S2R-HDR: A Large-Scale Rendered Dataset for HDR Fusion
Authors: Yujin Wang
Jiarui Wu
Yichen Bian
Fan Zhang
Tianfan Xue
Venue: ICLR 2026 Poster (2026)
Keywords: HDR Dataset
HDR Fusion
Domain Adaption
Primary area: datasets and benchmarks

Abstract: The generalization of learning-based high dynamic range (HDR) fusion is often limited by the availability of training data, as collecting large-scale HDR images from dynamic scenes is both costly and technically challenging. To address these challenges, we propose S2R-HDR, the first large-scale high-quality synthetic dataset for HDR fusion, with 24,000 HDR samples. Using Unreal Engine 5, we design a diverse set of realistic HDR scenes that encompass various dynamic elements, motion types, high dynamic range scenes, and lighting. Additionally, we develop an efficient rendering pipeline to generate realistic HDR images.
To further mitigate the domain gap between synthetic and real-world data, we introduce S2R-Adapter, a domain adaptation designed to bridge this gap and enhance the generalization ability of models. Experimental results on real-world datasets demonstrate that our approach achieves state-of-the-art HDR fusion performance. 
Dataset and code are available at https://openimaginglab.github.io/S2R-HDR.

Published as a conference paper at ICLR 2026
S2R-HDR: A LARGE-SCALE RENDERED DATASET FOR HDR FUSION
Yujin Wang'; Jiarui Wu²!; Yichen Bian 14; Fan Zhang', Tianfan Xue
'Shanghai AI Laboratory,&CUHK MMLab, CPPI under InnoHK, *Shanghai Jiao Tong University
(wangyujin, bianyichen, zhangfan [hangfan)@pjjab..... [wj024, tfxue]fie. www.kccckkk.ukke hpwpeppppl.b........o.oooo
ABSTRACT
The generalization of learning- high dynamic range (HDR) fusion of- ten limited by the availability of training data, as collecting large-scale HDR images from dynamic scenes is both costly and technically challenging. To ad- dress these challenges, we propose S2R-HDR, the first large-scale high-quality synthetic dataset for HDR fusion, with 24,000 HDR samples. Using Unreal Engine 5, we design diverse set of realistic HDR scenes that encompass var- ious dynamic elements, motion types, high dynamic range scenes, and light- ing. Additionally, istic HDR images. To further mitigate the domain gap between synthetic and real-world data, introduce S2R-Adapter, domain adaptation designed bridge this gap and enhance the generalization ability of models. Experimen- tal results real-world datasets demonstrate that approach achieves state- of-the-art HDR fusion performance. Dataset and code are available at https: //cpenimaginglaab github. io/ 02R22R-- HDR
develop efficient rendering pipeline to generate real-
INTRODUCTION
High dynamic range (HDR) fusion plays crucial role in various real-world applications, such as computational photography, visual perception, and autonomous driving. Despite notable advance- ments in HDR fusion techniques Yan et al. 2019: Kalantari & Ramamoorthi 2017 Liu ct al. [022]
Tel et al. 20233 Kong et al. (024) in recent years, models trained on small-scale datasets (Kalantari
& Ramamoorthi] 2017] Chen et al. 2021 Kong al., 2024 Tel et al. 2023) still face limitations in generalizing to complex scenes. Additionally, due to limited data scale, the complexity and chal-
lenges of HDR fusion have yet to be fully explored, particularly in scenarios involving large motion and direct sunlight, as illustrated in Figure
In real-world scenarios, collecting comprehensive, high-quality large-scale HDR datasets for dy- namic scenes is cime-connuming, resource- innturccinttensive, and poses significant technical challenges. Un- controllable elements such as lighting conditions, weather variations, and dynamic objects like ani- mals and vehicles make it difficult to fully control the data acquisition process. Capturing extreme high dynamic range scenarios suciiissssscshs--- as environments with direct sunlight poses an even greater challenge. like Figure []] Consequently, existing HDR datasets (Kalantari & Ramamoorthi [207]] Tel et al. 20233 Kong et al. 2024] Chen al. 2021] Shu et al., (004) are generally limited to arti- ficially controlled dynamic scenes and fail to capture the diversity of real-world environments. For example, some datasets focus exclusively on human motion, overlooking other essential dynamic elements, such animals and vehicles. Moreover, existing HDR datasets with ground truth fusion results are typically small. For instance, Kong et al. (2024) built the latest dataset with 123 samples. Models trained on these small datasets are prone overfitting, limiting their performance under challenging scenarios. A larger synthetic dataset (Barua et al. (025)) has been proposed recently for the single-image LDR to HDR conversion task, which intrinsically limited by the absence of complementary exposure information, resulting in unrecoverable details in saturated regions.
"Equal contribution.

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Ours
Ours
-3 EV
EV
EV
EV
SCT
Challenge123
Ours
SCT
Challenge123
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Figure 1: Comparing HDR fusion models (Kong et al. 2024 trained on our 32R-HDD dataset, with the proposed domain adapter 32R-Adapter, with the same model trained on previous SCT (Tel al. 2023) and Challengel 23 (Kong et al. (024) datasets. Results show our dataset and training scheme can reduce ghosting artifacts under large motion (left) and recover very high dynamic range scenes, such direct sunlight (right).
To address these limitations, introduce S2R-HDR, the first large-scale HDR synthetic dataset designed for HDR fusion. S2R-HDR features several distinctive characteristics: 1) High Quality:
Inspired by prior works eLi al., 2023 Yang al. 2023 Hu et al. 2023 Chen al. 2023 Yin
iet al, (024). we render hugh-quality raw HDR data using Unreal Engine, with realistic lighting,
shadow, weather, and motion effects. 2) Large Scale: The dataset contains 24,000 HDR images,
around 166 times larger than typical datasets (Kalantari & Ramamoorthi 2017 Tel et al., 2023 Kong et al. 2024). 3) Diversity: The dataset encompasses different motion types and lighting. also covers different dynamic elements such as animals, humans, and vehicles across a variety of indoor and outdoor settings. 4) Controllable Environmnn:: Using tools developed based on xrfeito- ria (Contributors) 2023), we can flexibly control environmental factors to create diverse data.
While rendering engines can generate large volume of high-quality synthetic data, domain gap exists between synthetic and real data, particularly in texture distribution, as discussed in Ap- pendix A.2 To address this, we propose S2R-Adapter, a plug-and-play simulation-to-rest domain adaptation approach designed to bridge this gap. This approach can be applied to both labeled and unlabeled data, meaning even if the target real HDR datasets do not have the ground truth fusion re- sult, can still adapt to it. To achieve this, inspired by previous works (Hu et al. 2021 Yang al. 2024 Liu et al. 2023a). our S2R-Adapter consists of two branches: 1) A share branch manages knowledge sharing, which ensures the knowledge learned from synthetic data not forgotten, and 2) transfer branch facilitates knowledge transfer, which ensures the model can adapt to real input.
be applied different network structures, includ- ing both CNN-based and transformer-based models. Integrating this strategy using re- reparameteriiztiio (Ding et al. 202T incurs no extra computational overhead during inference.
Additionally, our training strategy
Experimental results on both labeled and unlabeled real datasets demonstrate that the proposed dataset and method significantly enhance the performance of HDR fusion models trained on syn- thetic data when applied to real scenes, achieving state-of-the-art results. Our study not only pro- vides a new solution for HDR fusion but also presents a feasible path for generalization in fields where data acquisition is challenging.
RELATED WORKS
Image HDR datasets. Datasets are essential for the development and evaluation of algorithms.
Before the deep learning era. [Snn et al. 2012) and Tursun et al. (2016) provided real-world HDR datasets containing and 16 scenes, respectively, and KKlannari & Ramamoorthi (2017) further introduced the first paired LDR-HDR dataset with 89 pairs. Prabhakar et al. (2019) later expanded
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Illustration of our S2R-HDR dataset, covering both indoor and outdoor environments under diverse lighting conditions, including daytime, dusk, and nighttime, as well as various motion types such as humans, animals, and vehicles.
Figure 2:
+3EV
DEV
+ -EV
#2VV
DEV
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Figure 3: Visualization of our sequence data and synthesized multi-exposuue LDR images. Since the dataset consists of raw HDR sequences, it enables effortless data augmentation, such as brightness enhancement and motion amplitude adjustment.
this to 582 LDR-HDR pairs and Tell ct al. (2023) collected a dataset focusing on foreground objects and larger motion variations, with 1 44 samples. Other datasets are also built for deghosting (Shu
et al. 2024), mobile imaging (Liu et al. 2023b) or large motion (Kong et al.) (024)).
Image HDR methods. Deep learning has been introduced into the field of HDR fusion due to its remarkable performance in image processing. Early researchers designed alignment and fusion
pipeline (Kalantari Ramamoorthi 2017; Wu al. 2018). Subsequent works
et al. 2022 Chung & Chol 2023 Liu al. 2021 Yan et al., 2023a) focused on improving the alignment process by developing more advanced modules to handle motion artifacts across different exposures. Kong et al. (2024) also proposed novel efficient processing network.
Over time, several alternative pipelines for HDR fusion have been proposed, using attention mech- anisms Yan et al. 2019), non-local blocks Yan et al. 2020), generative adversarial networks (Niu al ). or mu multi-step fusion (Ye et al. allaal...00 (2021). Recently, transformer models have shown promis- ing results in HDR fusion (Song et al. [222]] Liu et al.
2022). Tel et al. (2023) also developed semantic-consistemi alignment-free transformer for HDR fusion. Recently, diffusion models have also been introduced to HDR fusion by (Yan et. 20.
2023b Hu et al., 2024, Chen et al. (025), further improving the performance. Self-supervised approaches have also been introduced to HDR fusion, with the SelfHDR method (Zhang al. 2024) utilizing a color and structure-focusee network to effectively handle deghosting.
Sim-to-real domain adaptation. Domain adaptation has been widely used to transfer models trained on synthetic data to real-world settings. To address the domain shifts, researchers use ei- ther adversarial approaches (Ganin et al. 2016 Tzeng et al. (017)) or domain randomization (Tobin et al. 2017). Recently, slapter-based domain adaptation (Hu et al.) 2021 Chen et al.
2022 Sung 2022 (ct al. 2022) has been proven to be more effective. Adapters (Hu et al. 202 Chen et al.
y are
form of parameter-effficiet fine-tuning (PEFT) (Hu et al. 2021 Zaken al. 202 Gao al. 2021
Hu et al. 2022a), which require fewer parameters than full retraining and help mitigate catastrophic forgetting (Chen al. 2022 Liu al. 2023a) in domain adaptation. Additionally, Test- Timt-Time-ae Adap-
lation (TTA) (Kundu et al., 2020 Boudiaf et al. 2023 Wang et al. 22022] Liu et al.) 2023al Chen
al. 2022) has been extensively explored, aiming adapt pre-trained model to unknown target
domains during test-time, without any labeled or source domain data.
3 S2R-HDA DATASET
Previously, to create an HDR dataset with ground truth, researchers often use a beam splitter and two cameras to simultaneously capture images with two different exposures (Froehlich al. 2014
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Table I: Qualitative comparison and analysis of different HDR datasets. Besides the DR, all num- bers are in percentage.
Extent of HDR
Intra-Irame Diversity
Overall Style
Dataset
FHI.P
EHI. f
S
CF
stdl. f
ALI.
DR
Size
Kulantari Kalanntri & Ramamoorthi 2017
15.07
3.07
18.4
4.74
10.02
6.19
2.71
89
SCT Tel et a. 2025
12.43
2.43
18.25
3.92
9.39
5,44
2.55
144
Challenge 13 Kong of al. 2024
26.91
5.19
20.47
5.19
12.73
9.88
2.36
123
S2K-HDR
28.02
5.47
38.02
14.96
15.16
10.53
3.86
24000
Wang et al. 2021). The beam splitter only has two different exposures, which limits the dynamic range of the image. However, there are various high dynamic range scenarios in natural scenes, such as environments with direct sunlight. Accurately extracting tens of thousands of data samples from these scenes is significant challenge. Previously, the largest commonly used dataset contained only 144 images (Tel et al. (023), whereas ours includes 24,000 HDR images, representing a substantial lapp in scale and diversity.
Moreover, capturing the ground truth often requires capturing different exposure images frame-by- frame (Kalantari & Ramamoorthi 2017; Tel et al. 2023, Kong et al. 2024; Chen et al. 2021 Shu al. 2024) and manually controlling motion between frames, making capturing extremely time- consuming. The captured motions are often limited and unrealistic, most of them are just basic human movements. These limitations have made it difficult scale HDR datasets both in terms of size and motion variety. Below, we discuss how we solve all these challenges.
3.1 RENDERING DESIGN
Rendering high-quality HDR data presents several challenges. One challenge is that rendered images have different distribution compared to the actual raw sensor data captured by cameras. To mitigate this difference, we made several improvements. First, by default, rendered images have baked-in tone mapping, an irreversible process that compresses dynamic range for standard displays, making it hard to recover original HDR data. To overcome this, we design a custom UE5 (Unreal Engine 5) rendering pipeline that modifies tone mapping and gamma correction, ensuring the output remains in linear HDR space, and stores results in floating-poit formats (EXR) to prevent data quantization. This approach ensures greater accuracy and makes the rendered data more suitable for HDR-related tasks. Second, we also simulate imperfections during handheld capturing. We incorporated camera shake simulation into our camera pose control to replicate the vibrations and instabilities that oceur during real-world capture. This ensures that the rendered data closely mimics real-world shooting conditions, yielding more realistic HDR data for image processing and model training.
Another challenge is to construct realistic and diversified HDR scenes, with varying motion, light- ing, and environmental details. To tackle this, we design and curate a diverse range dynamic scene materials, including common moving objects such as animals, pedestrians, and vehicles, ensuring that the scenes exhibit a high degree of dynamism and complexity, as shown in Figure 2 Addi- tionally, carefully build a variety of high dynamic range scenes, encompassing both indoor and outdoor environments, various lighting conditions across different times of day, and extreme lighting scenarios. This diversity ensures that the generated HDR data simulates a broad range of real-world environments as much as possible. Additional examples of our motion materials and HDR scenes can be found in Appendix B.I and Appendix B.2
In total, we rendered 1,000 sequences, each containing 24 frames, resulting in a dataset of 24,000 HDR images, all stored in EXR format at a resolution of 1920 1080. As demonstrated in Figure 2 our rendered data encompasses variety of environments and includes broad range of motion types, showcasing high degree of variability. Furthermore, since the data is in linear HDR format, it facilitates flexible data augmentation, enabling the easy generation of different LDR (low dynamic range) images, as shown in Figure 3
3.2 STATISTICS AND ANALYSIS
We further analyze the diversity of S2R-HDR in comparison to previous datasets (Kalantari & Ra-
mamoorthi 2017 Tel al. 2023 Kong al. 2024 Following the methodology of Shu et al.
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(a) S2R.Adapter
(b) Visualization of Representations
Resdered Domain
Real Domain
Outpur feotures
Branch
Pretrained
Transfer Branch
Scale Add
Prstralzed
loput featurss
Figure 4: Structure of S2R-Adapter and t-SNE visualization of feature representations.
seven metrics to evaluate the diversity of dif- ferent datasets across three dimensions: the extent of HDR, intra-frame diversity, and overall HDR style. As shown in [ the S2R-HDR dataset outperforms all prior datasets across these metrics. The "Extent of HDR" metric demonstrates that our dataset covers a broader range of highlights, in- dicating extended highlight range. The "Intra-frame Diversity" metric suggests that our images contain more detailed information and richer content. Finally, the "Overall Style" metric reveals that S2R-HDR exhibits significantly higher dynamic range, surpassing the performance of previ- ous datasets. Details of seven metrics can be found in Appendix B.4
)
Guo al. (2023); Hu al. 2022b).
Additionally, to visually illustrate the distribution between our dataset and existing real-world datasets (Kalantari & Ramamoorthi [0177] Tel et al. [223] Kong et al. (024). we extract seven- dimensional feature vectors for each image and apply t-SNE (Van der Maaten Hinton 2008) for dimensionality reduction. As shown in Figure [ (detailed in Appendix A.2). our S2R-HDR dataset
spans a broader range in terms of data diversity. Additional data samples, along with optical flow, depth, and normal maps, are provided in Appendix Bn6 and Appendix B.7 where we also discuss further application scenarios.
4 DOMAIN ADAPTION
With all the careful design proposed in the previous section, there is still noticcable gap between the synthetic S2R-HDB HDR dataset and the real one, as shown in the t-SNE visualization in Appendix [A.2] Thus, it is crucial to adapt the model trained on large-scale rendered dataset to a scall-scale real one. Still, direct fine-tuning on labeled real data can lead to overfitting and knowledge forget- ting (Yosinski al. 2014 Kirkpatrick et al. 2017).
To mitigate knowledge forgetting, we propose AdR-Adapter, visual adapters designed specifically for the HDR Fusion task, which enhance knowledge control.
This is inspired by recent studies (Liu et al. suggest that adapters (Hu et al., [202]] Rebuffi et al. 2017 Chen al. 2022] can mitigate forgetting in high-level vision tasks. Our adapter consists of two branches: share branch to preserve shared knowledge from the rendered dataset, and a transfer branch learn Nomain-specinn
2023a Chen et al. 2022 Sung et al. 2022), which
knowledge from the real dataset, shown in Figure((a)) (a). We chose this design because we want to utilize both the shared knowledge from S2R-HDR to address large motion and dynamic range fusion, and the domain-specific knowledge from the real dataset, like more realistic textures.
More specifically, the proposed S2R-Adapter uses plug-and-play structure, which can be attached to any pre-trained layers performing matrix multiplication (e.g., Linear Layer, Convolution Layer). Following Liu et al. (202a)), we use a low-rank adapter as the share branch, which can better address knowledge forgetting, and use a high-rank adapter the transfer branch, which can better extract domain speciffc knowledge. Below we introduce details of each branch.
Shared branch. Considering a linear layer. Let the pre-trained weight matrix be Wo E hous with input feature x. The original output of this layer is Wox. The shared branch uses a low-rank adapter, projecting the feature with cwonerroecttion matrix V, E Xr, followed by up- projection matrix U, E gr. ghout, where the rank r, < min hin bou)). The output of the shared branch is fa U, V.r.
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Transfer branch. The transfer branch employs high-rank adapter structure, starting with an up up-projection matrix Vi Riai IR,i followed by down-pun pon-projection matrix U2 E R Xouu, where the rank r2 max(hin: hoa). Thus, the output of the transfer branch is f. U.V.
The output features of the two branches are scaled by two separate factors ag axs then added to the pre-trained weight output:
f= Wo +a x f a2 ft- The scale factors at, and or control the trade-off between the shared knowledge and the transfer to the real domain distribution.
(1)
To verify the effectiveness of the proposed share branch and transfer branch adapters, visualize the distributions of the rendered and real images using t-SNE (Vnn der Maaten & Hinton 2008) in Figure 4 (b). From the share branch adapter, the feature distributions are consistent between the real and rendered domain, indicating that the share branch can ignore the domain difference between the real and the rendered domain, preserving the shared knowledge from forgetting. On the other hand, the transfer branch better separates the real distribution from the rendered distribution, showing its capability to model the real data distribution better and extract domain-specific knowledge in the real domain.
Verification using t-SNE.
Training with labeled data. In this study, we consider two domain adaptation tasks. One is adapting to real domains with ground-truth labels. The other is generalizing to any real domains without ground-truth labels during inference. When the labeled real domain data is available, we into the pre-trained model and fine-tune the system on the labeled data. We
inject our also learn the scale factors a,, and Og to ensure the optimal trade-off between shared knowledge and transferred knowledge on the real domain distribution.
Boch-prop
Input Patches
Student Model With Saft-da
Loss
EMA Update
Exposure
Augmentation xN
SJR-Asisphe
Teacher Output xN
Domain Shift
Figure 5: S2R-Adapter Framework test-time adaptation without ground-truth data.
Test-time adaptation with unlabeled data. During test-time adaptation, no labeled real domain data available, and each sample is seen only once. Therefore, a, and a cannot be learned across the real domain. Moreover, each test sample's varying distance to the rendered domain requires adaptive scaling transfer and shared branches. Therefore, inspired by Liu et al. (2023a): Ovadia al. (2019), we dynamically adjust the scale factors using domain shift. For larger shifts, increase the transfer branch's 's scale factor, encouraging more knowledge from the real domain. For smaller shifts, allocate more from the shared branch, preserving rendered domain knowledge. Domain shift is measured by model uncertainty, following Wang et al. (2022); Liu et al. (2023a); Roy et al. (222);; Ovadia et al. (019). In our HDR Fusion task, we augment input samples N times and calculate variance across N outputs as the uncertainty value U(x)). Augmentations include adjusting exposure, white balance, noise levels, and random flips. With the uncertainty value, adaptively adjust scale factors:
00 1 U(x); Or u(x))
Following previous works test-time adaptation Yang et al. 2024 Wang et al. 2022 we utilize the mean-teacher framework. As shown in Figure 5] we inject S2R- AdRatess to both the teacher model T and the student model S. We initialized both models with pre-trained weights rendered domain. Following Liu et al. (2023a), the teacher model generates uncertainty values and pseudo-labels ÿ for updating the S2R-A Adapters. The student model is optimized by the loss between
the

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Table 2: Experimental results on SCT (Tel et al. 2023) and Challenge 123 K et al. 2024) dataset with ground-truth. We first trained the two baseline networks on the 32R-HDR dataset, followed by simulation-to- -o-rrell domain adaptation on the SCT and Challenge 123 training sets using the S2R- Adapter. In contrast, the other methods were directly trained on the SCT and Challenge 123 training sets. The results marked with * are those recalculated using images provided by Tel et al. (2023).
Metheds
Ariit'AiuuuiitTss SCT
Tellet
2025
Trais/Aalptatinnai
Challesge123 (Kong ctal.
PSNR
PSNR
SSIM
SSIM
IDD VDP2"
PSNR
PSNR
SSIM
SSIM
IID VDP2
NELDRRNes Yon al 2020
36.68
39.61
0,9590
0.9855
63.72
37.82
26.75
0.9769
0.9632
53.38
DHDRNei Kalantis
40100
43.37
0,9754
0.9924
57.85
0:9707
09705
$1.32
AHDRNNT
0,9833
0.9945
40.44
019877
09703
54.58
DiMID
0.9654
38.78
09745
53.38
HDR-TTaesfeeee
42.50
40.70
$4.63
SCTNot
2023
0.9650
0.9652
40.65
54.35
WETNNCWESZRR (OWi))
43.24
48.32
0.9572
0.9962
42.58
30,08
0,9915
0.9805
55.35
SAFNei KKng 2004
0.9631
41.88
0.764
55.07
SAPNEEWS2KTOON (O2T0
43.33
48.90
0.9564
0.9959
43.43
0,9915
0.9624
56.51
the student output ý and the pseudo-label j7. The teacher model updates via the exponential moving average (EMA) of the student model:
T'= XT + (1-
where t is the test step, À is set to 0.999, following Tarvainen & Valpola (017
5 EXPERIMENTS
Datasets. In line with the latest research (Tel et al. 2023 Kong et al. 2024), we train and evaluate our models on recent HDR datasets: the SCT Dataset al. 2023). which contains 108 training samples and 36 test samples featuring dynamic scenes with significant foreground or camera motion; and the Challengel 23 Dataset(Kong et al. 2024), a complex multi-exposure HDR dataset collected using a vivo X90 Pro+, comprising 96 training samples and 27 test samples.
Experiment details. We select the three latest methods (Liu al. 2022 Tel al. 2023 Kong et al. 2024) as our baselines: HIDR-Transfomee (Liu et al. 2022 and SCTNet (Tel et al. 2023) are transformer-basee approaches, while SAFNet (Kong et al., 2024) a CNN-based approach. When training these methods on our 2RR dataset, first generate three different exposure LDR images from the original HDR images. Then, we apply the same data augmentation and training strategy. Regarding adaptation steps, for supervised adaptation with ground-truth labels, fine- tune the model for about 30 epochs. For the self-supervised mode (test-time adaptation without ground-truth), the model adapts as it processes the data during test time, each sample is seen only once.
Evaluation metrics. We employ commonly used metrics, including PSNR and SSIM, along with VDP2 (Mantiuk et al.] 2011). a metric designed for HDR evaluation. PSNR and SSIM are computed in both linear and p-llaw tone-mapped domains, denoted as -f and -u, respectively.
5.1 RESULTS
Results on test datasets with ground truth. To validate the effectiveneess of our method (S2R-HDR HDR dataset and S2R Adapter), we conducted a comparative study on the latest SCT (Tel et al. 2023) and Challenge122 123 (Kong et al.) (2024) datasets against seven widely adopted HDR approaches, in- cluding both CNN-based (Kalantari Ramamoorthi 2017)) Yan et al. [220 2019 Tel al.) 2023). Transformer- basedd (Liu et al. [022 Tel et al. (223) and diffusion-- based Yan et al. (2023)) models. We selected the latest SCTNet and SAFNet as our baseline networks, where SCTNet represents the Transformer- based method and SAFNet represents the CNN-based method. Specifically, trained the two baseline networks on the S2R-HDR dataset, followed by synthetic- to-rell domain adaptation on the SCT and Challenge 123 training sets using the S2R Adapter. In contrast, the other methods were directly trained on the SCT and Challenge 23 training sets.
first
As shown in Table 2 our method achieved the best results on both datasets. In terms of the PSNR -p metric, our approach demonstrated least 0,6dB improvement over both baseline networks on PSNR-u, and notably achieved a significant 2dB gain on the Challenge 123 dataset across both base- lines. Additionally, we provide a comparative analysis of visual effects, as illustrated in Figure 6
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Guan SIoTher towe euppod HDR Iroge
Lock
SARMA Dunn cee-raapeeenee wrage
Pinchee
Transfemer
Transfortur
[Dun)
(Durs)
Figure 6: Visual results on the SCT (Tel al. 2023 datasets (left) and Challenge 123 (Kong al,, 2024 datasets (right) with ground-truth training data. Our method effectively eliminates artifacts caused by motion occlusions, delivering superior visual quality.
Table Experimental result on SCT Teel et al. 2023) and Challenge 23 (Kong et al. 2024) without ground-truth (test-time adaptation). We report the testing results of baselines pre-trained on real- world datasets generalizing to the SCT and Challenge 123 test datasets, followed by the S2R-Adapter test-time adaptation results of SCTNet and SAFNet pre-traine pre-trained on S2R-HDR.
Methods
Test SCT Thl al.
2023
Teston Challengel 23 Kong
5024
Train
PSNK
PSNK-
SS
SSIM-
HUR-VDP!
Trin
PSNK-
PSNK-c
SSIM
SS1M-F
DHI--VPPS
0.9497
25.35
HDR-Trandfe SC
Tel
Challenee123 23
32.00
0,953
36.57
25.07
03559
$2.09
$2.69
SAFNs
35.14
38.77
0,9619
0.9863
64.00
34.26
25.50
0.9718
0.990
$2.69
S.R.-illpiez (Cund
SC TNdt
SIRHOR
54.83
42.32
03526
019533
65.09
SEK-HOR
41.49
30:37
0,9862
05736
SAFNet Adapter
SIR-HDR
S2R-HDR
Our method effectively reduces artifacts caused by motion occlusions, delivering superior visual quality. We further visualize the difference maps of model output on the SCT dataset before and af- ter applying the S2R-Adapter in Appendix A.. The results show that our S2R-Adapter effectively reduces the domain gap, especially in texture-rice rich areas.
Results on test datasets without ground truth. We conduct the following experiment to validate the effectiveness of -2R-Adapter when generalizing to unseen test datasets where ground truth la- bels are not available for the adapted models. The pre-trained models are adapted to unseen target datasets SCT (Tel et al. (2233) and Challenge 123 (Kong et al. 2024), without accessing the ground truth of the target domain during adaptation. Models will see each test sample only once. As shown in Table 3 compared with SCTNet and SAFNet trained on existing real-world datasets, models trained on our S2R-HDR dataset coupled with our S2R-Adapter can more effectively generalize to the unseen target domain. For instance, using SAFNet the SCT dataset, approach achieved 1.1dB improvement in PSNR-p and an 8,46dB improvement in PSNR-C compared to the best base- lines. The S2R-Adapter alone provides 1.39dB and 3.38dB improvements in PSNR-u and PSNR- respectively.
With our test-time adaptation framework, models pre-trained on our dataset can effectively gener- alize to unseen images. A qualitative comparison using real-captured data without ground truth is illustrated Figure 7 Our method effectively alleviates artifacts highlight areas during night- time and reduces ghosting caused by large motions. More visual comparisons are available in Ap- pendix A.4
5.2 ABLATION STUDY
Effectiveness of S2R-HDR dataset. To evaluate the effectiveness the S2R-HDR dataset, we select the three latest methods (Liu al. 2022 Tel et 2023 Kong al. 2024) baselines, which include both transformer maasdeeer--baeed and CNN-based approaches. Additionaly l,, we chose the two most recent datasets, SCT et 2023) and Challengel (Kong at. 2024), comparative datasets.
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LDita
SAP Sal-r2 tons-mapped HORE image
LDRs
SA S2w/Dungg tone-mapped HOR Image
Transformer
NDR
SCTNet
SANNet
Net-22R (Duns)
Transformrr
NDR
SCTMet
SAFNet
SA-Nett-B BBRR (Ours)
Figure 7: Visual results on real-captured scenes show our solution reduces ghosting backlit scenes (left) and recovers highlights (right).
Table Experimental results on the effectiveness of the S2R-HDR dataset. We test the cross-dataset generalization of models trained on different datasets. Models trained our S2R-HDR dataset achieve superior generalization and require only minimal fine-tuning on SCT or Challenge 123 to reach state-of- the-art performance. The best results are in bold.
Malbods
Training
Testing on SCT Teleral 3023
Texting os Challesge123 Kony et al. 2004
PSNR
PSNB..
55
PSNBB.u
SSIM.
Challenge
Kong
SCT
SIR
SER HDR Flne
Chillenue123
SCT Tel 2023
42.55
47.51
0.9550
0.9952
34.57
25.07
0.9753
0.9599
SCTNet Tel et 2013
Challenge I2 Koni 2014
SCT Caamenger
32.60
40.00
42.79
35.03
0.900
0.535
0.9955
0.9639
40.04
40.65
25.21
25.73
0.9998
019822
0.9211
0,730
SER HDR
34,83
42.32
05526
0.9133
41.49
30.37
0.982
0.796
S2R-HDR Frn--tmee un SCT Challange12 23
43.22
47.25
0.9872
0.9611
42.10
30.15
0.9914
0.97%
SCT (ol 2023
42,66
45.38
0.831
03955
34.26
25.50
09718
0.590
SAFNet Kong et
3024
Challetge Ias Koor tel 2004
SCT Cartingerz
35.14
33.77
03619
09908
41.38
29.73
0.997
0.764
SIR-HDR
Flac-tuse Chillenge123
We train the three baseline methods on the SCT dataset, the Challenge 123 dataset, merged dataset combining SCT and Challenge 123, and our S2R-HDR dataset, then evaluate their generalization by testing on both SCT and Challenge 123. Furthermore, given the domain gap between synthetic datasets (such as 32R-HDR) and real-word world datasets (SCT and Challenge125 123), we also fine-tune the models trained on the synthetic S2R-HI HDR dataset on the real datasets, following the approach in Niklaus et al. (2021).
dataset surprisingly outperforms the trained di- rectly on Challenge123 when evaluated on the same dataset. Moreover, models trained on either the SCT or Challenge datasets suffer significant performance degradation during cross-validation, indicating their limited generalization capability. In contrast, models trained solely on our S2R- HDR wathset-without any exposure SCT 1 12-mmmoosstrtttt superior cross-dataset generalization. highlighting the high quality and robustness of our dataset. Additionally, models trained on S2R-HDR require only minimal fine-tuning on SCT or Challenge of-the-art performance. Across all three tested methods, models trained on S2R-HDR outperformed those trained directly on SCT or Challenge1 123, achieving at least a 0.4 dB improvement PSNR -u. These results confirm the effectiveness of our S2R-HDR dataset in enhancing model robustness and generalization for HDR fusion tasks. We further show the visualization results of our S2R-HDR dataset comparison experiments in Appendix A.5 We also compare S2R-HDR dataset with the Kalantari (Kalantari & Ramamoorthi 2017) and Real-HHDRyDDeghosttnn (Shu et al. 2024)
As shown in Table 4 the model trained
achieve state-
datasets in Appendix A.6
Effectiveness of S2R-Adapter's two branches. To validate the effectiveness of the knowledge- sharing branch and trowlerge-transfer branch designed in Adapter method, conducted ab-

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Published as a conference paper at ICLR 2026
Table 5: Ablation study of the S2R-Adapter us-
Table 6: Ablation study of the S2R-Adapter Framework under test-time adaptation without GT data. The baseline is the SAFNet (Kong et al. (024) pre-trained on S2R-HDR Dataset. The test data is the SCT Dataset. TS stands for the teacher-student framework, Adapter for shared and target branch adapters, and Unc for scale factor adjustment with uncertainty.
ing the SAFNet model (Kong et al. 2024) pre-
trained on the S2R-HDR dataset. The exper-
iments are conducted on the SCT dataset (Tel ct al. 2023). When both branches work together with learned scale factors a and at, optimal performance is achieved. In the case non- learnable 0g and at, their values are set to 1.
Baselre
Pieotroe
Siae
Treesor
Teurred
PSNR.
PSNR.
55TM
55MM
Adapter
PSNR
SSIM
S
$4,50
45,85
43,00
48.70
0.9831
019658
34.89
43.85
0.9500
0.9939
43.33
68.76
(U5860
(US55S
$4.93
44.71
0.9477
0,9914
36.05
46.79
0.9699
0.9948
65.28
63.33
08-68
50:00
(L9863
119964
(19559
(19059
36.28
47.33
0.9586
0,9949
Table 7: Experiment results on knowledge control using SAFNet (Kong al. et al. 2023) and S2R-HDR datasets. The result shows that our S2R-Adapter better alleviates knowl-
2024 the SCT Tel
edge forgetting.
Test on SCT (Tel et al. 2023
Test on S2R-HDR
SAPNet Kong al. 2024
PSNR-p
PSNK-7
PSNR-1
PSNR-F
Fine-tune on SCT Tel el al.
2023
43.03
48.79
35.52
29.40
S2R-Adapter on SCT Iel el
2023
43.33
48.90
35.95
29,80
lation experiments on the SCT dataset using SAFNet the baseline to evaluate the impact of each branch on the experimental results. As shown in Table we tested the effect of using each branch individually. Results indicate that using only the knowledge-sharing branch outperforms simple fine-tuning, suggesting that this branch effectively learns shared knowledge, thereby reducing the forgetting of pre-trained knowledge. Meanwhile, using only the knowledge-transfer branch leads to more substantial improvement, further confirming the significant differences between synthetic and real data. When both branches work together with learned scale factors a, and og, optimal performance is achieved.
Effectiveness of knowledge control. We conduct experiments to show that our method better fa- cilitates knowledge control than simple tie-ttnnng. effectively alleviating knowledge forgetting. Specifically, we first train the SAFNet (Kong al. (024) on the S2R-HDR as a pre-trained model. Then, we apply simple fine-tuning and our adapter- based fine-tuning for domain adaptation on the SCT (Tel et al. 2023) dataset and subsequently test the models on the original S2R-HDR training set to measure knowledge forgetting. As shown in Table S2R-Adapter effectively adapts to the SCT dataset while minimizing knowledge forgetting, demonstrating better preservation of pre-trained knowledge.
Effectiveness of S2R-Adapter framework under test-time adaptation. We validate the S2R- Adapter Framework's 's effectiveness during test-time adaptation through ablation studies the SCT dataset, using SAFNet as the baseline, pre-trained on our S2R-HDR dataset. As shown in Table 6 the teacher-student framework enhances results by making the test-time adaptation process more robust. Most improvements are from our shared and transfer branch adapters. Additionally, dynam- ically adjusting the scale factor between the adapter branches based on uncertainty measurement allows for better control of shared and transferred knowledge across varying domain shifts, further enhancing performance.
CONCLUSIIO
This paper introduces the S2R-HDR dataset, a large-scale, high-qualit, resource for HDR fusion in dynamic scenes. By providing diverse, controllable, and high-fidelity synthetic data, the dataset addresses the limitations of existing HDR datasets. Additionally, we propose the S2R-Adapter, novel domain adaptation method that effectively bridges the gap between synthetic and real data, enabling efficient knowledge transfer. Experimental results on both labeled and unlabeled datasets demonstrate that our $2R-HDB dataset and S2R-Adapter significantly enhance the performance of HDR fusion models in real-world scenarios. This provides viable solution for the HDR field, where data acquisition is often limited. Future work will focus on expanding the HDR dataset to support a wider range of application scenarios.
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7 REPRODUCIBILITY STATEMENT
To ensure the reproducibility our findings, detailed implementation instructions are provided in Section 5 and Appendix A.T] In addition, the source code and datasets publicly available URL: htt.://ttis//oppriinnngiaaaaaaiu httpgig....giiii.bo///----D
8 ACKNOWLEDGEMENT
This work is supported/funded in part by the Shanghai Artificial Intelligence Laboratory. the Centre for Perceptual and Interactive Intelligence (CPIT) Ltd., CUHK-led InnoCentre under the InnoHK initiative of the Innovation and Technology Commission of the Hong Kong Special Administrative CUHKCUUU-
Region Govermeen, RGC Early Career Scheme (ECS) No. 24209224, Joint Collaboration Fund No. 2025A0505000053, We thank Yixuan Li for helpful discussions on Unreal Engine rendering. We also thank Haoran Yang for his assistance in capturing the real-world dataset.
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A ADDITIONAL EXPERIMENTS
A.1 EXPERIMENTS DETAILS
When training these methods on our S2R-HDR dataset, we first generate three different exposure (1-2, O, +2) (-3, 0, +3}) LDR images from the original HDR images. Following this, we apply the same data augmentation techniques, and training schedules across all models. Additionally, we introduce random Gaussian noise with a E [0.0001, 0.001] to the lowest exposure image and [0.00001, 0.0001] to the middle exposure image.
For the SCTNet (Tel et al. 2023) architecture, which is based on the Transformer framework, we employ a linear layer as the projection layer of the S2R-Adapter (as illustrated in the left part of Figure 4 and integrate it into SCTNet's WindowAntntion Linear Layer. In contrast, for the SAFNet (Kong et al. (202)) architecture, which is based on CNNs, tional layer as the S2R-Adapters
utilize 1x1 convolu- projection layer and inject it into the network at layers indexed by [3:25:2] and (42:58:4). For both CNN and Transformer architecture, we set the rank of the shared branch adapter rs to be 1, and the rank of the transfer branch to be 64, following Liu et al. 2023a).
In our test-time adaptation experiments, each test sample is processed only once. To assess sample uncertainty as measure domain shift, we employ test-time augmentation techniques. Specif- ically, we augment test samples using a variety of exposure levels: [-0.1, -0.5, 0, 0.5, 1). Addi- tionally, we apply random transformations, including flips, white balance adjustments, and random Gaussian noise. For augmentations involving exposure and white balance, we apply the parameters to the input images following inverse tone mapping. Correspondingly, the inverse transformations directly applied to the model outputs. This results in N augmented outputs per sample, from which we compute the variance across these outputs to quantify uncertainty (xx).
We used the Photomatix software to perform tone mapping on HDR images.
A.2 ANALYSIS OF DOMAIN GAP AND ADAPTER
S2R-HDR Real Dalasets
Figure 8: The distribution of our S2R-HDR dataset and real captured HDR datasets (Kalantari & Ramamoorthi, 2017: Tel et al. 2023 Kong et al. 2024). Following the approach outlined in Shu et al. al.2(4)) Guo al. (2023): Hu et al. (2022b), for each HDR image, we first extract 7-dimensional features listed in Table 18 that capture key aspects of HDR, including the extent of dynamic range, intra-frame diversity, and the overall style of the HDR images. These features are then projected into 2D space using t-SNE (Van der Maaten & Hinton 2008) for visualization. Regarding the concern
about real-dataset sample size, we analyzed all real images (Kalantari & Ramamoorthi 2017 Tel
ct al.
2023
Kong al. 2024) without sampling bias.
Distribution of our S2R-HDR dataset vs. real datasets. To better understand the distribution of our S2R- -2R-HDR dataset compared to existing real-world HDR datasets (Kalantari & Ramamoorthi
2017 Tel et al. 2023 Kong et al.) 2024), we extract seven-ding seven-dimensional features representing key
HDR characteristics, including dynamic range, intra -raaefrmme diversity, and overall image style. These features visualized using t-SNE (Van der Maaten & Hinton 2008), as illustrated in Figure The
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results demonstrate that our 32R-HDR dataset spans significantly broader range of HDR scenarios, reflecting richer variations in lighting conditions, scene dynamics, and environmental styles.
Why we need adapter. Despite the broader coverage of S2R-HDR, there remains noticeable domain gap between our synthetic data and real HDR datasets, as highlighted in Figure g This gap primarily stems from differences in texture representation, environmental details, and natural lighting conditions. To address this, we introduce an S2R-Adapter to bridge the disparity, enabling models trained on synthetic data to generalize effectively to real-world scenes.
What the adapter learns. To gain deeper insights into the domain gap between real and rendered data and better understand what domain adaptation learns, we compute difference maps for mod- els trained on the rendered dataset (S2R-HDE HHDR) before and after domain adaptation the SCT dataset. As shown in Figure 9 the differences are primarily concentrated in regions con- taining trees, grass, and people, while ground, sky, and buildings remain largely unchanged. This suggests that the key discrepancies between real and rendered data mainly arise in texture-rich areas such human figures and vegetation. The results further confirm that domain adaptation effectively mitigates the domain gap.
0.6
04
02
0.0
S2R-HDR
$2R-Ador S2R-Adapter
Difference Map
Figure 9: Difference maps of models trained on the rendered dataset (S2R-HDR) before and after domain adaptation (S2R-Adapter) to the SCT dataset. The differences are primarily concentrated in texture-rich regions such as trees, grass, and people, while ground, sky, and buildings remain largely unchanged. This highlights that the key domain discrepancies lie in fine textures and demonstrates the effectiveness of domain adaptation in bridging the domain gap.
A.3 DOMAIN ADAPTATION METHOD COMPARISON (FINE-TUNE VS S2R-ADAPTER)
As emphasized, the key challenge lies in addressing the domain gap. We summarize the domain adaptation experiments using fine-tuning and the A2R-Adapter As shown in Table regardless of the method employed, mitigating the domain gap consistently leads to notable performance im- provements.
As shown in Table g the performance SCTNet and SAFNet was evaluated on the SCT and Chal- 123 datasets after being trained on different datasets. Since SCTNet is specifically designed for the SCT dataset, it already performs well on this dataset. Consequently, using the A2R-Adapter results in only modest improvement compared fine-tuning SCTNet on the SCT dataset. How- ever, on the Challenge 123 dataset, the use of the S2R-Adapter leads to a more substantial improve- ment over fine-tuning SCTNet alone, with PSNR- increasing from 42.1 dB 42.58 dB. Similarly, SAFNet, which is designed for the Challenge 123 dataset, shows a more significant performance improvement on the SCT dataset, with PSNR- increasing from 43.03 dB to 43.33 dB. The key challenge remains addressing the domain gap, and mitigating this gap consistently results in signifi- cant performance gains.
We further conduct comparative experiments to evaluate the effectiveness of our dataset against existing datasets an adapter- based setting. Specifically, we first pre-train the SAFNet (Kong et al.) 2024) the S2R-HDR and Challenge 123 (Kong et al. 2024) datasets, respectively, and then adapt them to the SCT dataset using the -2R-Adapter. As shown Table 9 models pretrained on the observe that using the S2R-Adapter consistently outperforms both training solely on the SCT dataset and joint training on 123 datasets, demonstrating the advantage of the proposed adaptation strategy.
S2R-HDR dataset achieve better performance after adaptation. Morcover,
SCT and Challenge 23
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Moreover, shown in 7 the main text, our S2R-Adapter not only improves performance but also offers better knowledge control, with reduced knowledge forgetting compared to fine-tuning In addition to fine-tur aneettning labeled real data, our S2R-Adapter demonstrates superior performance in test-time adaptation, allowing flexible adaptation to unseen data without the need for ground-truth labels. This is particularly beneficial for HDR fusion tasks, where large-scale, accurately labeled real-world datasets are often unavailable. By pretraining on large synthetic datasets and adapting any unlabeled target distribution during test time, our approach exhibits greater flexibility and generalization.
Table 8: Experimental results the effectiveness of the S2R-HD 22R-DDR dataset. We test the cross-dataset generalization of models trained on different datasets. Models trained on our S2R-HDA dataset achieve superior generalization and require only minimal fine-tuning SCT Challenge122 to reach state-of-the-ae performance. The best results are in bold.
Methods
Traising
Testnng on SCT
Tel 2013
Testing on Challenge1
Kong al.
2024
PSNRky
SSIM-ji
SSIM-/
PSNR-y
S5IM-j
SCT 2003
42.55
0.9850
019952
54.57
0.9753
0:0599
SCTNN:
2023
Challenge Konng
Cmnthergezs
32.60
09935
S2R
Chaileene
Challenge1
S2R HDR Adagner
43.24
0.9872
SCT
2013
SAINet Kong 2014
Challenge lar Konng
SCT Chattegetes
S2R-HDR
0.900
09939
42:75
Fise-ture Challkngee23
0.9960
42.91
0.9006
S2R.HDR Fine-tune SCT Challesge123
48.79
019588
S2R-HDR S7R-Adspter
43.33
48.90
0.9864
0.9950
31.84
0.9915
0.9824
Table 9: Effectiveness of S2R-HDR and S2R- -da-ter Pretraining SAFNet (Kong et al. S2R-HDR and adapting to SCT (Tel et 2023) yields the best overall results.
2024) on
Training
PNSR-H
PNSR-6
SSIM-N μ
SSIM-
SCT TTll et al. 2023
42.66
48.38
0.9831
0,9955
Challeng 123 (Kogg et al. 2024
35.14
38.77
0.9619
0.9868
SCT & Challenge
42.12
45.14
0.9853
0.9941
S2R-HDR with $2R- Adapter on SCT
43.44
48.90
0.9864
0.9959
Challengel
123 23 with S2R-Adapter on SCT
42.92
47.05
0.9856
0.9951
A.4 ADDITIONAL RESULTS ON REAL- CAPTURE IMAGES
We further provide visual comparison using real-captured data without ground truth in Figure 10 Our approach effectively reduces artifacts in challenging scenarios.
A.5 VISUALIZATION OF EFFECTIVENESS OF S2R-HDR
We further show the visualization results of our S2R-HDR dataset comparison experiments in Fig- ure [T] models trained on our S2R-HDR dataset achieve optimal visual quality compared to those trained on other datasets. Additionally, as depicted in the left image of Figure II our dataset effec- tively mitigates motion occlusion challenges. Similarly, shown in the right image of Figure II] our dataset effectively addresses challenges related to high light fusion.
A.6 ADDITIONAL DATA EFFECTIVENESS COMPARISON EXPERIMENTS
To validate the effectiveness of our S2R-HDR dataset, we used SCTNet (Tel et al. 2023 as the base- line model and conducted experiments on the Real-HDRV (Deghosting) (Shu al. 2024] dataset, which, although the largest, is less commonly used. The results, as shown in Table 10 with simple fine-tuning, dataset consistently delivers the best results.
We also use SCTNet (Tel et al. 2023) as a bascline model and conduct experiments on the earliest Kalantari (Kalantari & Ramamoorthi 2017) dataset. SCTNet is retrained the entire dataset, and
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SUR(Ourr)
Image
ann-ma image
Transformer
SCTNH
SAFNet
SAPNet-52R (Durs)
HDR Transformer
SCTNet
WEEF
LDRs
SAFNet 83R(Oums) tone-mapped HDR image
HDR
(Dum)
(Durs)
Figure 10: Visualization results real-captured data without ground truth. Our approach effectively reduces artifacts in highlight areas and alleviates ghosting in nighttime scenarios.
NDR Transformez
Tim Hostows
HDR
Time
Times
501 Not
SCTNo SAPMA
Time
Time
LDRs
SAFNet tone-mapped HDR image
LDRs
SAFNet tone-mapped HOR Image
SAFRA
Time
Times
Time
Times
Challsnge123
Rendered
Figure 11: Visualization results of our S2R-HDR dataset comparison experiments. Models trained on our S2R-HDR dataset exhibit significantly fewer artifacts compared to those trained on the SCT
dataset TTl 2023) Challenge 123 dataset (Kong 2024)
the results, shown in Table 11 demonstrate that with simple fine-tuning, our dataset consistently yields the best performance.
A.7 ADDITIONAL EXPERIMENTS ON THE EFFECTIVENESS OF S2R-ADAPTER
We conducted additional experiments more datasets to further evaluate the effectiveness of S2R- Adapter. These experiments include test- time adaptation without ground truth on Kalantari's (Kalan- tari & Ramamoorthi 2017). Sen's (Sen et al. 2012), and Tursun's (Tursun et al.) 2016) datasets.
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Table 10: Experimental results of data effectiveness comparison with the Real-HDRV (Deghost-
ing) (Shu et al. 2024 Datasets.
SCT
Challenge123
SCTNet
PSNR-
PSNR-
SSIM-W
SSIM-
PSNR-H
PSNR-E
SSIM-
SSIM-C
Train Real-HDRV (Deghosting)
35.37
46.13
0,9651
0,9949
36.41
26.42
0.9711
0.9674
Fine-tune on SCT/Challengee13
42.98
47.27
0.9880
0.9956
40.84
28.91
0.9905
0.9765
Train S2R-HIDR Fine-tune on SCT/Challenge123 123
34.83
42.32
0,9526
0.9933
41.49
30.37
0.9862
0.9796
43,22
47.28
0,9872
0.9961
42.10
30.18
0.9914
0.9798
Table 11: Experimental results of data effectiveness comparison on the earliest Kalantari (Kllantari & Ramamoorthi 2017) Datasets.
Methods
Training
PSNR-p
PSNR-E
SSIM-j
SSIM-f
Kalantari
44.13
42.32
0.9916
0.9890
SCTNet
S2R-HDR
41.41
36.68
0.9859
0.9787
S2R-HDR Fine-tune on Kalantari
44.32
43.16
0.9923
0,9911
We first performed test-time adaptation without ground-truth labels Kalantari's dataset using the SAFNet. Table 12 presents the results.
Table 12: Test-time adaptation results on Kalantari
Kalantari & Ramamoorthi
2017 dataset.
Methods
PSNR-1 y
PSNR-
SSIM-
SSIM-F
SAFNet trained on SCT
34.75
39.41
0,9806
0.9821
SAFNet trained on Challenge
40.31
37.19
0.9843
0.9772
SAFNet trained on S2R-HDR
43.06
40.63
0.9887
0.9878
SAFNet trained on S2R-HDR with -dd-teeter
43.29
41.488
0.9889
0.9890
We also conducted test-time adaptation on Sen's (Sen al. 2012) and Tursun's 's Tursun 2016
datasets. We report the non-reference IQA metric MUSIQ (K et al. 2021 scores in Table 13
Table 13: Test-time adaptation results (MUSIQ scores) on Sen's (enn et al. 2012) and Tursun's Tur sun et al. 2016) datasets.
Methods
MUSIQ on Tursun's
MUSIQ on Sen's
SAFNet trained SCT
63,94
65.71
SAFNet trained Challenge123
63.42
65.65
SAFNet trained S2R-HDR
63.32
65.97
SAFNet trained S2R-HDR with S2R-Adapter
65.30
67.45
A.8 ADDITIONAL RESULTS ON HDR VIDEO RECONSTRUCTION TASK
We also conduct experiments on HDR video reconstruction task to validate the effectiveness of our S2R-HDR dataset. We use recent open-sourced methods, HDRFlow (Xu et 2024), our baseline model, and conduct training experiments
Vimeo-90K (Xue et al. 2019). Sintel (Butler
al. 2012). and Real-HDRV (Shu et al., 2024) datasets, testing on DeepHDRVideo (Chen al.
202 dataset. The results, shown in Table 14 with simple fine-tuning, our dataset consistently delivers the best results.
Additionally, in Table 15 we tested temporal consistency using the TOG HDR dynamic dataset (alanntari et al. 2013) with method (Lai et al., 2018), this metric is calculated using Equa- tion 4 which measures the temporal consistency of video by quantifying the flow warping error between adjacent frames. In this equation, Ve represents the original frame, Vi+1 represents the warped frame using optical flow and M a non-occlusion mask indicating non non-occluded regions. The results show that our dataset achieves the best performance in temporal consistency.
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Table 14: Experimental results of data effectiveness comparison on the HDR video reconstruction task. The HDRFlow method is trained on different datasets and tested on the DeepHDRVideo CChn al. 202T dataset.
Methods
Training
PSNR-1
SSIM-y
Vimeo+Sinite Real-HDRV
43.25
53.05
0.9520
0.9956
HDRFlow
Xu et al. 2024
43.37
53.05
0.9540
0,9958
S2R-HD RDR
43.13
52.50
0.9508
0.9953
S2R-HDR Fine-tune on Real-HDRV
43.51
53.52
0.9546
0.9960
Table 15: Experimental results of temporal consistency comparison on the HDR video reconstruc- tion task.
Training Datasets
Sintel+
HDRV
S2R-HDR
Temporal Stability Scorel,
0.2201
0,2183
0.1773
Ewarp (V,, V(+1) M0 E M,
||1100 -
(2(1)
(4)
Fowarp (V) T-I 1= Erarr (V,, V(+1)
A.9 DETAILED DATASET GENERATION PIPELINE
We provide complete and transparent overview of our dataset-generation pipeline Figure Because this pipeline allows us to directly control the lighting parameters of the sources in the UES simulation environment (including their positions, intensities, etc.), we can synthesize a highly diverse set of illumination conditions. In addition, we manipulate the pose and motion of objects the scene by binding different animation sequences to them, thereby producing a wide variety of motion patterns. With the exception of the use of several high-quality UE5 scene assets that purchased for scene construction, the rest of the pipeline is fully reproducible.
A.10 DETAILED DATASET SCALE COMPARISON
We include a comparison of dataset scales between our S2R-HDR dataset and the datasets from 123 SCT (Tel et al. 2023), Challenge 123 (Kong et al. 2024), Kalantari (Kalantari Ramamoorthi
2017) and Real- HDRV(Deghosting) (Shu et al. 2024), presented in Table 16
Table 16: Comparison of dataset scale. We compare 32R-HDE dataset with SCT TTel al. 2023), Challenge 123 (Kong et al.] 2024). Kalantari (alantari & Ramamoorthi 2017). and Real-
HDRV (ORRV(DDeosstin (Shu et 2024).
SCT
Tel
1025
123
Kong at
2024
Kalontan
Kalartan & Kamamconh: 2017
Real Beu--EDDVDDeghooonngg
Stu al.. 2024 S2K-
Datzset size
144 (108/36)
123 (96/27)
39 (74/15)
500 (450500)
24,000
A.11 FURTHER SCENE-LEVEL ANALYSIS OF S2R-HDR
We provide additional scene-level analysis of our proposed dataset S2R-HDR, First, following the same methodology as in (Shu et al., 2024), we analyze the directions of motion using the optical flow. 13 the proportion of pixels corresponding to different motion directions in S2R- HDR, from which it can be observed that the motion patterns in our dataset are highly diverse. Second, Tabic summarizes the distribution of illumination conditions and locations. In terms of scene semantics, our dataset covers a wide variety environments, including home interiors, urban streets, churches, rural areas, parks, subway stations, workshops, and more. Regarding the foreground assets, Figurd [ggurd44pp further illustrates the richness of our foreground content together with the associated motion patterns.
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Launch and intiaiizo
Select high dynamic rernge sveries
Deploy the foraground
the foreground
Sturic foreground or dynamic foroground
Dyremc
Stelio
an sequence
foreground assets
Muliipl faragrounds
Now
Create initaazza parameters
Camora
Create the metion
Gameea shake
No
Sample Perfin rosse and compute he jitered camera poses
Dissble gamma correction mapping
asing
Enable ray tracing
Export the rendered frsmme sequence
Yos
Render multple
No
Figure 12: Distribution of optical flow directions across S2R-HDR.
A.12 TEST-TIME ADAPTATION RUNTIME
Like most test-time adaptation (TTA) methods (Kundu et al. 2020 Liu et al. 2023a Wang et al.
2022 Sun et al. 2020 Wang et al. 2020), approach incurs additional computational cost due
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180
225
Figure 13: Distribution of optical flow directions across S2R-HDR.
model weight updates during inference to adapt to the target domain without ground-trute -tunh labels. However, this cost is modest and justified by the performance gains.
To quantify the overhead, we evaluated test-time adaptation runtime on SCT test images (resolution 1500x1000) using SAFNet as the base model. Experiments were conducted on server with an AMD EPYC 7402 (48C) 2.8 GHz CPU, SxNVIDIA RTX 4090 GPUs, 512 GB RAM, running CentOS 7.9. The results are follows:
Table 17: Test-time Adaptation Runtime
Standard Testing
Test-Time Adaptation
Runtime (seconds) PSNR-tested on SCT dataset
0.3813
0.6128
43.85
47.23
The TTA process introduces an additional 0.23 seconds per image, with a performance improvement of +3.38 PSNR. This trade-off is typical for TTA methods and demonstrates effective adaptation to unseen data. The overhead arises from the model updating its weights better align with the target domain distribution, without requiring ground-truth labels. This extra computation is common in test-time adaptation methods (Kundu et al. 20200 Liu et al.) 2023a Wang al. 2022, [un et al.) 2020 Wang et al. 2020), they rely on on-the-fly -hy-fly optimization to adapt to distribution shifts during inference.
Table 18: Metrics to assess the diversity of different HDR datasets.
PHLP
Fraction of HighLight Pixel Guo et al.
2023
EHI.
Extent Hight ight (Gto 2023
SI
Spatial Information (Sories. 2012]
ColorFFulness
Suesstrunk 2003
stdL.
standard deviation of Luminance (Guo al
2023
ALL
Average Lurninance Level (uuo al 2023
Dynamic Range: the log differences between the highest 2%. luminance and the lowest 28, luminance. [Tu et
2022b
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Table 19: Statistical analysis of data scenarios, time of day, and indoor/outdoor distribution.
Time
Motion Type
Environment
Daylight
Twilight
Night
Indoor
2016
1152
432
Local Motion
Outdoor
2160
1440
1104
Indoor
3360
1920
720
Full Motion
Outdoor
4272
3024
2400
B DATA EXAMPLES OF S2R-HDR
B. MOTION MATERIALS
As demonstrated in Figure 14 the S2R-HDR dataset comprises three principal categories of motion materials: (a) human subjects with a comprehensive coverage of appearance variations, including garment diversity and gender attributes; (b) vehicular objects incorporating distinct transportation modalities with differential motion patterns; and (c) zoological specimens exhibiting biologically plausible locomotion characteristics. These motion materials are sourced from two origins: (1) manually created motion sequences by authors, and (2) pre-defined motion patterns from the Unreal Engine materials we purchased, both of which are designed for integration into environmental contexts to facilitate dynamic motion synthesis.
(a) Human
(b) Vehicle
Animal
(d) Mations (human, vehicie, animal)
Figure 14: Illustration of motion materials.
B.2 HIGH DYNAMIC RANGE ENVIRONMENTS
As illustrated in Figure 15 the S2R-HDR dataset presents a collection of high dynamic range envi- ronments encompassing both indoor and outdoor configurations. Through systematic utilization of Unreal Engine 5's Lumen global illumination system, achieve precise control over environmen- tal lighting parameters. This technical capability enables physics- based synthesis of illumination scenarios spanning three critical lighting regimes: daylight, twilight, and night.
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(a) Daylight
(b) Twilight
(c) Night
Figure 15: Illustration of high dynamic range environments.
Daylight
(b) Twilight
(c) Night
Figure 16: Illustration of image examples of our S2R-
B.3 SYNTHESIS OF CAMERA SHAKE
To enhance the realism of our dataset and simulate inevitable device vibrations encountered in prac- tical imaging scenarios, we introduce controlled camera motion perturbations in selected sequences. Specifically, 30% of the sequences incorporate Perlin noise-based jittering, applied simultaneously both positional coordinates and rotational axes of the camera. The noise frequency and amplitude are adjusted to ensure perceptually plausible motion. This augmentation significantly improves the authenticity of the dataset while expanding kinematic diversity, better approximating real-world camera operation.
B.4 HDR DATASET EVALUATION METRICS
To quantitatively assess the superiority of our dataset compared to real-world datasets, we employ seven evaluation metrics whose detailed definitions provided in Table [8] Specifically, FHLP and EHL measure the extent HDR. SI, CF and stdL quantify intra-frame diversity. ALL and DR evaluate overall style.
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Original Image
Optical Flow
Depth Map
Diffuse Information
Surface Normal
Figure 17: Different image types provided S2R-HDR.
B.5 SCENE AND MOTION DISTRIBUTIONS
Our dataset comprehensively encompasses diverse motion patterns, varied environments, and het- erogeneous environmental illumination conditions. The distribution of different categories across the total collection of 24,000 images is detailed in Table 19
B.6
S2R-HDR IMAGE EXAMPLES
As shown in Figure 16
we present additional image examples of S2R-HDR.
B.7 FURTHER APPLICATION OF S2R-HDR
Leveraging the advanced rendering capabilities of Unreal Engine 5, we have augmented our cus- tomized rendering pipeline with the capability to render multiple specialized image data types. Be- yond producing standard output images, the system simultaneously generates four distinct auxiliary data modalities: optical flow fields, depth maps, diffuse reflectance information, and surface normal vectors. other words, as shown in Figure I7 each frame has its corresponding four additional auxiliary information.
Currently, our dataset is still limited to the HDR fusion task. The provision of such comprehensive supplementary data aims broaden the utility of S2R-HDR, facilitating its extension to diverse application domains beyond HDR imaging.
C
THE USE OF LARGE LANGUAGE MODELS
We only utilize LLMs to polish writing and correct grammar.
26


Reviews:
[{"review_id": "BBUEFFW8nY", "reviewer": "Reviewer_rtUP", "summary": "This paper proposes S2R-HDR, a synthetic dataset of 24,000 HDR images rendered using Unreal Engine 5, representing a large-scale increase over existing datasets. To bridge the synthetic-to-real domain gap, they introduce S2R-Adapter, a dual-branch domain adaptation module with shared and transfer branches. The method demonstrates state-of-the-art performance on the reported benchmarks through both supervised fine-tuning and test-time adaptation", "questions": "- What are the benefits of your approach over the one from (Barua et al., 2025), i.e. could their approach be adjusted to multiple exposures?\n- What kind of features were extracted for t-SNE?\n- Some more statistics on the dataset would be useful, e.g. more analysis on motion (quantifying local and global by presenting optical flow analysis), and some analysis on the number of different environments.", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "- The major strength of the dataset is definitely its scale.\n- The dataset presents a high variety of environments as opposed to real captures that are often in the same environment, or provide a very small number of examples in various environments.\n- The idea of using the adapter for domain adaptation is sound and seems to be working well.\n- The quantitative and qualitative results look convincing.", "weaknesses": "- The authors introduce a shake simulation into camera poses. It would be great to assess whether those are close to the real world. In a fairly easy way, one could record sensor data with a phone capture to compare the two (potentially real patterns could also be transferred to the sim).\n- The authors report the size of the other dataset considering sequences of images, while the presented dataset is reported as the number of frames, which is misleading to the reader.\n- It would be good to see high-resolution images from the dataset; the compressed ones in the paper make it harder to judge quality.\n- I believe there is room for additional experiments to help with assessing the weight of the contributions. In Table 4, we see that training only on S2R-HDR is typically not enough but rather needs fine-tuning on the target dataset. It raises the question whether the scale or data diversity is needed - one could add all datasets to the mix in the comparison. Further, and more importantly, what happens if we take one or more of the real-world datasets to train the approach and then use the adapter on the target data? This would clarify the impact of the dataset a lot.\n- Continuing the argument regarding experimentation with the source data, part of the issues with the real-world dataset is imperfect real ISPs versus the synthetic pipeline. Would the adapter approach work, for e.g. a collection of real-world data (e.g. from YouTube) with a synthetic degradation applied?  \n- It seems that the approach does not work on the Kalantari dataset - Table 11 misses the entry of SAFNet trained on Kalantari (SAFNet reports higher values). It would be better to comment on that instead of skipping completely. Similarly, in Table 10, the authors report 44.13dB on Kalantari, whereas the SCT paper reports 44.49dB.", "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": "qvFWDJCAkl", "reviewer": "Reviewer_2u9g", "summary": "This paper mainly proposes a large-scale synthetic HDR dataset and a post-processing module for domain adaptation between the synthetic and real-world domains. Specifically, a large-scale HDR dataset is essential for training powerful HDR models, especially in the era of large-scale generative models. Compared with existing datasets, the proposed HDR dataset leverages Unreal Engine 5 to synthesize data, resulting in 24,000 high-quality HDR frames featuring diverse motion types, lighting conditions, and both indoor and outdoor scenes. Current HDR models trained on small real datasets often struggle with large-scale motion and diverse lighting conditions, whereas models trained on the proposed large-scale dataset achieve significantly better performance.\n\nRegarding the proposed post-processing module, called S2R-Adapter, it aims to reduce the domain gap between synthetic and real-world data. This module can be used in two modes: 1). fine-tuning mode, which requires labeled datasets for supervised training; 2). self-supervised mode (or test-time mode), which can be applied to unlabeled data without ground-truth images.\n\nExperimental results demonstrate that the proposed large-scale synthetic HDR dataset effectively improves the performance of existing HDR fusion models. They also show that the proposed dataset and adapter achieve better generalization across different datasets.\n\nOverall, the quality of this paper is above the borderline and it is likely to be accepted. However, several questions and concerns remain are  listed in below sessions.", "questions": "I jointly discuss the weakness and questions on this paper in the Weakness section. The authors can prepare their rebuttal referred to the comments in the Weakness section.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "1. The motivation of this paper is very clear, which aims to tackles the dataset bottleneck in HDE imaging based on multiple-image fusion.\n2.  It also point out the challenging issue regarding to domain gap between the synthesized and real-world data, and propose S2R-adapter to handle this issue.\n3. The paper demonstrates comprehensive experiment results, which are promising.", "weaknesses": "1. One of the major contributions of this paper is the proposed large-scale dataset, but the paper does not clearly describe how this dataset is created. For example, it only mentions that Unreal Engine 5 is used with varying tone-mapping and gamma-correction parameters to render different data, but the explicit pipeline for dataset generation is missing. Why does this synthetic pipeline effectively produce data with diverse lighting conditions and object motions? Is this dataset curation pipeline reproducible?\n\n2. In the S2R-Adapter framework, the paper shows that it uses a diffusion model as the backbone. Which specific diffusion model is used in the experiments? If the S2R-Adapter is employed, what is the resulting model complexity and inference time? During fine-tuning, how large a dataset is required to train the adapter effectively? It would be better if the experimental settings elaborated more on the training details.\n\n3. The S2R-Adapter can be used in self-supervised mode, and the paper mentions that “we dynamically adjust the scale factors using domain shift.” It is unclear how the domain shift is measured and how the model determines whether the domain shift is large or small. The paper refers to data augmentation and uncertainty estimation — but why does this strategy effectively measure domain shift and determine its magnitude?\n\n4. In Table 2, it would be better to include the model complexity and inference time for all compared models. Additionally, how many adaptation steps does the proposed S2R-Adapter use in the experiments? For the self-supervised mode, how does the performance vary across different datasets?", "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": "9YIFQN9sJv", "reviewer": "Reviewer_Ug55", "summary": "The core contribution of this paper is addressing the generalization challenge in HDR fusion models caused by the scarcity of real-world data. By introducing the first large-scale synthetic dataset (S2R-HDR) containing 24,000 high-quality HDR samples rendered using Unreal Engine 5, which covers diverse dynamic scenes and lighting conditions, along with an innovative domain adaptation method (S2R-Adapter) that balances synthetic and real-world data knowledge through a dual-branch design, the model achieves state-of-the-art performance on real-world datasets. Through comprehensive experiments on real-world datasets, the combined use of S2R-HDR and S2R-Adapter enables models to achieve state-of-the-art performance, significantly reducing motion artifacts like ghosting and enhancing the restoration of details in extreme illumination conditions.", "questions": "Q1: Is the first claim accurate? The dataset compiled by Barua et al. contains 40,000 HDR images, resulting in a total dataset size of 1M. The volume of this data significantly exceeds that of your dataset.\nQ2:Why is the dataset from Barua et al. unsuitable for multi-exposure HDR image reconstruction? The author should revise the introduction section regarding the description of the Barua et al. dataset, preferably providing a detailed comparison between this dataset and the proposed dataset in the paper. The dataset from Barua et al. includes multi-exposure data.\nQ3: The authors need to elaborate on the specific rendering process using UE5, including scene creation and virtual camera capture. Could a corresponding flowchart be provided? If acceptable, could the process script also be included?\nQ4: Why can the shared branch be utilized to preserve knowledge for rendering data? Why can the transfer branch learn domain-specific knowledge? The authors do not appear to provide detailed theoretical proofs, offering only some explanations in the experimental section. I could not find the basis for the corresponding effects of these two modules in the paper.", "limitations": "", "rating": 6, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 3, "strengths": "This study constructs a novel dataset for multi-exposure HDR fusion tasks, which contributes to the advancement of this research field. This dataset comprises diverse types of image data captured under various environmental conditions and exposure levels, demonstrating considerable diversity. Additionally, to address the discrepancy between synthetic and real data, this paper propose the S2R-Adapter as an effective solution for mitigating the domain gap between synthetic and real-world data. The two branches of this module are particularly noteworthy, as the shared branch and the transfer branch can effectively reduce the domain gap.This paper conducted both supervised and unsupervised experiments, and the experiments appear to be sufficiently comprehensive.", "weaknesses": "In the introduction, the author states that the dataset proposed by Barua et al. is designed for single-image LDR to HDR conversion tasks. However, this assertion appears to be inaccurate, as the dataset actually includes corresponding multi-exposure LDR images. In fact, it encompasses variations in color hues, saturation, exposure, and contrast levels. It is worth noting that this dataset is exceptionally large, containing 40K HDR image. Since this dataset is based on GTA, it inherently includes features such as vehicles, humans, and varying lighting conditions. The author only describes the practices of shared branches and transfer branches, but throughout the entire paper, I cannot fully comprehend how these two branches achieve their intended functions. What is the theoretical basis for this? Or is it solely validated through experimental results? In addition, it appears that the author has omitted the UE5 dataset construction flowchart.", "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": "bXQ3lrgknP", "reviewer": "Reviewer_cNjM", "summary": "This paper proposes a method for generating image data in the field of HDR fusion using the Unreal Engine 5. To address the domain gap between synthetic and real data, it also introduces a domain adaptation approach applicable to both labeled and unlabeled datasets.", "questions": "1. The authors emphasize the efficiency of their method. Could you provide the additional computational cost (for both training and inference) introduced by the domain adaptor? Specifically, during inference, if the method necessarily requires additional test-time-training from the adaptor for every new scenarios (e.g., day, night, dusk), the claimed efficiency might be compromised. Clarifying this practical overhead is crucial.\n\n\n2. The LDR data in the Unreal Engine 5 synthetic dataset is generated via data augmentation, but the process (e.g., adding noise) is not detailed. For instance, the LDR examples in Figure 3 show no noise variation across exposures. This lack of realistic degradation might be a key reason for the poor generalization when trained solely on the proposed dataset. Enhancing the data synthesis pipeline with a more realistic degradation model could significantly boost the dataset's generalization capability and contribution.\n\n\n3. Abundant HDR data from professional film-grade equipment can be acquired on the internet. What is the specific advantage of the dataset proposed in this work compared to these readily available sources? Please elaborate on its unique value.", "limitations": "", "rating": 6, "confidence": 5, "soundness": 3, "presentation": 3, "contribution": 2, "strengths": "1. The arguments in the paper are well-grounded, and the writing is logically rigorous and easy to follow.\n\n\n2. The proposed dataset offers a richer variety of HDR scenes, substantiated by partial visualizations and extensive quantitative comparisons. When combined with the strongly generalized domain adaptation method, it further enhances the performance in the field of HDR fusion.", "weaknesses": "The paper does not discuss the limitations of the proposed method. Is it effective in all scenarios? Please specify any potential constraints and provide examples of failure cases, which would help readers understand the method's boundaries.", "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)