using denoising diffusion models modees-oddmiineeee prosmmenmmintttttme examples of stochastic models- to address image restoration Fei et al. 22023; Özdenizci & Legenstein (2023). Research has shown that training to predict residual images enables effective image restoration Zhang et al. 2017] 2018): Zamir et al. (021 Anwar & Barnes 20200, enhancing performance through diffusion- based residual learning.
In this paper, we propose new generalizable gaussian splatting framework, ReSplat, aimed at degradation-agnostic novel view synthesis. At the core of framework is method that leverages the model priors of diffusion-based unified image restoration network through Gaussian splatting. Unlike NeRF's representation, Gaussian splatting uses a point-based representation that enables ex- plicit scene geometry extraction during training. As shown in Fig. [ generalizable Gaussian splat- ting models Charatan al. (202)); Chen et al. (2025) Liu
(2025 inherently estimate Gaussian centroids (geometry) using multi-view stereo (MVS) and radiance (color) through multi-vier mil image aggregation. In our framework, diffusion model iteratively estimates Gaussian centroids, or 3D geometry, leveraging this information to achieve 3D-consistent image restoration.
Our framework specifically adapts a 3D cross-attention module to the residual diffusion model, enabling it to utilize the location information of Gaussian centroids. Here, Gaussian centroids are derived from the point clouds of restored images estimated the previous time-step. Second, our model performs multi-view aligned pre-filtering when generating Gaussian ellipsoids. This process involves calculating weight map that is applied to the image features used to generate the Gaussian ellipsoids, helping to achieve artifact-free novel view synthesis. Through these techniques, model retains the advantages of a generalizable method that operates without a scene optimization process, working effectively even in sparse-view settings while remaining degradation-aanossicc. This makes it a more practical NVS model, demonstrating superior NVS and image restoration performance in multiple degradation settings compared other approaches.
In summary, our contributions are summarized as follows:
1. We propose ReSplat, a novel framework for multi-view image restoration using 3DGS, We introduce multi-view aligned denoising diffusion model for universal image restoration. 3. Our method outperforms other methods in novel view synthesis and image restoration tasks.
RELATED WORKS
2.1 GENERALIZABLE RADIANCE FIELDS
Generating realistic images has been a central research topic for many years. Neural scene represen-
tations, such as Neural Radiance Fields (NeRF) Mildenhall et al. (2021), have emerged as effective solutions for view synthesis, achieving remarkable results. Subsequent NeRF-based approaches have further improved rendering quality Roessle et al. (2022); Wei et al. (2021): Deng et al. (2022), as well as optimization and rendering speed Sun et al. (2022) Chen et al. (2022); Pridovich- Keil
et al. 2022;; Yu al. (2021a): Müller al. 2022) However, NeRF still requires optimization for
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Published as a conference paper at ICLR 2026
each new scene to synthesize novel views. To address this, various studies have proposed general-
izable NeRF models Yu et al. (2021b): Wang et al. (202I): Liu et al. (2022); Wang et al. (002b);
Suhail et al. ) Cao et al. (2022), enabling cross scene generalization by learning view in- terpolation function from source images. In these generalizable NeRFs, a common technique is to apply volume rendering for aggregating information from images, such deep features, depth maps, or cost volumes Wang et al. (202 [);[Lu et al. ; et al. (2022); CChen et al. (202I); Xu al. (2023). GPNR /uhail al. (2022) and GNT Wang et al. (202bb) utilize transformers to aggregate features, enhancing information interaction along ray to directly predict RGB values for each pixel. Yoon et al. (2024) enhances rendering quality by utilizing geometry- driven multi- reference texture transfer. PixelSplat Charatan et al. (2023) and MVSplat Chen et al. (2025) propose generalizable volume rendering techniques that utilize scene parameterization with 3D Gaussian primitives Kerbl et al. (2023). We note that existing generalizable radiance fields have predomi- nantly been studied on clean images, and we aim to address this limitation by developing a universal model utilizing residual diffusion model.
2.2 NOVEL VIEW SYNTHESIS WITH DEGRADATIONS
Some research has advanced novel view synthesis (NVS) by leveraging radiance fields with physics- based multi-view geometry techniques, targeting cases where train-view images require enhance- ment. NeRF-W Maartnn-ruulli et al. (202T) addresses variations in illumination and transient oc- clusions by relaxing strict assumptions on consistency across inputs. Deblur-NeRF Ma et al. (2022) introduces a spatially-varying blur kernel model to handle blurry inputs effectively. CROP Yoon & Yoon| (023) proposes a cross-guided optimization of radiance fields paired with multi-view im- age super- resouttono for high-resolution NVS. RawNeRF Mildenhall et al.] (2022) facilitates high- dynamic range (HDR) view synthesis by training NeRF on raw input data and generating raw-format outputs. Similarly, HDR-NeRF Huang et al. (2022) supports exposure control and HDR image syn- thesis by learning two distinct implicit functions: one for the radiance field and another for tone mapping. LLNeRF /Wang et al. (2023) and Aleth-NeRF Cui et al. (2024) conducted rescarch on novel view synthesis under low-light conditions. More recently, DiET-GS Lee & Lee (2025) and DiSR-NeRFLLee et al. (2024) leverage diffusion priors to improve 3D representations from degraded inputs, but they are designed for specific corruption types such as motion blur or low resolution. HQGS Lin et al.] and RobustGS Wu et al. (2025) further study Gaussian Splatting under various degraded conditions and propose task-specific 3DGS pipelines to boost robustness in these scenar- ios. Overall, these studies do not explicitly leverage a pretrained universal image restoration model and remain tailored to specific degradation regimes or 3D configurations. In contrast, we target a degradation-agnan aggossic framework that actively uses pretrained universal restoration prior within a feed-forward 3DGS pipeline, so that a single model can handle diverse and mixed degradations.
2.3 UNIVERSAL IMAGE RESTORATION
Developing a unified model capable of handling multiple degradations has become a growing area of interest. AiRnet Li et al. (2022) introduces module to align various distributions into shared distribution using contrastive learning, though this approach can be challenging to train and may limit performance. IDR Zhang et al. 2023) identifies that distinct degradation types can be sepa- rated using singular value decomposition (SVD), allowing for clean image reconstruction through reformulation of singular values and vectors. PromptiR [Potlapalli et al. (2024) enhances perfor- mance by employing a prompt block to capture degradation-specific features. multi-task DINO- based restoration Lin al. (2023) and mask-based blind restoration Qin et al. (2024) exploit strong visual priors from foundation models. Adair (Cui et al. (2025) and Perceive- TR Zhang et al. (2025) further improve all-in-one restoration by adaptively modeling degradation-specife frequency cues and enhancing degradation perception. respectively. Recent works have also focused on complex distortions, such as SphereSR Yoon et al.] (2022) for spherical super-resolution with arbitrary pro- jections, and adverse weather removal via spectral-based methodJeong et al. (2025). Methods such as ProRes Ma et al. (2023) and DA-CLIP Luo et al. (2023) leverage prompt learning to fully utilize the power of large-scale models. Daclip-IR Luo et al. (2024) incorporates a CLIP-based encoder to identify degradation types, extracting semantic information from distorted images to guide a dif- fusion model in generating high-quality outputs. DiffUIR Zheng et al. (2024) introduces selective hourglass mapping to adapt residual denoising diffusion models et al. (2024) as a comprehensive image restoration approach.
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3 METHODS
Forward Process (Eq.9)
Skip Connection
Multi-View Image
Restoration Output
Iin & 1t
PO
(Sec. 3.3)
|Sec. 3.4)
Feed-forward Gaussian Splatting Model
Predicted 3D Gaussians
Figure 2: The overall framework for degaradation-agnostt feed-forward gaussian splatting (GS). A diffusion-based image restoration model restores the original image by iteratively estimating the residual image. During this process, feed-forward GS is performed using the original image gener- ated in the intermediate stages of diffusion sampling. By utilizing the Gaussian points information obtained in this process, the diffusion model receives multi-view information in the next diffusion step, enabling more accurate image restoration.
In this section, we provide an overview of the residual denoising diffusion model (RDDM), univer- sal RDDM, and 3D Gaussian splatting as preliminaries. Next, we propose an overall framework for ietrauddttionaggnosti feed-forward Gaussian splatting. Additionally, we detail two modules specifi- cally designed to enhance NVS performance.
3.1 PRELIMINARIES
3D Gaussian Splatting 3D-GS Kerbl et al. (2023) models scene using collection of anisotropic
3D Gaussians, which retain the differential characteristics of volumetric representations while en- abling efficient rendering through a tile-based rasterization approach. Beginning with points derived from Prnncture-roo-Mottonn (SfM), each point serves the position (mean) js of 3D gaussian elipsoids.
G(x) = (==--)>>>>> (
where represents specific point in the 3D scene, and Σ is the covariance matrix of the 3D Gaussian. is constructed from a scaling matrix S and a rotation matrix R with the equation RSST RT For performing tile-based rasterization, the 3D Gaussians G(z) are projected onto the image plane as 2D Gaussians Gz)z). The rasterizer then sorts these 2D Gaussians and applies alpha blending:
C(r) = Σ co. [ o,), == a,=,)(()""))
x' represents the queried pixel position, and N denotes the number of sorted 2D Gaussians associ- ated with that pixel.
Feed-forward 3D Gaussian Splatting While vanilla 3DGS optimizes Gaussian parameters per scene, recent feed-forward 3DGS models Liu et al. (2025) predict them in a single forward pass from few posed views. Given N input images (% Fi and their camera poses N Ji=ts the
Jill
network á maps multi-view features to per-pixel Gaussian primitives:
Φ: ((Ti, N - (Ugg, Eg, aj, ())
where EHj.Ej, aj. G3) denote the center, covariance, opacity, color of candidate Gaussians. These predictions are then pruned and merged into the explicit Gaussian set pi which is rendered using the standard splatting formulation above. In addition, the feed-forward 3DGS predicts per- view aggregation weights W that are used to combine warped multi-view features at each novel- view pixel; in Sec. 3.4 modulate these weights with degradation-aware pre-filtering module.

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Algorithm Training
Algorithm 2 Sampling
Input:
Clean image, Degraded image: Lo, lini
Require: Degraded image: Lini
GT novel view image: Invi
Image Restoration Model: (*));
GT residual map: Les In Io:
Feed-Forward GS Model: ((*):
Image Restoration Model: (());
1: é ~ NNO,
Feed-forward GS Model: ((+);
2: IT = (1- -OT) + Bre;
1: repeat
Po None:
2:
To (To)::
4: for t = .T.... do
3: Pe 6(Io,Inn
if > then
4:
1~ Uniform(1..... T);
4-1 = T1 ar Tee (Po: Ie, Tin; 1)
6:
5:
+ +dllini
6:
7:
Take the gradient descent step on
4, = aglres + Bic 8,lmi
7:
Po = 1 Lin):
8:
else
Tell 1 (Po Tt, Tins 1)
9:
11-1 = Inn 1 (Po Ibs lins L);
TT Tee (m T 11
10:
Lnv Lien (Lio Les In):
11:
return Jo. Inn
8: until converged
Residual Denoising Diffusion Model RDDM Liu et al. (2024) uses standard T T-step diffusion model that includes both a forward and a reverse process. the forward process, one-step noising is formulated as Markov chain:
4 = atces
arless 3,1)
(4)
where ar and 3e the noise coefficients for Tres and gaussian noise. I the result timestep and lee represents the residual between the degraded image Jn and the clean image To. with Jes = In Lo- the reverse process, RDDM approximates the true generative distribution pe (fc-1 by using q(44-1 144 lo- ), which is also formulated as a Markov chain when deterministic implicit sampling using DDIM Song et al. (020):
po N (T--1: 18 + a. The.
+ B, B,-15 0
(5)
In summary, the relatios between and 4-1 in both the forward and reverse processes is as follows:
1. 1,-1 + actres + BLET-1
(6)
4-1 4 (ā, ā a - (3. 1)
Universal Residual Denoising Diffusion Model DiffUIR Zheng et al. (2024) utilizes the condition- ing mechanism from RDDM and incorporate shared distribution term (SDT) within the diffusion algorithm for universal image restoration. They adjust the forward process as follows:
= 1--1 + agtres + B 8e4mn
where J represents the SDT, and 8 is the shared distribution coefficient. The reverse process is also as follows:
4-1 I, ar + 8,lin (Bf/B.)e
(9)
Finally, in the deterministic implicit sampling process, I--1 and the pseudo clean image Ig can be derived using the following equation:
T = Ig + a. a.-1 - -in s.t. I0 -In 1
(10)
3.2 OVERALL FRAMEWORK
We aim to develop a novel view synthesis model that can be performed under arbitrary degradation. Most of the existing novel view synthesis studies have been conducted on clean images without cor- ruption, and even the case of studies on situations with corruption. models specialized for specific degradation types are being developed Ma et al.) (222 Wang et al. (2022a); Yoon & Yoon (202)) Wang et al. (202);; Chen et al. (2023b). These studies solve the problem by simultancously opti- mizing optimization and physical characteristies by implementing physical characteristics that
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of views
Attention
11a
1%
gausstans
Wiihht Prcette
(W Jrual Row1
Fg
Hurp
...
...
Hears
pra Fiv
W3
Self Atsention
Wire
Figure 3: GS-guided multi-view alignment. Module embedded in the residual diffusion model that shares info between adjacent views using Gaussian centers.
Figure 4: Pre-filtering with warped features. Warped inputs are self-attended to form pre- filtering weights for feature aggregation.
cause specific degradation as a rendering process. Therefore, there is a need for new degradation- agnostic novel view synthesis (NVS) studies. To address this, propose new framework, ReSplat, the NVS model that leverages the diffusion prior studied in the field of 2D image restoration.
Training stage As shown in Fig. we combine the feed-forward gaussian splatting (GS) model and the universal image restoration (URR) model. The GS and UIR models support complementary roles. Unlike NeRF, ferd-forward GS inevitably performs multi-view stereo (MVS) within the model be- cause it needs to explicitly extract point clouds. This enables acquisition of 3D scene geometry information and helps the UIR model find corresponding points for adjacent multi-view images. Meanwhile, the UIR model performs degradation-agnostic image restoration to help feed-forward GS perform NVS using images with corruption removed. In addition, we adopt DIfTURR Zheng et al. 2224), a diffusion model-based UIR model, to perform iterative scene geometry extraction and iterative image refinement so that the UIR results can be gradually refined. The training process can be found in Algorithm I The first term lcs Tess( (Po It, Im 1)]l, corresponds to the universal restoration loss LUIR. which supervises the residual prediction of the UIR model 6. The second term Ino Te (In I [n)]]l defines the novel-view rendering loss LNv, which supervises the feed-forward GS model é using the ground-truth clean novel view Inn-
Sampling stage The specific sampling process of ReSplat is shown in Algorithm 2 Given N multi- view input images [ J the 1th noise images [ i=1 are generated according to the forward process of the DiffUIR. We generate predicted clean images {Tol from predicted {Tfes I by the UIR model. The predicted clean images are used to generate explicit point cloud P by the MVS module of the foed-forward GS model. Meanwhile, we generate (iin) to perform the next diffusion step. After that, we perform 3d aligned diffusion reverse process using Pe (sec. 3.3 Through this, we regenerate the refined {1i)-1 and Po We repeat the process and perform the feed-forward GS overall process using the finally generated [TA] In this process, we perform remove points where artifacts exist before the multi-view feature aggregation process, thereby generating a more robust GS output (sec. 3.4 Through this, we can obtain rendered output for the novel view point.
feature pre-filtering process conditioned on the original corrupted images [ 1.W,
3.3
GS GUIDED MULTI-VIEW ALIGNMENT
Since the original UIR model is designed for single image. it is necessary to design a module for enabling multi-view image interaction. As shown in Fig. 3 we adapt module that performs feature attention in space to UIR by utilizing P a pseudo geometry generated during the sampling process. Multi-view features are projected toward each gaussian center in P When there N multi-view feature vectors { for the ith center point Pi, we perform self-attention between the corresponding vectors. This process is repeated in the encoder of the diffusion model and helps ensure the 3D consistency of multi-view images. The processed feature vector jirrop is reprojected to the original pixel coordinates. However, since the reprojected point is located in continuous coor- dinates, not discrete coordinates, it is necessary to propagate to the surrounding discrete coordinates. Therefore, we perform a weighted sum by applying 2D interpolation weights [w;] } to all reprojected points existing between adjacent pixels. Each weight is determined by the area of the opposite

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Published as a conference paper at ICLR 2026
Table 1: Novel View Synthesis (NV) and Image Restoration (IR) results of five corruption types LLFF degradation dataset with three multi-view inputs. The best scores and second best scores are highlighted with their respective colors.
Method
Operation
Year
Corruption Type
PSNRCY
Novel View Synthesis
SSIMC)
LPIPSC))
PSNKCC
Molni View Image Restonatio
SSIMEC
LPIPSC)
AaRmet
IR NV
2022
20.11
0.6896
0.4250
21.99
0.7543
0.3709
PrompilK
NV
2023
20.04
0,6822
04208
22.14
0.7526
0,3668
GAURA
DiFUIR
IR NV
Only KV
3024
2024
Motion H
22.15
21.28
0.7198
0.7824
0.3209
0.4343
26.34
0.8640
0.2951
RcSplat
IR NV
2025
23.15
0,8039
0:3151
27.14
D38850
0:27E3
AiRint
IR + NV
2022
20.22
0.6852
0.3026
21.37
0.884
0.2139
Prompil
NV
2023
20-54
0.2067
0.377
23.21
0.8588
0,1912
GAURA
DIFUIR
IR + NV
Only NV
2024
2024
Snow
24.24
20.48
0.7044
0.8549
0.3195
0.1826
31.20
0.9627
0.1019
ReSplat
IR w/ NV
2002
24.46
0,8614
0.1677
32,07
D.9685
0,0886
AiRnns
IR + NV
2022
9.139
0.3841
0.9992
8.871
0.4155
O. 1.2549
PrompiIR
IR NV
2023
9.784
0.4651
0.3508
9.585
0.5251
0.2280
Only
Haze
17:22
0.7205
0:4516
DILUIR
IR NV
2024
21.56
0.8392
0.1857
25.57
0.9612
0.0701
ReSplat
IR w/ NV
20025
21.99
0.8471
0,1750
26.45
0.9680
0,0619
AiRnes
IR + NV
2022
9.526
0.1364
0.6641
6.388
D.8599
O. 0.7654
PrompilK
IR RV
2023
6.367
0,0803
0.0210
6.298
00080S
0.5789
Only
15.28
0.5177
Low-light
DAFLUK
IR Nv
20124
18.87
0.8241
0.2429
21.88
0.9344
0.1647
ReSplal
IR WV
2025
19.76
0.8276
0,2433
22.82
0.9522
0,1605
AiRnes
IR NV
2022
21149
0.79X
D:3416
23.09
D.8018
O. 0.3DTN
PrompilR
IR NV
2023
20.71
0.7175
0.2992
24.78
0.8555
0.2459
GAURA
Only NV
0.4110
DIFFLII
IR NN
2014
Rain
23.51
0,8313
0,2538
29.69
0.9357
0.1919
ReSplal
IR WV
2025
24.11
0,8505
0,2140
31,28
0.9538
0,1535
gion, ensuring that features closer to the query point have a higher influence. Therefore, when there a discrete point q, the multi-view feature Fg that q obtains is as follows.
Fg Σ
wiflrep where i Q
(11)
and Q is the set of the index of all points that exist within the smallest rectangle surrounding the point 9.
3.4 PRE-FILTERING WITH WARPED FEATURES
The final outputs of the UIR, {Ta fi=1: are first depth-warped toward the novel pose using P The forwafo GS backbone then produces per-view aggregation weights fimal 1 for combining the N warped multi-view features at each novel-view pixel. Since these weights have critical impact on determining the radiance of the final Gaussian ellipsoids, we introduce a pre-filterig module that is additionally conditioned on the corrupted inputs {
As illustrated in Fig. 4 the pre-filtering module takes the warped restored and degraded images as input and predicts per-view reliability map {Wis WW p independently of the occlusion-based weights from the GS model. We then modulate the original GS weights by this reliability map:
W fimal (z) = (xiee)) (z) Wi
(12)
and use the updated W fimal in the splatting renderer. In other words, the pre-filtering module acts as a soft, degradation aware gate on top of the standard visibility weights: regions where residual artifacts (e.g., remaining rain streaks, snow blobs, or haze fragments) pre and are fown-weighted while geometry-consisten clean structures more robust radiance field and improved NVS quality. In practice,
strong inconsistent
across views receive lower Wi
preserved, leading simply replace the original per-view aggregation weights W with the updated W These weights then used in its standard multi-view feature aggregation and Gaussian rendering pipeline to determine the contribution of each input view at every novel-view location.
4 EXPERIMENTS
4.1 EXPERIMENT SETTINGS
Datasets For training our model, utilize the synthetic multi-degradationn generation pipeline pro- posed by GAURA Gupta et al. (2024) to construct a multi-view degradation dataset. We use a train- ing dataset provided by IBRNet Wang al. (2021), commonly used in novel view synthesis task.
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Table 2: Novel View Synthesis results and multi-view image restoration results of three types mrain+motion blur, snow+motion blur, and haze+snow) on LLFF mixed degradation dataset with three multi-viens inputs. The best
scores are highlighted.
Method
Cormuption Type
PSNK(T
SSINCT
Synthesi
EPIPSC)
FSNRU
View Image
SSIM(T)
LPIFS(T)
DIFUUR
RaineMotionn Blur
20.07
0.6910
04883
20.41
0,7083
0.4653
ReSplat
20.44
0.7090
0,4555
20.74
0.7220
0.4507
DIfFUIR
Snow Motion Blur
21.63
0.7407
0,4076
22.51
0,775
0,3848
ReSplat
22,00
0.7594
0.3782
22.90
0,7908
0.3661
DifFURR
ReSplat
Haze+Suow
20.17
0.6988
0.7730
0.3458
0,3148
15.32
19:92
0,7702
0,8067
0,2843
0,2808
Haze
Low-light
Snow
Rain
Molion Blur
AlRnet
PromptIR
DIFTUIR
GAURA
Ours
GT
Figure 5: Comparsions of novel view synthesis results of five types (motion blur, snow, haze, low- light, rain) on LLFF degradation dataset.
The test using synthetic degradations applied to the LLFF Mildenhall et al. (2019) dataset. For real-world scenarios, we evaluate our model using the DeblurNeRF Ma et (2022) dataset for motion blur, the REVIDE /Zhang et al. (2021) dataset for haze, and the L..NeRF Wang et al. (2023) dataset for low-light conditions.
divided into synthetic and real-world datasets. The synthetic dataset is generated
Network We use DifFUIR Zheng et al. (2024), residual diffusion model, our baseline for image restoration. also use MVSGaussian Liu et al. (2025), one of the state-of-the-art models, as the feed-forward GS. To accelerate the training process, MVSGaussian is first trained our training dataset without image restoration process.
We conduct comparison of universal image restoration with AiRnet, PrompuR, and DiffUIR. The goal our approach is to develop an adapter that transforms UiR model to handle multi-view inputs. Therefore, utilize network pretrained with single-view UiR. For fair comparison, all models are fine-tuned on our training dataset.
During the inference time, ReSplat uses DDIM sampling strategy with total of three sampling steps fixed. The inference process for the three multi-view inputs can be completed within one second. For more details, please refer to the supplementary material.
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Table 3: Novel view synthesis results of three types (motion blur, haze, low-light) weal-world degradation datasets.
Table 4: Ablation study with Novel View Synthesis re-
sults of 5 types (motion blur, snow, haze, low-light, rain) on synthetic degradations. Values represent the
Medaod
Type
PSNKUC)
Novel View Synthssis
SSIMIT)
LPIESE))
AlKintt
18.59
0.403
average of five degradations. The best scores and
PronsiiR
18.42
0.6239
0.3959
GALRA
Monion Blur
21334
0.7711
0.3909
second best scores are highlighted.
RGSglal
AlRact
15.01
0.7189
0.3290
Frumpilk
10.00
15.16
0.7100
U.M
Model
Alignment
Pre Filsering
FSNRCT
Nowd View Synhhnes
SSIMIT)
LPIPS(D)
22.19
03264
0.2572
Kesplax
X
22.35
0.2990
0.2368
AlKnet
9.336
0.1304
0.000
0.2506
PronpiR
PromsUR
DINUIR
GAURA
Dark
Figure 6: Visual Comparsions of novel view synthesis results of 3 types (motion blur, haze, low- light) on real-world degradation dataset (DeblurNeRF, REVIDE, and L.NeRF dataset).
4.2 QUANTITATIVE ANALYSIS
Synthetic Degradation As shown in Table we evaluate ReSplat against baselines (AiRnet, PromptIR, DiffUIR, GAURA) across five corruption types: motion blur, snow, haze, low-light, and rain, for both novel view synthesis and multi-view image restoration. Performance is measured using PSNR, SSIM, and LPIPS. For novel view synthesis, ReSplat consistently outperforms other methods, especially in motion blur, snow, and rain scenarios, producing sharper, more perceptually accurate views. multi-view image restoration, ReSplat excels in high-corruption cases, achiev- ing the best overall visual fidelity and structural similarity. Notably, it handles complex degradations like heavy rain and motion blur more effectively than competing models, preserving both fine details and global consistency.
Mixed Degradation Table presents a comparison between our method, ReSplat, and the strongest baseline, DiffUIR, under various mixed degradation scenarios, including RainoMotion Blur, Snow+Motion Blur, and Haze+Snow. ReSplat consistently achieves the best performance across all conditions, significantly outperforming DinUIR in both novel view synthesis and multi- view image restoration. We conduct a direct comparison between the top-performing method (Re- Splat) and the next best (DiffUIR) to highlight the effectiveness of approach. Notably, in more challenging scenarios such as Snow+Motion Blur and Haze+Snow, ReSplat delivers considerably higher fidelity, as reflected by higher PSNR and SSIM values and lower LPIPS.
Real-Worke Degradation As shown in Table we evaluate ReSplat against baselines for novel view synthesis under real-world corruptions: motion blur (DeblurNeRF Ma al. (2022)), haze (RE- VIDE Zhang et al.) (2021)), and low-light (LLNeRF Wang et al.] (2023)) dataset. ReSplat achieves the best overall results for motion blur, preserving structural and perceptual quality. In haze, yields the lowest LPIPS, indicating superior perceptual quality despite similar PSNR/SSIM scores with DiffUIR. Under low-light conditions, ReSplat balances structural integrity and perceptual fi- delity, performing competitively across all metrics. In addition. Table 5 and Table results in-the-wild rain (NTURain Chen et al. (2018) and snow (RSVDiChen al. (2023a)) datasets, where ReSplat consistently outperforms UIR and GS baselines, demonstrating robust generalization.

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Published as a conference paper at ICLR 2026
Table 5: Novel View Synthesis (NV) results of Rain corruption on the real-world deraining
Table 6: Novel View Synthesis (NV) results of Snow corruption on the real-world desnowing
dataset with three multi-view inputs. The best
dataset with three multi-view inputs. The best
scores and second best scores are highlighted.
scores and second best scores are highlighted.
Method
Type
PSNR(T)
SSIM(T)
LPIPS(C)
Method
Type
PSNR(T)
SSIM(T)
LPIPS(
AiRnet
24.05
0.8183
0.1955
AiRnet
20.23
0.7035
0.3103
PromptIK
24.23
0.8230
0,1801
Promptl
21.27
0,7192
0,3007
GAURA
Rain
19.39
0.6622
U.3967
GAURA
Snow
20.22
0.7578
0.3647
DIMUIR
0.845
0.2094
DIAUTR
0.8215
0.2277
ReSplat
24.35
0.8232
0.1772
ReSplat
22.45
0.8263
0,2175
Without Alignment
Alignmant
Without Pre-filtering
With Pre-fittering
Figure 7: Qualitative comparison of the align- Figure 8: Qualitative comparison of pre-filtering ment module. The top row shows the restored RGB outputs, while the bottom row visualizes the corresponding error maps.
module. The top row shows the restored RGB outputs, while the bottom row visualizes the cor- responding error maps.
4.3 QUALITATIVE COMPARISON AND ANALYSIS
Synthetic Degradation As shown in Fig. 5 our method surpasses existing baselines across all tested degradations. It achieves higher visual fidelity and color accuracy, especially in challenging haze and low-light scenarios, closely matching the ground truth.
World Degradation As shown in Fig. our method robustly handles real-world degradations, producing sharper results with superior color fidelity. Unlike competing approaches that introduce noise, ours effectively reconstructs fine structures in challenging scenarios.
4.4 ABLATION STUDIES
We conduct ablation study using four model variants: Model baseline, Model 2 with pre- filtering, Model 3 with alignment, and Model with both. Metrics are averaged over five LLFF degradation datasets. As shown in Table 4 each component contributes to improved performance in novel view synthesis. Adding pre-filterng (Model 2) increases PSNR and reduces LPIPS, indicating modest improvement in reconstruction quality. Alignment alone (Model further enhances PSNR and other quality metrics compared to the baseline. When both alignment and pre-filtering are applied (Model 4), the model achieves the best overall results, with PSNR of 22.69, demonstrating clear effect across various degradations. Qualitative comparisons in Fig. and Fig. 8 illustrate that alignment reduces multi-view geometric inconsistencies. while pre-filtering suppresses residual artifacts without destroying fine structures in the rendered novel views.
5 LIMITATIONS
Despite its performance, ReSplat has several limitations. The diffusion refinement adds computa- tional and memory overhead, requiring further optimization for high-resolution scaling. Addition- ally, it inherits 3DGS's biases in handling specularities and transparency. While dependent on pretrained restoration prior, this modularity allows ReSplat to benefit from future restoration ad- vancements without architectural changes.
6 CONCLUSION
We present a feed-forward gaussian splatting framework for degradation-agnon novel view syn- thesis. By integrating residual diffusion model with 3D cross-attention and multi-view pre- filtering, our method robustly restores images and improves geometry estimation, outperforming existing approaches in both novel view synthesis and universal multi-view image restoration.
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ACKNOWLEDGEMENTS
This work was supported by the Technology Innovation Program (2410013617:200243355 Devel- opment of autonomous driving connectivity technology based on consor-infrastructure cooperation) funded By the Ministry of Trade, Industry & Energy(MOTTE, Korea) and the Institute of Infor- mation & communications Technology Planning & Evaluation (IITP) grant funded by the Korea (MVT) (No. RS-2024-00457882, Al Research Hub Project).
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Reviews:
[{"review_id": "tjl1QohFDC", "reviewer": "Reviewer_FesE", "summary": "The paper introduces a method, ReSplat, for feedforward NVS when input images are degraded in various ways, ReSplat leverages the feedforward 3D Gaussian Splatting method MVSplat[1] and an universal residual diffusion model DiffUIR[2] to solve the problem. The authors propose using Gaussian points information in the Diffusion encoder to promote 3D consistency and a pre-filtering technique to eliminate residual artifacts.", "questions": "1. I don't understand how exactly Gaussian points information are used in the diffusion model. After per-point features obtained in Sec 3.3, are they using as conditional features in the diffusion model? I suggest outlining the attention and diffusion encoder equations. \n2. What are the memory consumption and training time of ReSplat?", "limitations": "", "rating": 4, "confidence": 4, "soundness": 2, "presentation": 2, "contribution": 2, "strengths": "1. The topic of NVS with degraded inputs the paper focuses on is important and interesting.\n2. ReSplat outperforms other methods in both quantitative and qualitative results.\n3. The experiments are comprehensive, evaluating on both synthetic and real-world image degradations.", "weaknesses": "1. My main concern is the effectiveness of the proposed 3D alignment module and pre-filtering technique, as the improvements in Table 4 ablation study are moderate.\n2. No qualitative figure in ablation study is provided. \n3. A limitations section should be discussed and added.", "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": "jeMFUusS5Q", "reviewer": "Reviewer_FMUU", "summary": "This paper proposes novel framework that can synthesize clean novel-view images given by the corrupted multi-view images. The model can construct clean 3D representation from images with diverse types of degradation by combining diffusion-based UIR model and feed-forward 3DGS.", "questions": "Refer to the *Weaknesses* section.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "* The motivation of this paper is explained clearly in the introduction part. Most of the previous literatures in the *Novel View Synthesis with Degradations* are limited to certain types of degradation and can't be generalized to corruptions that are net seen in the training dataset. GAURA attempted to mitigate this limitation by constructing degradation-aware generalizable NeRF but didn't leverage the prior knowledge of pretrained 2D UIR model. ReSplat points out those limitations which are important enough for the practicality of 3DGS.\n* ReSplat can be regarded as combination of DiffUIR and MVSGaussian. However, paper also proposes two novel modules in Sec. 3.3 and Sec. 3.4 to improve the aggregation of information from multi-view images. I believe Sec. 3.3 is more notable contribution where they explicitly fuse the features from matching points across multi-view images, thereby aggregating the multi-view information effectively. This is common practice in feed-forward 3DGS [c], but there is novelty to use this technique for 3D-aware denoising process.\n* Experiments are conducted on both synthetic and real-world datasets with multiple types of degradation. ReSplat consistently shows the performance improvement compared to the baselines.\n\n---\n\n### References\n\n[c] Charatan, David, et al. \"pixelsplat: 3d gaussian splats from image pairs for scalable generalizable 3d reconstruction.\" Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2024.", "weaknesses": "### Weaknesses\n\nThere are some parts that technical details are missing.\n\n* In the preliminary, the author explain about the original 3DGS. However, I believe author should explain the brief summary for feed-forward 3DGS (like MVSGaussian) instead of standard 3DGS since they used the feed-forward 3D model in this work. This explanation of the feed-forward model will also aid understanding in the later sections, such as Sec. 3.4. It seems that the writing assumes readers are already familiar with generalizable 3DGS models, such as MVSGaussian and skips a lot of technical details in the main paper.\n\n* In the Algorithm. 1, the novel view image $I_{nv}$ is used in line 7 but there is no explanation about this part in the main paper. Is this rendering loss conducted between rendered image and ground-truth clean image? Furthermore, detailed explanations about training with objective terms are missing in the main text where all of them are briefly summarized into Algorithm. 1.\n\n* It is unclear that how the *Pre-filtering with warped features* operates. How does the outputted $[W^i_{pre}]^N_{I=1}$ contribute to the final weights map? Furthermore, the motivation of this module is also hard to understand. In the L85-86, the paper explained that this module can assist to achieve *artifact-free* novel view synthesis. However, it is hard to grasp the relationship between the terms of 'artifact-free' and the operations of this module. \n\n* What does the operation 'IR → NV' in Tab. 1 mean? How are the single-image IR methods such as DiffUIR evaluated on novel view synthesis? According to L372-374, it seems that author adopted additional adapter to transfer single-image IR models into multi-view settings. Is it correct?\n\n* Author can diversify the baselines used in the comparison. For example, the most naive solution of achieving 3D reconstruction with UIR is: 1) Restore the corrupted multi-view images with pretrained 2D UIR method. 2) Reconstruct the clean 3DGS by using the restored multi-view images and pretrained feed-forward 3DGS. Author should compare the performance of ReSplat with this naive two-stage framework.\n\n* How many input views are used during the evaluation? How many views can ReSplat handle? Please specify the evaluation details.\n\n* There are previous literatures [a, b] that tried to use diffusion prior to construct clean 3D representation from the degraded images. However, all of them are limited to certain types of degradation (low-resolution or motion blur). It is better to cite those papers in the *Related works* since they are closely related with this paper in terms of using diffusion model for the 3D-IR task.\n\n---\n\n### References\n\n[a] Lee, Seungjun, and Gim Hee Lee. \"DiET-GS: Diffusion Prior and Event Stream-Assisted Motion Deblurring 3D Gaussian Splatting.\" Proceedings of the Computer Vision and Pattern Recognition Conference. 2025.\n\n[b] Lee, Jie Long, Chen Li, and Gim Hee Lee. \"Disr-nerf: Diffusion-guided view-consistent super-resolution nerf.\" Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2024.", "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": "VyYfeCDv5V", "reviewer": "Reviewer_AE9N", "summary": "The paper introduces ReSplat, a novel framework for degradation-agnostic NVS. Unlike prior methods that either assume clean inputs or focus on specific degradation types, ReSplat integrates 3D Gaussian Splatting with a residual diffusion-based universal image restoration module. The approach jointly estimates restored multi-view images and explicit scene geometry through Gaussian splats, enabling multi-view consistent restoration and sharper novel view generation.", "questions": "1. The real-world evaluations are limited to blur, haze, and low-light datasets. How would the method perform under more diverse real-world degradations such as snow or rain in the wild?\n2. The paper reports efficient inference but does not discuss training cost. What are the training resources (e.g., GPU hours, memory usage) required, and how do they compare with baselines like DiffUIR?\n3. The method claims to be degradation-agnostic. How well does ReSplat generalize to unseen or compound degradations that were not included in training?\n4. The contribution seems more about integration than proposing fundamentally new algorithms. Can the authors clarify which parts of the framework they view as the key novel technical contributions, beyond combining restoration and Gaussian splatting?\n5. The ablation studies only vary internal modules but still rely on strong SOTA backbones for restoration and NVS. How would the framework perform if weaker restoration networks or weaker NVS models were used in place of the chosen SOTA baselines?\n6. Several important recent works on NVS under degraded scenes [1,2] and unified image restoration [3–6] are not discussed. \n[1] HQGS: High-Quality Novel View Synthesis with Gaussian Splatting in Degraded Scenes.\n[2] RobustGS: Unified boosting of feedforward 3D Gaussian Splatting under low-quality conditions.\n[3] Adair: Adaptive all-in-one image restoration via frequency mining and modulation.\n[4] Perceive-IR: Learning to perceive degradation better for all-in-one image restoration.\n[5] Multi-task image restoration guided by robust DINO features.\n[6] Restore Anything with Masks: Leveraging Mask Image Modeling for Blind All-in-One Image Restoration.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. Combining Gaussian splatting with a residual diffusion model is conceptually new and addresses degradation-agnostic NVS in a unified way. And the proposed models have strong performance than competitive baselines (DiffUIR, GAURA) across both synthetic and real-world degradations, with consistent improvements in PSNR/SSIM/LPIPS.\n2. Evaluation covers multiple degradation types (blur, haze, low-light, snow, rain) and mixed-degradation scenarios, showcasing robustness.\n3. The method maintains practical inference speed (under one second for three views), which is important for deployment.\nAblation study: Clearly shows the effect of multi-view alignment and pre-filtering, validating each module’s importance.", "weaknesses": "1. Real-world evaluations are restricted to blur, haze, and low-light datasets. Claims of degradation-agnostic performance would be stronger with more diverse real-world tests (e.g., snow/rain in the wild).\n\n2. While inference is efficient, the paper does not provide sufficient detail about training cost (e.g., GPU hours, memory usage). For diffusion-based models, this is important.\n\n3. Although the model claims to be degradation-agnostic, it is unclear how well it generalizes to unseen or compound degradations not present in training.\n\n4. The core innovation lies more in integration than in fundamentally new algorithms. Some may find the contribution incremental.\n\n5. The ablation studies are weak, the model are constructed by the SOTA restoration network and NVS network, how the framework performs when changing these parts with other weaker restoration and NVS networks?\n\n6. Some important NVS in low-quality scene [1,2] and image unified image restoration [3,4,5,6] papers are lacking.\n\n[1] HQGS: High-Quality Novel View Synthesis with Gaussian Splatting in Degraded Scenes. \n[2] Robustgs: Unified boosting of feedforward 3d gaussian splatting under low-quality conditions.\n[3] Adair: Adaptive all-in-one image restoration via frequency mining and modulation. \n[4] Perceive-ir: Learning to perceive degradation better for all-in-one image restoration\n[5] Multi-task image restoration guided by robust DINO features.\n[5] Restore Anything with Masks: Leveraging Mask Image Modeling for Blind All-in-One Image Restoration.", "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": "qwio5ctb2Y", "reviewer": "Reviewer_26dS", "summary": "1. **Originality-wise**: The core idea of creating a synergistic loop between a universal image restoration model and a feed-forward Gaussian Splatting model is highly novel. Specifically, using the intermediate 3D geometry from the GS model to enforce multi-view consistency in a diffusion-based restoration process is a clever and previously unexplored mechanism for this problem.\n2. **Quality-wise**: The claims are strongly supported by comprehensive experiments across a wide array of synthetic, mixed, and real-world degradations. The method consistently achieves state-of-the-art results in both novel view synthesis and image restoration tasks, demonstrating the framework's robustness and effectiveness. \n3. **Clarity-wise**: The paper is well-structured and clearly articulates a complex problem and its solution. The overall framework is well-illustrated with diagrams that effectively convey the interplay between the restoration and synthesis modules. The motivation, methodology, and results are presented logically and are easy to follow.", "questions": "Please refer to Weaknesses part.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "1. **Solves a Novel and Practical Problem**: The work addresses degradation-agnostic novel view synthesis, a topic of high practical importance for real-world applications that has been underexplored compared to synthesis from clean images.\n\n2. **Innovative Synergistic Framework**: The core contribution is a novel framework where an image restoration model and a Gaussian Splatting model work in tandem. The use of 3D geometry from the GS model to guide the UIR model and ensure multi-view consistency is a key innovation.\n\n3. **SOTA Performance and Thorough Validation**: The method demonstrates superior performance on both novel view synthesis and image restoration tasks across a comprehensive set of experiments, including single, mixed, and real-world degradations.", "weaknesses": "1. Ambiguous Mechanism and Potential for Detail Suppression in the Pre-filtering Module: The paper proposes a pre-filtering module to suppress artifacts but provides insufficient insight into its inner workings. The mechanism, which uses self-attention on both the corrupted and restored images, is a black box. It is unclear whether the module learns to identify specific \"restoration artifacts\" or if it simply learns to penalize any high-frequency regions that differ significantly from the degraded input.\n\n2. the complete absence of failure cases is a significant omission for a paper claiming such robust, \"agnostic\" capabilities. A rigorous scientific contribution requires a transparent discussion of a method's limitations. \n---\nI have listed my concerns, and the score will be adjusted based on the author's response.", "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)