Image Stitching in Adverse Condition: A Bidirectional-Consistency Learning Framework and Benchmark
Authors: Zengxi Zhang
Junchen Ge
Zhiying Jiang
Miao Zhang
Jinyuan Liu
Venue: NeurIPS 2025 poster ()
Keywords: image stitching
deep learning
computervision
Primary area: applications

Abstract: Deep learning-based image stitching methods have achieved promising performance on conventional stitching datasets. However, real-world scenarios may introduce challenges such as complex weather conditions, illumination variations, and dynamic scene motion, which severely degrade image quality and lead to significant misalignment in stitching results. To solve this problem, we propose an adverse condition-tolerant image stitching network, dubbed ACDIS. We first introduce a bidirectional consistency learning framework, which ensures reliable alignment through an iterative optimization paradigm that integrates differentiable image restoration and Gaussian-distribute encoded homography estimation. Subsequently, we incorporate motion constraints into the seamless composition network to produce robust stitching results without interference from moving scenes. We further propose the first adverse scene image stitching dataset, which covers diverse parallax and scenes under low-light, haze, and underwater environments. Extensive experiments show that the proposed method can generate visually pleasing stitched images under adverse conditions, outperforming state-of-the-art methods.

Image Stitching in Adverse Condition: A Bidirectionll-Cnnisttenyyy Learning Framework and Benchmark
Zengxi Zhang The University of Tokyo
Junchen Ge Tsinghua University junchen54ge@gmail. com
cyouzoukyuudgmail.c com
Zhiying Jiang Dalian Martime University zyj 1yjia0063000m..1. com
Miao Zhang Dalian University of Technology
mi miaozhangodlut. edu. cn
Jinyuan Liu"
Dalian University of Technology
stlantis91@hhomme com
Abstract
Deep learningbbased image stitching methods have achieved promising perfor- mance on conventional stitching datasets. However, real-world scenarios may introduce challenges such as complex weather conditions, illumination variations, and dynamic scene motion, which severely degrade image quality and lead to significant misalignment in stitching results. To solve this problem, propose an adverse condition-toleran image stitching network, dubbed ACDIS. We first introduce bidirectional consistency learning framework, which ensures reliable aligument through an iterative optimization paradigm that integrates differentiable image restoration and bauusian-distribie encoded homography estimation. Subse- quently, we incorporate motion constraints into the seamless composition network produce robust stitching results without interference from moving scenes. We further propose the first adverse scene image stitching dataset, which covers diverse parallax and scenes under low-light, haze, and underwater environments, Extensive experiments show that the proposed method can generate visually pleasing stitched images under adverse conditions, outperforming state-of-the-arr methods. Code and benchmark are available at
Introduction
Image stitching aims to construct wide field-of-view (FoV) scene from multiple images captured in different viewpoints. It is widely used various applications such as autonomous driving [.. map construction (2) and virtual reality B] Although it has achieved rapid development in recent years, aligning images over large disparity views, visually degraded environments and dynamic scenes still remains challenging.
Early stitching methods 5 predominantly relied on SIFT-based feature detection 7 to estimate parametric warping models. However, they fall short in low-texture regions or scenes with repetitive geometric patterns. In recent years, learning-basee methods 9 10 replace handcrafted feature
"Corresponding Author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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F F
(9) Conventional alignment strlegy
Our aligument stralegy
(c) The proposed Advense Somee Image Stiching Datasel
Figure 1: (a) and (b) compare the alignment process of the traditional approach and proposed strategy in challenging environments. The progression from top to bottom illustrates the refinement of alignment from coarse to fine, where the quadrilateral boxes denote regions corresponding the same scene. The line charts on the left depict the current intensity difference of the restoration effect within the respective boxes. (c) presents the baseline and scene distribution of the proposed dataset.
extraction with deep semantic representations, enabling more robust alignment. Nevertheless, they still struggle to achieve satisfactory results in adverse environments (e.g., low-light, haze, underwater, and dynamic scenes). These conditions corrupt the image pairs through noise, color casts, or scene variation, leading to information distortion, which in turn affects feature matching and reconstruction.
To address this issue, some methods
3 employ image stitching by introducing environment- insensitive multi-modal data to alleviate the impact of degradation factors. However, these cannot be applied in general scenarios due their strong data dependency. Furthermore, few methods mitigate image degradation in adverse environments without introducing additional information. On the other hand, existing stitching datasets [14] 9 are mainly collected under ideal natural light conditions and lack guiding reference, making it difficult to evaluate stitching tasks in degraded scenes.
To overcome the above limitations, in this paper, we propose robust deep image stitching network for adverse conditions (ACDIS), which consists of two stages: In the first stage, we introduce bidirectional-consitteee learning framework to achieve robust image aligmment under harsh environment. Specifically, considering that conventional homography estimation methods with deterministic displacement outputs often produce unreliable predictions in textureless or blurry regions, we begin by proposing a recursive parameterized homography estimation module that models displacements in Cauusian-distriuuted transformation space. By jointly predicting the mean and variance, the model explicitly encodes uncertainty and mitigates overconfident regression. A Jensen-Shannon divergence-based optimization further refines the displacement distribution, yielding stable and reliable homography estimation.
Traditional perception methods in adverse conditions typically treat visual enhancement [1] 16 [8) as a preprocessing step, as illustrated in Fig. [ (a). However, as depicted in the line chart, this straightforward concatenation strategy may introduce irreducible restoration discrepancies, ultimately disrupting feature matching. To address this, as depicted Fig. [ (b), we embed a differentiable restoration module within the iterative homography estimation pipeline, enabling bidirectional optimization of both restoration and alignment. As shown in the corresponding line chart, this mechanism progressively harmonizes the scene structure, enhancing restoration consistency in iterative deformation processes, thereby achieving more reliable alignment.
In the second stage, propose motion-tolerant seamless composition network, which introduces motion loss to reduce the impact of moving object displacement caused by inconsistent image capturing times while generating clear stitched images. For evaluating the performance of stitching methods in harsh environments, we additionally propose the first adverse scene image stitching dataset (ASIS), which covers three harsh environments: low-light, haze, and underwater. ASIS includes 17 scenes with a total of 2,250 pairs of images, which are derived from manual capturing and network collection. All the image pairs are far from the plane strueture and under wide parallax range. In addition, we pre-align all image pairs to obtain the reference homography for a more comprehensive evaluation. In summary, our contributions are follows:
A recursive parameterized homography estimation module is proposed that iteratively encodes displacement within a Gaursian-distributted transformation space, progressively refining reliable homography transformation.

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(o) Bidirectional Consistency-Driven Alignment Module (BCDA)
(b) Motion- Teleront Seamless Composition (MSC)
R2HE
RHHE
Enc.
Inputs
Slax
Dec.
Mart
(c) Recursive Parameterized Homagraphy Estimation (RPHE)
Enc.
Frer
(d) Differentiible Image Restoration (DIR)
Uptnne
Resize
HP
DC5
Gomma
Tonk
Contrrc
Somplny Bcl
Layer
Hiney-phy Agy yetee
Opertiio
Frudicur
White
Inputs
bifferentiable Blocks (Das)
Output
Figure 2: Workflow of the proposed Method.
We propose bidirectional-consisteee learning framework, which achieves adverse condition-tolerant homography estimation by embedding lightweight differentiable restora- tion blocks into iterative alignment process.
A motion-tolerant seamless composition network is introduced that generates visually pleasing stitching images while avoiding the interference of moving objects on temporally inconsistent image pairs.
We release the first real-world adverse environment image stitching dataset, which contains 2,250 degraded image pairs with homography reference evaluate the effectiveness of stitching methods in adverse conditions.
Related Work
2.1 Image Stitching Methods
Early traditional stitching works 5
19
20
21
22 constructed global adaptive warping
by using SIFT 7 to extract key points. Brown et al. used multi-image matching technology in image stitching tasks and combined multi-band mixing generate panoramas. Zaragoza et al. [14] fine-tuned the warping projection by moving direct linear transformations to reduce artifacts during warping, therehy reducing the reliance ghost concealment algorithms. Peng al. 23 introduced edge detection and sampled edges to construct triangles representing geometric structures. Then, a similarity transformation is performed by combining the geometric structure preserving energy term.
recent years, some works have applied deep learning networks S 2 12] [0 to the stitching task. Nie et al. [9] proposed an unsupervised image stitching method and introduced seam loss during reconstruction process to eliminate the pixel-level misalignment. Then, they [IO] employed seam-driven mask regions. Although these methods have proven effective, in practice, immeasurable environmental factors often affect the captured images, thus greatly affecting the stitching effect.
alternative, effectively preserving structural coherence across the stitched
2.2 Image Stitching Datasets
25 have been proposed to more comprehensively evaluate the effect of image stitching. Nie et al. 8 synthesized a stitching dataset with ground-truth by cropping and warping existing image datasets. Subsequently, considering the lack of disparity in synthetic datasets, they [9] additionally provided a weal-world unsupervised dataset UDIS-D DD.. However, as the source data are captured instead of synthesized, the ground truth of corresponding FoV scenes cannot be obtained, limiting quantitative evaluation. Kweon et al. [26] then proposed stitching dataset with pixel-level warping and ground-truth stitching results, which simulated the real-world stitching by collected data from 3D virtual scenes. However, except for small number of low-light images provided by UDIS-D, there currently no stitching dataset specifically used to evaluate the robustness of the image stitching network in harsh environments.
In recent years, many datasets 5 21 19

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3 The Proposed Method
Our method consists of two stages: Bidirectional Consistency-Drivve Alignment Module (BCDA) and Motion-Tolerant Seamless Composition (MSC), as shown in Fig. 2 {dref, 3arr denotes images captured in adverse environments, where ref and tar represent the reference and target viewpoint.
BCDA comprises Recursive Parameterized Homography Estimation Module (RPHE) and Differ- entiable Image Restoration (DIR). RPHE alleviates the challenge of spatially limited dynamic cost volume with large baseline image pairs by sampling offset candidate values by mean and variance in 2D directions. DIR is designed to mitigate the interference of degradation factors feature extraction. It applies CNN-encoded hyperparameters on differentiable restoration blocks to achieve lightweight and robust image restoration. Rather than applying the restoration module a preprocess- ing step for alignment, we establish a Didirectionll-Consistenng Learning Framework (BCLF), where DIR is embedded within the coarse-to-fine deformation estimation process, forming a progressive bidirectional optimization pipeline. Ultimately, BCDA achieves region-asymptotical image pair restoration and robust image alignment. After obtaining { [arr by warping restored images, we input them into MSC to generate motion-toleran wide field-of-view scene 7 Next, provide detailed information about each module.
consistent
3.1 Recursive Parameterized Homography Estimation
apply dynamic cost volume C(x) E. with search redius to match difference between (z, y) of Lref and (x: = r, y r) of Iear for reducing the memory consumption. However, spatial limited cost volume is not conducive to convergence in large baseline scenarios. Furthermore, these methods based on deterministic displacement outputs tend to produce unreliable predictions in textureless or blurry regions, and perform poorly in image stitching tasks under adverse conditions such as heavy fog or low light.
Some traditional stitching methods 9
27
28
29
To address this, modeling displacement space d as a Gaussian distribution provides a more robust alternative. The proposed model predicts not only the mean (displacement) but also the variance (as an uncertainty estimate [30), allowing it to express high uncertainty in ambiguous or ill-posed regions and avoid overconfident regression to potentially incorrect values. Specifically, the cost volume will sample candidate offsets by mean jt and standard deviation o from each offset:
C(i, Us, o.) = c. dlui,ss) d(Hi, o:) ~ N (Hi,o?)
(I)
where represents the sampling from Gaussian distribution N. i E {x, y). x and denotes the horizontal and vertical displacements.
As shown in Fig. 2 we first sampling candidate offsets (4s, 14 + o; and μ - a, from the initialized offsets in each iteration. Then, we transform them all into the homography H, H+ and H- via Direct Linear Transformation (DLT) for warping the original images respectively. Subsequently, we input the warped images into the DIR (3.2) and pyramid CNN to extract features resilient adverse environments. Through Attention Layer (AL) [31] and Homography Aggregation Module (HA) [31], we finally obtain the residual μ for updating. The detailed structure of CNN, AL and HA is illustrated in supplementary materials.
In order effectively learn the parameters 0 {u, o of the Gaussian distribution, use JS divergence (32) based optimization J, which forces to gradually approach the Gaussian distribution during the optimization process:
1 J. J.= (F (N + F (AIIlN
where N₁ is the short form of N o?). F--1I-) represents KL divergence N (per of ) denotes predefined Gaussian distribution, where Mer represents the ground truth offset. We can then update 0 via feedforward gradient optimization 33 35 in each iteration l:
33
N
1 (a GT
1-
(Her M4 1
of = of
1-1
2
(0
11/3133
2
GT
(3)
Hor M 1-1
1
1
14
11-1
(o:
1/3
CT

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Considering that Per is not available during inference, we replaced the Per by approximate the optimizing step A = Mer p which is estimated in each iteration. By updating 0 in N iterations, we can finally estimate a robust homography that is suitable for large baseline scenes in harsh conditions. L11 loss is used during the training stage with the ground truth of the offset in N iterations, which can be described as:
Σ (N- 10' Marl.
L1
(4)
3.2 Bidirecioonll-Cnnisteeyyy Learning Framework
When conducting computer vision tasks in adverse conditions, previous works [36] 37 38 usually directly employ restoration networks as the preprocessing step for the current task. However, when faced with stitching tasks, restoration networks encounter two main challenges: (1) Data-driven networks often overlook the physical imaging priors of images under adverse scenes, resulting in visually insensitive artifacts that implicitly disrupt the geometry structure of the images. (2) Restoration networks may exhibit varying degrees of effectiveness in restoring images from different perspectives. Both of these challenges can affect the feature matching between image pairs, thereby influencing warping estimation.
To address the first challenge, we propose Differentiable Image Restoration Module (DIR), which includes a Hyperparameter Predictor (HP) and Differentiable Restoration Blocks (DBs), First, HP learns the global information of the resized input image to obtain hyperparameters. Then, we used the obtained hyperparameters as weights in DBs to achieve adaptive image restoration.
DBs consists of Medium Scattering Restoration (MSR), White Balance (WB), Gamma, Hue, Contrast and Sharpen. MSR estimates the atmospheric light A and the transmission map t(x) through the atmospheric light imaging model
[39 to restore the absorption and scattering of light in the gas liquid medium, which is expressed as T(x) = (x))((x) + A(1 - t(x)), where / and J denotes the degraded image and its clear counterpart. A can be obtained by calculating the first 1000 brightest
pixels in the dark channel and averaging the pixels at the corresponding positions of the original image. ((x) can be obtained according to the Beer-Lambert law [40 41), which is described
19())
(xr, w) = 1 w min min (EE(())
AC
where w is an hyperparameter that controls the degree of restoration, which is obtained by HP.
WB and Gamma are pixel-level restoration blocks. Among them, 41,, 4h. are used as hyper- parameters of HP for mapping of three channel pixels. G is the hyperparameter of Gamma for the weight of the power function. Tone restoration can be expressed
channel-independent monotonic piecewise linear function 422 The point (k/ M, TR/TMM on the tone curve is represented by M
hyperparameters (to. ..., tM-1), where Tk- Σ t,. The mapping process can be expressed as Lo L En'o" clip (L- 1, - j. 0, 1) tx, where Lo and 1, represent the output and the input image. Contrast restoration determines the linear difference between the I, and I through the hyperparameter a, which is expressed as Io = En (I:) + (1 a) J. where En is the mapping process (42 The sharpening [43] process is described as F(x, X) = T(x) ((T(r) G(((r))), where G represents Gaussian filter. The hyperparameter À is positive scaling factor control the prominence of details.
Txr
In summary, different from traditional data-driven networks, DIR leverages physical priors adap- tively restore images in various adverse environments while minimizing interference, ensuring more accurate feature matching. We use L1 loss L1 and SSIM loss L, with the ground truth image a in the training process, which can be described as:
Cres (To, Tor) + >T ToT)
Although artifacts can be reduced with physical prior, consistent restoration of images from different perspective still remains challenging. Therefore, we further build Bidirectional-Cnniitte Learning Framework. Specifically, we use DIR to restore the warped images at each iteration, rather than just once as preprocessing means only before homography estimation process, achieving in equivariant restoration. This framework can be expressed as:
E + (W (Tar: Hr*)))))) , V (Trefi E
5

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6:43 4 16
188 9 3
(216.
(5.00 4.82 1.00
Stin Oytinal homography
(o) SIFT-based feuture point matching
(b) Correct match feature pairs filter
(c) Manual selection feature pairs
(d) RANSAC-bused optimal homography estimation
Figure 4: Reference generation process of the newly proposed ASIS dataset.
where and V denote the restoration and the homography estimation and module. F and H represent the enhanced warping images and current homography in the n-th iteration. Since the computation cost of DIR is only 0.07 GFlops at each restoration, it will not significantly increase the time consumption in embedding it in the iteration. As the iteration proceeds, f becomes more aligned with fref. enabling DIR to increasingly achieve consistent restoration of overlapping regions, which further improves homography estimation.
3.3 Motion-Toles Tolerant Seamless Composition
Inspired by UDIS2 [0]. this stage generates a soft mask with floating point numbers and synthesizes a seamless stitched image 1 by I = M
Time coc.eco
The + M x har U-Net [44] is applied as the network structure for generating composition masks {M Maa }. Since the image pairs are usually captured at different times, the presence of moving objects can affect the visual appearance of the stitched image, especially in the seam region. As shown in (a) of Fig. 3 taking human subject as example, when capturing images from two viewpoints, the man in the scene may move during the interval, causing him to appear different positions across the two viewpoints. order to avoid unsmooth stitching result with ghost caused by above challenges, we further introduce motion loss LM in the training process of MSC to improve the robustness to moving objects, which can be described as follows:
1
Mov mask
(a) Moving object challenge
(b) Moving mask generation
Figure The generation process of motion mask during image capturing.
LM W1 M MM + W2 I M M Iarll
(8)
where (wi, w2) = S(D(M; 1o), D(M I²)), which controls the composition weight of MSC the region moving objects between f and f M denotes the seam mask, which is introduced in supplementary materials. S denotes the Softmax operation. D represents the 2D adjacent difference, which can be illustrated as follows:
D(I) Σ [[i.i + - I(i.)) + Eu(++ I(i.j).
(9)
where i and j represent the location index in and y axis. n denotes the motion mask of the warping images, which can be described as follows:
M M (B (1IWe - 1. Φ) .k)..
(10)
where B denotes the binarization operation with the threshold ø. M represents the morphology filter. is the kernel of M with the size of 3. Therefore, under the constraints of LM- MSC can adaptively ignore the interference of moving objects appearing in overlapping areas on scamless composition, thereby generating visually pleasing stitched images. We additionally apply boundary loss [10) LB and smoothness loss [0] Ls to make the stitched images smooth and clear. The final composition loss is expressed as follows:
Lcom = X4LB + A5CC AdCM-
(1I)

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Input
APAP
VFIS
LPC
UDIS
UDIS2
Ours
Figure Visualization results on the ASIS Dataset.
4 The Proposed Dataset
In order to comprchensively evaluate image stitching tasks in various harsh environments, we released an Adverse Scene Image Stitching Dataset (ASIS) that integrates in low light, haze and underwater environments. A visual representation of the dataset is shown in (c) of Fig. [ ASIS contains 2,250 pairs of images, including 750 images each of low-light, underwater, and haze environments, covering 17 scenes such as caves, wrecks and fields. It worth mentioning that some of the sources these data come from the Internet and some come from independent photography. The captured images far from planar structures, which ensures the disparity diversity of the proposed ASIS dataset. More detailed information of ASIS is demonstrated in supplementary materials.
To verify the warping performance of different methods, we provided the homography reference for ASIS. Inspired by [45] the generation process of the reference is illustrated in Fig. We first match image pairs using SIFT [46], however, due to image degradation caused by adverse conditions, usually suffers from low number and accuracy of matched feature points. Therefore, filter correct matching pairs to prevent the generation of outliers. Moreover, as shown in Fig (b), generate reliable homography.
manually
where the number of correctly matched pairs is insufficient Thus, we further select new matching pairs to achieve a more robust homography generation. After obtaining matching pairs, apply Random sample consensus (RANSAC) [47] to generate the optimal homography and perform preliminary distortion to verify the performance of the reference.
Experiments
5.1
Implement Details
The training process is divided into three steps. First, we pre-train the DIR module with a learning rate of le-4 and an epoch of 200. LSRW [48] and UIEBD [49] are used as training datasets for low-light and underwater image enhancement tasks respectively. We also synthesize the training dataset for single image dehazing from VOC (50) according to the atmospheric scattering model [51]. Subsequently, we trained the BCDA module with DIR with an epoch of 160. We then use the above datasets to synthesize the homography training datasets through the synthesis method of VFIS (8]. During the training process, the μ and are initialized to and 32 respectively. M, N, Ger are set to 8, 6. 2 and 20. Batch size and learning rate are set to 16 and 5e-5. Finally, we trained the MSC with learning rate of le-4 and an epoch of 100. A X2, As. ds .As and Ag are set to 2, 100, 100, and I. All the experiments conducted on Torch with NVIDIA RTX 4090 GPU.

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Table 1: Quantitative comparison for image stitching under adverse environment. 1 indicates that higher values correspond to superior outcomes. The cop-performing and second -eot results are highlighted in red and blue.
Low Low-light Environment
Low-light Image
HBLAALS
NeRCo
Method
Ours
APAP
VFIS
LPC
UDIS
UDIS2
APAP
VFIS
LPC
UDIS
UDIS2
APAP
VHI
LPCC
UDIS
UDIS2
PSNR-
25.792
23.002
24.465
24.910
24.742
27.461
22.048
25.346
25.867 25,890
28,244
22.244
25.717
26.340
26.053
30,506
SSIM
0.876
0.869
0.879
0.914
0.905
0.897
0.835
0.887
0.919
0.914
0.906
0.863
0.883
0.935
0.927
0.939
SIQE
10.352
12.329
10.861
11.372
11.493
8.786
11.897
11.062
10.197
10:437
8.507
12.578
10.837
11.089
9.688
12.854
NIQE_
4.402
3.671
4.402
4.261
4.679
4,500
3.312
3.655 3.596
4.234
4.168
3.263
4.350
4.178
5.103
3.186
Underwater Environment
Underwater Image
HBLALS
WaterFlow
Method
Ours
APAP
VFIS
LPC
UDIS
UDIS2
APAP
VFIS
LPC
UDIS
UDIS2
APAP
VFIS
LPC
UDIS
UDIS2
PSNR
24.631
23.740
23.054
105
25.354
25.192
22.399
23.299
24.851
24.897
24.251
21.276
22.458
23.706
24.776
28.607
SSIM
0.856
0.838
0.826
0.876
0.889
0.863
0.796
0.823
0.863
0.868
0.862
0.787
0.826
0.806
0.861
0.918
SIQE
5.657
5.668
4.012
5.646
6.115
5.456
6.089
5.313
5.901
6.152
4.934
5.998
5.919
5.086
6.057
6.444
NIQE
4.210
3.998
4.510
4.941
6.444
3,918
3.531
4.000 4.49
5.849
4.108
3.715
4.203
4.586
6.067
4.202
Haze Environment
Haze Image
HBLALS
WGWS
Method
Ours
APAP
VFIS
LPC
UUDIS
UDIS2
APAP
VFIS
LPC
UDIS
UDIS2
APAP
VFIS
LPC
UDIS
UDIS2
PSNR
25.065
22.037
23:657
23.532
24. 69.990
27.615
23.969
135
26.009
27.065
27.530
24.038
26.380
25.836
27.245
31.136
SSIM
0.911
0.866
0,900
0.909
0.928
0.941
0.869
0.928
0.921
0.939
0.936
0.878
0.929
0.922
0.941
0.963
SIQE
7.785
6.817
6,251
6.970
7.637
6.885
7.524
6.798
7.009
7.120
7.908
8.000
7.361
7,470
7.510
8.057
NIQE
5.632
5.297
5.681
5.629
6.224
5.255
4.972
5.311
5.434
5.990
5.687
5.160
5.747
5.547
6.190
4.970
5.2
Comparison with Existing Methods
This method mainly studies the stitching algorithm in low-light, haze and underwater environments.
For fair comparison, we first use a unified restoration framework HBLALS [52 and environment- specific restoration methods NeRCo (53). WaterFlow (54) and WGWS (55) respectively to obtain images closer to ideal conditions. We then applied the these clear images representative stitching algorithms, including traditional 44) LPC [56), and deep learning-bac
VFIS [8. UDIS ()) and UDIS2 [1O]. It is worth mentioning that also applied the stitching algorithm to the original images without restoration to verify the effectiveness of the restoration method for image stitching.
The first row in Fig. shows the stitching comparison of images restored using HBLALS in low-light scenes. Given the resolution differences across stitching methods, we resize them just for a consistent and aesthetic display. APAP fails to achieve a smooth transition, resulting in obvious seams. VFIS, UDIS and UDIS2 are severely misaligned the green zoom-in region. LPC breaks the consistency of the road structure in the red zoom-in region. The second row shows the stitching results in the underwater environment, where severe color distortions and similar rock textures impact the stitching performance. LPC causes the loss of distinctive details from the reference image. VFIS and UDIS2 disrupt the reef structures in the overlapping region, resulting in visually unappealing seams. In UDIS, the information within the red box exhibits noticeable blurring, while APAP introduces prominent artifacts in the red box and visible seams in the green box. The third row shows the visualization in the haze environment. Most methods suffer from significant misalignment during the stitching process, resulting in a loss of scene information. Only APAP correctly estimates the image distortion, however introducing significant visual interference at the seam region.
Tab. [ shows the quantitative results of all methods. For a comprehensive comparison of the proposed methods, we verify the alignment effects by calculating PSNR and SSIM on the overlapping regions, Larger values represent better warping effects. Subsequently, the stitching effect is evaluated through the stitched image quality evaluator (SIQE) [57]. We additionally quoted the Natural Image Quality Evaluator (NIQE) to evaluate the quality of the reconstructed image. A higher SIQE index indicates better stitching effect, and lower NIQE index indicates better image quality.
evident that the proposed method outperforms all others in PSNR and SSIM, validating its effectiveness in warping estimation. Additionally, the SIQE of the proposed method also exceeds other methods in all adverse environments, confirming that our method can greatly reduce the impact of image degradation during stitching. Our NIQE surpasses all methods in low-light and
8

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MACE (+01)
PSNR (x1)
SSIM (x30)
SIQE (x2)
NIQE (x8)
30
25
20
Wio DIR wio BCLF Our
wio DIR w/o BCLF Cluss
wio DIR
wio BCLF
Our
Oun-GC
Ours
wio wio wio Ours
(a)
(b)
(c)
(d)
(e)
Figure 7: Evaluation ablation experiment.
haze environments but does exhibit advantages in underwater environment. This is because underwater scene images are mostly captured close range with large disparities between image pairs. Therefore, correct warping leads excessive invalid black regions in the generated images, affecting the evaluation of image quality metrics. Considering all metrics, our method achieves significant advantages in warping and stitching performance under adverse conditions.
5.3
Ablation Study
5.3.1 Study on BidirectionalCCnnisteeyy Learning Framework:
We conducted ablation studies across different environments to verify the effectiveness of the learning framework. Specifically, we first aban- doned DIR (wo DIR) to verify the effect of image restoration the proposed method. Subsequently, instead of employing BCLF, we only concatenate DIR with homography estimation module (w/o BCLF) to verify the effect of our learning frame- work. We additionally utilized the mean Aver- age Corner Error (mACE) [59] to validate the performance of the homography estimation. A smaller mACE indicates a more accurate estima- tion. Fig. G illustrates the performance in three adverse environments. It is worth noting that fine details in degradation scenes are difficult to discern by the human eye. Therefore, we additionally restored the results of the unenhanced version in qualitative comparison to facilitate readers in comparing the stitching effects.
Input w/o DIR
w/o BCLF
Ours
Figure 6: Visualization results for the study on DIR and BCLF.
In the low-light example, w/o DIR produces artifacts on the floor tiles pointed by the red arrows, destroying the original texture. w/o BCLF suffers from a clear misalignment on the path pointed by the green arrow. In the underwater environment, facing reef group with similar structure, neither w/o DIR nor w/o BCLF considers of the correspondence between the input pairs, resulting in misalignment of the stitching results. In the haze environment, both w/o DIR and w/o BCLF misestimate the deformation, ultimately causing the results lose a large amount of scene information. (a)-(c) of Fig. depict the quantitative results of the scenarios mentioned above. In summary, the proposed enhancement method demonstrates significant effectiveness under adverse environments.
5.3.2 Study Recursive Parameterized Homography Estimation:
We further conducted ablation studies RPHE to validate its performance in homography estima- tion. Specifically, we adopted traditional global correlation (Ours-GC) [9] and dynamic correla- tion (Ours-DC) [TO] as a replacement for the pa- rameterized cost volume. Fig. presentss the visual results in low-light environment. The yellow rect- angle indicates the overlap ratio of image pairs The image pairs in the first example face the chal- lenge of low overlap ratio. Compared with the ours stitching result, both results Ours-GC and Ours-GC fails matching the limited scene struc-
Input
Ours-GC
Ours-DC
Ours
Figure 8: Visualization results for the ablation study on the RPHE.

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ture. In the second example, the overlapping region mainly consists of ground areas with fewer fine details, posing significant challenges for stitching. Ours-GC suffers from significant loss of scene information, while noticeable misalignment occurs in Ours-DC. Only the proposed method achieves accurate stitching results in limited effective areas. Quantitative results of this ablation study are shown in Fig. (d), which also validate the advantages of the proposed homography estimation strategy in all metrics.
5.3.3 Study on Motion-Tolerant Seamless Composition:
We ablate LB. Ls. and LM respectively to ver- ify the effect of losses on the image composition stage. The visualization results In this example, due to the time difference in im- age capturing, the character in the red frame has moved from the back of the car to the front of the car. The reconstructed scenes of w/o CB, w/o Ls and w/o Lx all show the same person at different times in the stitched images. At the same time, both w/o Ls and w/o LM suffer from serious in- formation loss in the human leg. Furthermore, as shown in the green frame, obvious seam differ- ences appears in the results w/o LB and w/o Cs. Only the proposed method ensures the temporal uniformity while smoothing the seam difference. (e) of Fig. 7 shows the qualitative metrics of the reconstruction module. Since all experiments are performed under the same image warping, we only use image quality metrics for evaluation. Both qualitative and quantitative experiments demonstrate the effectiveness of the proposed constraints for reconstructing the network.
shown in Fig.
Input
Input
w/o LB
w/o Ls.
w/o Lm
Ours
Figure 9: Visualization results for the ablation study on the MSC.
Limitations
The proposed motion constraint only improves stitching performance for small-range movements in dynamic scenes. For instance, when pedestrian moves within the overlapping region of two images, our method effectively suppresses visual ghosting. However, if the movement extends beyond the overlap, the network cannot recognize it the object, leading to duplicate appearances in the final stitched image. This limitation arises because the motion loss focuses solely on movements within the overlapping area and fails handle large displacements. Addressing fast, large-scale motions while ensuring non-redundant targets in the output remains challenging and may require integrating explicit object detection with a generative model reconstruct occluded backgrounds around duplicated targets.
7 Conclusion
We propose an adverse condition-tolerant image stitching network. It features a bidirectional consis- tency learning framework, which iteratively optimizes differentiable restoration and Gaussian-encodee homography estimation for reliable alignment. Additionally, motion constraint is proposed in the seamless composition stage, suppressing artifacts from moving objects. We also construct the first adverse scene image stitching dataset, covering diverse low-light, haze, and underwater scenarios. Extensive experiments on the proposed dataset demonstrate the stitching performance of our method under adverse conditions.
Acknowledgments
This work is partially supported by the China Postdoctoral Science Foundation (No. 2023M730741);; in part by the National Natural Science Foundation of China (Nos. 62302078, 62372080); and part by the Fundamental Research Funds for the Central Universities (No. 3132025276).
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A Technical Appendices and Supplementary Material
(a) CNN
(b) Hyperpareneter Predictor (HP)
(c) Allcation Layer (AL)
Festarce
Image
Fasturkk
(d) Homography Aggregator (IIA)
Rociee
Denblin
Paspetas
Figure 10: Detailed structure of CNN (a), Hyperparameter Predictor (b), Attention Layer (c) and Homography Aggregator (HA).
Iref
M re
M
hrr
M Figure 11: Visualization of the seam mask.
M
A.1 Detailed Network Architecture
Fig. 10 demonstrates the detailed network structure of the proposed Pyramid-CNN (a), Hyperparame- ter Predictor (b), Attention Layer (c) and Homography Aggregator (d).
A.2 Details in MSC
The generation process the introduced seam mask {M:s M can be formulated as follows:
V = MMeffss Moefs-11j + M M - M Miefsajals
(12)
VMC. far | M Mioni-151
Mianis M M
Mc. C(E(E((((((VMM)))))) Mfes
(13)
M C(E(E(E(E(V @ Mar
where i.j are index the Cartesian coordinates. [M Mfr are the content mask, which replace the original pixel of image into all-in-one matrix. & the convolution layer with 3x3 C clip image to 0-1. The visualization of the {M Mi.. M M is also shown in Fig. I]
SOBEL filters.
A.3 More Demonstration ASIS Dataset
additionally provide examples of the dataset in different scenarios under adverse environments, which is demonsterated in Fig. 2 It is worth mentioning that the low-light scene image pairs
22

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Overlap
100
80
G0
40
20
Distribution
Low- Light
Underwater
Haze
Figure 12: Examples of image pairs under three adverse environment in order of baseline from small to large.
Input 1
APAP
VFIS
LPC
Input 2
UDIS
UDIS2
Ours
Figure 13: Visualization results on the low-light environment.
collected manually, and the image pairs of the underwater and haze environment are collected from the Internet
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Input 1
APAP
VFIS
LPC
Input 2
UDIS
UDIS2
Ours
Figure 14: Visualization results on the haze environment.
Input 1
APAP
VFIS
LPC
Input 2
UDIS
UDIS2
Ours
Figure 15: Visualization results on the underwater environment.
A.4 More Visual Comparisons
additionally provide examples for evaluating the performance in low-light,haze and underwater environment. We use the unified restortation framework HBALALS [52] combined with APAP [14), VFIS 8. LPC (56), UDIS 99] and UDIS2 [00 to compare with our method. The qualitative results
are shown in Fig. Fig. 14 and Fig. 1
24


Reviews:
[{"review_id": "maYCCWfnZ6", "reviewer": "Reviewer_gzJT", "summary": "A method for image stitching is proposed which is designed to handle adverse lightening and haze conditions. The proposed method is in two steps : first, two images alignment using consistency between the two images, and second, two images composition able to handle moving objects. A new dataset with adverse lightening and haze conditions for image stitching is introduced with 2,250 pairs of images. Comparative experiments are proposed  with 5 other methods on the proposed dataset. An ablation study for each step is also proposed.", "questions": "May you better explain the way the \"Recursive Parameterized Homography Estimation\" module is working ?\nMay you better explain the way the \"Bidirectional-Consistency Learning Framework\" is working ?\nHow can be performed the stitching of series of images ? \nWhat about the property of the youtube video used for building the proposed dataset ?", "limitations": "Yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 2, "significance": 3, "originality": 2, "strengths_and_weaknesses": "If the proposed structure seems standard, several contributions are proposed in the steps. The methods is not always clearly described and explained. For instance, in section 3.2, the equation (5) is not related to Beer-Lamber law. It is difficult to understand the processing described in section 3.1 and 3.2. For instance, in section 3.2, how is removed the mu_GT which is not known ?  The obtained results are visually not always convincing, like in Fig 14 and Fig 15, compared to other methods. It is only the two images stitching which is discussed and not the stitching of series of images.", "was_revised": true, "final_justification": "I found authors responses interesting and from other reviewers point of view, I am ready to raise my evaluation.", "rebuttal": "Thank you for your valuable comments. I will provide a detailed explanation based on your suggestions and concerns, and revise the manuscript accordingly. \n\n---\n**W1**: “In sec 3.2, Eq. 5 is not related to Beer-Lamber law.”\n\n**Ans**: \nThank you for your valuable comment. We agree that Eq. 5:\n$$t(x, \\omega)=1-\\omega \\min _C\\left(\\min _{y \\in \\Omega(x)} \\frac{I^C(y)}{A^C}\\right),$$ \nis not a direct analytical derivation from the Beer-Lambert law. Rather, it is an approximate estimation of the transmission map t(x).\n\nSpecifically, the haze imaging model used in our work:\n$I(x) = J(x) \\cdot t(x) + A \\cdot (1 - t(x))$\nassumes that the transmission t(x) follows the exponential decay with respect to scene depth d(x), which directly reflects the Beer-Lambert principle. However, since depth d(x) is unknown in single image scenarios, the dark channel prior[1] is adopted to approximate the transmission via local statistics.\nTherefore, Eq. 5 is not a direct derivation from Beer-Lambert, but a practical formulation motivated by its physical behavior, designed for estimating transmission maps in the absence of ground-truth depth. We will revise the text in the manuscript to clarify this distinction.\n\n[1] Single image haze removal using dark channel prior.\n\n---\n**W2**:  in section 3.2, how is removed the  $\\mu_{\\mathrm{GT}}$ which is not known? ”\n\n**Ans**:  \nThank you for your comment. $\\mu_{\\mathrm{GT}}$ is not appear in Sec 3.2, but appears in Eq.3 in Sec 3.1. If you would like to know how it is eliminated during inference, please refer to **Ans for Q1**. If you would like to know why $\\mu_{\\mathrm{GT}}$ does not appear in Sec 3.2, I can explain it from the perspective of the dataset. \n\nOur training dataset is synthesized based on adverse-scene datasets that include clean reference images. Specifically, we first sample the same spatial position from both the clean and degraded images to obtain the reference view image. Then, we apply random offsets (including synchronized endpoint translation and independent endpoint perturbations) to generate both the degraded and clean target view images. The random offset parameters used in this process correspond to $\\mu_{\\mathrm{GT}}$ as mentioned in the manuscript, which allows for supervised training for homography estimation during optimization.\n\nAs for the restoration task described in Sec 3.2, the loss function (Eq. 6) does not rely on $\\mu_{\\mathrm{GT}}$, since our synthesized dataset already provides clean images at both the reference and target positions, making deformation through $\\mu_{\\mathrm{GT}}$ unnecessary.\n\n---\n**W3**: “The obtained results are not always convincing, like in Fig 14-15. It is only the two images stitching which is discussed and not the stitching of series of images.”\n\n**Ans**: \nThank you for your question. To date, representative image stitching algorithms(including all comparison methods in the manuscript) are all designed to compute correspondences between only two images. Therefore, when stitching more than two images, one must first input the first two images into the algorithm to obtain an initial stitched result, and then iteratively feed the resulting image and the next image into the algorithm to generate the updated stitched image. As a result, current stitching methods are incapable of learning or modeling the correspondence among multiple images simultaneously. Consequently, evaluating multi-image stitching essentially holds the same meaning as evaluating pairwise stitching performance, since each step still only involves two-image correspondence. Moreover, previous benchmark comparisons for representative stitching methods have only evaluated two-image stitching performance. Based on this, we did not conduct additional comparisons on multi-image stitching.\n\n---\n**Q1**: “Explain the way the \"RPHE\" module is working.”\n\n**Ans**: The iterative process in our homography estimation network can be summarized in three main steps to better clarify the mechanism:\n\n1. In each iteration, disparity candidates are sampled using the current mean $\\mu$ and standard deviation $\\sigma$. Specifically, at the $i$-th iteration, candidates are uniformly sampled within the interval $[ \\mu_i - \\sigma_i, \\mu_i + \\sigma_i ]$. For the initial iteration, $\\mu_0$ is initialized to 0 and $\\sigma_0$ is set to 32 (as mentioned in the experimental details of the manuscript).\n\n2. The matching cost is computed based on the sampled disparities of the current iteration, which is then used to predict the update step $\\Delta_i$.\n\n3. According to Eq. 3 in the manuscript, the predicted step $\\Delta_i$ is used to update the current $\\mu$ and $\\sigma$ according to follows formulation:\n\n$$\\qquad\\qquad\\sigma_i^{t}  =\\sigma_i^{t-1}-\\frac{1}{2}\\left(\\frac{(\\sigma_i^{t-1})^2-\\sigma_G^2-(\\Delta^{t-1})^2}{ (\\sigma_i^{t-1})^3}-\\frac{1}{\\sigma_i^{t-1}}+\\frac{\\sigma_i^{t-1}}{\\sigma_G^2}\\right), \n\t\t\\mu_i^{t}  =\\mu_i^{t-1}-\\left(-\\frac{\\Delta^{t-1}}{2}\\left(\\frac{1}{(\\sigma_i^{t-1})^2}+\\frac{1}{\\sigma_G^2}\\right)\\right).$$\n &nbsp; &nbsp; &nbsp; It is worth noting that since the ground-truth mean \\$\\mu\\_{G}\\$ is unavailable during inference, we approximate \\$\\Delta^{t-1}\\$ as \\$\\qquad\\mu\\_{G} - \\mu\\_i^{t-1}\\$. $\\sigma_G$ is a predefined parameter.\n\nAfter $N$ iterations, the final updated $\\mu_N$ is taken as the predicted displacement. Meanwhile, $\\sigma$ progressively decreases throughout the iterations, making the sampling more concentrated and thus improving the accuracy and stability of the final prediction.\n\nThis approach ensures differentiability during training and uses JS divergence to guide the distribution toward a standard Gaussian, accelerating convergence and improving generalization. It effectively combines randomness with optimization to overcome traditional limitations.\n\n---\n**Q2**: “Better explain the way the \"BCLF\" is working”\n\n**Ans**:\nThe Bidirectional-Consistency Learning Framework is proposed to address the issue of inconsistent restoration caused by enhancement networks. In image alignment tasks under adverse conditions, we aim for the backbone network to produce restoration results that are equivariant under spatial transformations. This objective can be formally expressed as:\n\n$$\n\\Phi\\left(\\mathcal{W}\\left(I_{\\text{tar}} ; \\mathbf{H}\\right)\\right) = \\mathcal{W}\\left(\\Phi\\left(I_{\\text{tar}}\\right); \\mathbf{H}\\right),\n$$\n\nwhere $\\mathcal{W}$ denotes the warping operation based on homography $\\mathbf{H}$, $I_{\\text{tar}}$ is the target image, and $\\Phi$ is the restoration module.\n\nIn this ideal case, the restored features from corresponding positions would perfectly match. However, in practice, it is difficult for restoration networks to maintain equivariance under homography transformations, which involve translation, rotation, scaling, shearing, aspect ratio change, and perspective distortion. This leads to inconsistent features between corresponding points after enhancement \\[1], ultimately degrading the performance of homography estimation—an observation consistent with the results shown in Fig. 1(a) of the manuscript.\n\nTo address this issue, we depart from the conventional sequential paradigm of restoration followed by warping. Instead, we propose an alternating scheme within our recursive homography estimation framework, referred to as *restoration-guided warping*. The image warping module and restoration module are interleaved during the iterative process, which can be formulated as:\n\n$$\nE_{\\text{tar}}^n = \\Phi\\left(\\mathcal{W}\\left(I_{\\text{tar}} ; \\hat{\\mathbf{H}}^n\\right)\\right), \\quad\n\\hat{\\mathbf{H}}^{n+1} = \\Psi\\left(I_{\\text{ref}} ; E_{\\text{tar}}^n\\right),\n$$\n\nwhere $\\Psi$ denotes the homography estimation module, and $E_{\\text{tar}}^n$ and $\\mathbf{H}^n$ represent the enhanced warped image and the estimated homography at the $n$-th iteration, respectively.\n\nAs the iteration proceeds, the warped image $I_{\\text{tar}}^w$ becomes increasingly aligned with the reference image $I_{\\text{ref}}$, allowing the DIR module to produce more consistent restoration in the overlapping regions, which in turn improves the accuracy of homography estimation. \n\n[1] Gift: Learning transformation-invariant dense visual descriptors via group CNNs. \n\n---\n**Q3**: “How can be performed the stitching of series of images?”\n\n**Ans**: \nMost stitching methods only take two images as input and estimate transformations for stitching by computing the correspondence between these two images. This process can be described as $I^s = \\mathcal{S}(I_{1}, I_{2})$, where $I_{1}, I_{2}$ denote the two input images, $I^s$ and $\\mathcal{S}$ represent the stitched result and stitching network.\n\nTo stitch more than two images $[I_{1}, I_{2}, \\ldots, I_{t}]$, the network can only iteratively combine each new image $I_t$ with the previous stitched result $I^s_{t-1}$. The entire procedure can be expressed as \n$I^s_t = \\mathcal{S}(I^s_{t-1}, I_{t})$, where  $t \\geq 2$.\n\n---\n**Q4**: “The property of the YouTube video used for building the dataset ?”\n\n**Ans**: \nThe proposed dataset includes three types of scenes, among which the foggy and underwater scene images were sourced from online videos.\n\nThe fog data comes from 3 YouTube videos, which have been posted in supplementary details.\nAll three videos have a frame rate of 30 fps and a resolution of 1280×720. The total number of frames across them is 192,266.\nThe underwater scene data is sourced from the 2 YouTube videos. Both underwater videos also have a frame rate of 30 fps and a resolution of 1280×720. The combined total number of frames in both videos is 358,228.\n\nSince these videos were not originally recorded for image stitching, most consecutive frames contain excessive parallax or severe occlusions, making them unsuitable for stitching tasks. Therefore, we carefully selected 750 image pairs from each of the fog and underwater scenes. These pairs were chosen to ensure valid displacement while maximizing the diversity of scene deformations."}, {"review_id": "nCAeSxk7Hy", "reviewer": "Reviewer_PgFv", "summary": "This paper proposes a method that effectively handles various adverse factors in image stitching. It presents a novel approach for computing the correlation volume and successfully integrates an image enhancement module with the homography estimation module. As a result, the method demonstrates superior stitching performance. Additionally, the paper introduces a new dataset that reflects these diverse and challenging conditions.", "questions": "**Q1.** I have a question regarding the process of generating homographies for training. To learn the Gaussian distribution, it seems both the mean (μ) and standard deviation (σ) must be specified. Could you clarify how these are determined? Specifically, is the mean (μ) sampled from a uniform distribution, and is the final displacement value then sampled from the resulting normal distribution, N(μ, σ)?\n\n**Q2.** Regarding the DIR module, would using a generic CNN be less effective than the current design with its specialized, hand-crafted blocks? Was this alternative considered?\n\n**Q3.** Could you clarify why the mACE metric is not reported in Table 1?\n\n**Q4.** How does the total training time of the proposed method compare to that of other existing methods?", "limitations": "**L1.** The method requires input images to be of a fixed size.\n\n**L2.** Other limitations are as detailed in the main 'Weaknesses' section.", "rating": 5, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 2, "significance": 3, "originality": 3, "strengths_and_weaknesses": "**S1.** The effective integration of image enhancement into the homography estimation process enables more accurate results compared to using enhancement merely as a preprocessing step.\n\n**S2.** It proposes a method for utilizing a correlation volume to handle image pairs with large baselines.\n\n**S3.** Factors affecting image quality are incorporated into the DIR module by leveraging inductive biases.\n\n**S4.** The paper contributes a novel benchmark dataset.\n\n**W1.** The DIR module may struggle to address types of image degradation that are not explicitly covered by its pre-defined blocks.\n\n**W2.** The inclusion of a sampling process reduces the method's efficiency.\n\n**W3.** The DIR, BCDA, and MSC modules are trained separately, rather than through a single, end-to-end process.\n\n**W4.** Comparative experiments against other methods on the newly proposed dataset are not included in the paper.", "was_revised": true, "final_justification": "I believe the following strengths of this paper will have a impact on future research:\n\n- It addresses the overconfidence problem by utilizing stochastic predictions rather than conventional deterministic ones.\n\n- It introduces a scalable DIR module, moving beyond naive CNN architectures and integrating it into the stitching learning process.\n\n- It presents a novel dataset for the community.\n\nTherefore, I would like to raise my rating.", "rebuttal": "Thank you for your valuable comments. I will provide a detailed explanation based on your suggestions and concerns, and revise the manuscript accordingly. Below are my responses to your questions:\n\n----\n**Ans for W1**: \nThank you for your valuable feedback. Explicitly designing submodules for specific degradation types does indeed limit adaptability when dealing with unknown or compound degradations.\n\nHowever, we have paid particular attention to modularity and scalability in the design of the DIR module. Each degradation-specific sub-module is implemented in a plug-and-play manner, allowing flexible extension or replacement to accommodate new degradation types without retraining the entire model. Moreover, all DIP modules are connected to a shared encoder that captures global contextual information. Even when a specific degradation type is not explicitly modeled, the shared features can still compensate for potential performance loss through semantic interaction.\n\n---\n**Ans for W2**: \nThank you for your suggestion. The core purpose of adopting uniform sampling is to construct a differentiable and robust optimization module that can model the uncertainty of the current offset distribution, thereby enhancing the model’s robustness to local minima and ambiguous regions.\n\nTo further evaluate the efficiency, we conducted a runtime comparison under the same experimental settings.\nThe table below lists the processing time per image pair for all methods across low-light, underwater, and foggy scenes.\nIt can be observed that our proposed method still performs competitively under sampling conditions, with runtime only slightly higher than VFIS and its enhanced variant (Restoration + VFIS). In future work, we will explore further simplifications of the module architecture and investigate low-iteration optimization strategies.\n\n||||LLI|||||HBLALS|||||NeRCo||||\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|| APAP|VFIS|&nbsp; LPC|UDIS|UDIS2|&nbsp; APAP|&nbsp; VFIS|&nbsp; LPC|UDIS|UDIS2|&nbsp; APAP|&nbsp; VFIS|&nbsp; LPC|UDIS|UDIS2|Ours|\n|time(s)|2.282|**0.283** | &nbsp; 1.550 | 0.692|0.351|&nbsp;  2.313|&nbsp;  *0.302*|&nbsp; 1.577|0.710|0.380|&nbsp;  2.291|&nbsp;  0.299|&nbsp; 1.562|0.704|0.360|0.336|\n---\n\n||||UI|||||HBLALS|||||WFlow||||\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|| APAP|VFIS|&nbsp; LPC|UDIS|UDIS2|&nbsp; APAP|&nbsp; VFIS|&nbsp; LPC|UDIS|UDIS2|&nbsp; APAP|&nbsp; VFIS|&nbsp; LPC|UDIS|UDIS2|Ours|\n|time(s)|2.262|**0.304**|&nbsp;  1.602|0.699|0.350|&nbsp;  2.274|&nbsp;  *0.313*|&nbsp;  1.623|0.710|0.366|&nbsp;  2.382|&nbsp;  0.400|&nbsp;  1.695|0.804|0.362|0.322| \n---\n\n||||SI|||||HBLALS|||||WGWS||||\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|| APAP|VFIS|&nbsp; LPC|UDIS|UDIS2|&nbsp; APAP|&nbsp; VFIS|&nbsp; LPC|UDIS|UDIS2|&nbsp; APAP|&nbsp; VFIS|&nbsp; LPC|UDIS|UDIS2|Ours|\n|time(s)|2.174|**0.262**|&nbsp; 1.499|0.660|0.295|&nbsp; 2.180|&nbsp; *0.273*|&nbsp; 0.520|0.679|0.348|&nbsp; 2.262|&nbsp; 0.331|&nbsp; 1.573|0.759|0.390|0.334|\n---\n\n**Ans for W3** :\nThank you for your question. First, BCDA and MSC are treated as two independent stages in the image stitching pipeline and are trained separately. The BCDA module focuses on estimating transformation parameters, typically supervised by geometric errors or positional discrepancies; in contrast, the composition stage emphasizes image blending quality and relies on pixel-level or perceptual losses. Due to the significant differences in their numerical scales, gradient directions, and convergence behaviors, end-to-end training cannot effectively optimize both tasks simultaneously. Therefore, similar to other learning-based stitching methods \\[1–2], we adopt a staged training strategy.\nFor training BCDA and DIR, directly training BCDA disrupts DIR’s pixel-level supervision, reducing its enhancement. To avoid this, we pre-trained DIR for 200 epochs before joint training.\n\n[1] Unsupervised deep image stitching: Reconstructing stitched features to images. TIP, 2021.\n[2] Parallax-tolerant unsu\u0002pervised deep image stitching. ICCV, 2023.\n\n---\n**Ans for W4**:: \nThank you for your suggestion. Tab. 1 and Fig. 5 in the manuscript present the quantitative and qualitative comparison of the newly proposed dataset, covering image stitching performance under three challenging conditions.\n\nBesides directly stitching degraded images for baseline comparison, we also restore them using task-specific methods before stitching to ensure fairer evaluation. The results highlight that our method is not just a simple combination, but a tailored solution for stitching under degradation.\n\n---\n**Ans for Q1**:: \nThank you for your question. The iterative process in our homography estimation network can be summarized in three main steps to better clarify the mechanism:\n\n1. In each iteration, disparity candidates are sampled using the current mean $\\mu$ and standard deviation $\\sigma$. Specifically, at the $i$-th iteration, candidates are uniformly sampled within the interval $[ \\mu_i - \\sigma_i, \\mu_i + \\sigma_i ]$. For the initial iteration, $\\mu_0$ is initialized to 0 and $\\sigma_0$ is set to 32 (as mentioned in the experimental details of the manuscript).\n\n2. The matching cost is computed based on the sampled disparities of the current iteration, which is then used to predict the update step $\\Delta_i$.\n\n3. According to Equation (3) in the manuscript, the predicted step $\\Delta_i$ is used to update the current $\\mu$ and $\\sigma$.\n\nAfter $N$ iterations, the final updated $\\mu_N$ is taken as the predicted displacement. A direct linear transformation is then applied to obtain the final homography matrix. Meanwhile, $\\sigma$ progressively decreases throughout the iterations, making the sampling more concentrated and thus improving the accuracy and stability of the final prediction.\n\nThe advantage of this approach lies in its ability to maintain differentiability throughout the sampling and matching process during training. Moreover, by introducing JS divergence, the distribution gradually converges toward a standard Gaussian, which further accelerates model convergence and enhances generalization. This combination of random modeling and differentiable optimization effectively addresses the limitations of traditional methods.\n\n---\n**Ans for Q2**:: \nThank you for your question. We have indeed considered the issue you raised. In the early stages of our work, we initially planned to design a conventional CNN-based restoration module. However, we ultimately chose to implement a differentiable restoration (DIR) module for the following three reasons:\n\nFirst, our goal is stitching-oriented image restoration, which enhances visual quality while preserving structural consistency between image pairs. Although traditional data-driven CNN methods often perform well under visual metrics, they may introduce artifacts that impair correspondence, disrupting consistency in overlapping regions. DIR combines physics-guided enhancement with a bi-directional consistency framework, facilitating structural preservation and equivariant feature extraction.\n\nSecond, under our proposed bi-directional consistency framework, the restoration module is repeatedly invoked at each iteration. To ensure efficiency, we aimed to minimize its computational overhead. The proposed DIR module is highly lightweight, with a total cost of only 0.07 Gflops. The learnable part consists of just five convolutional layers and one feedforward layer, significantly reducing the burden on the overall network.\n\nLastly, as mentioned earlier, we placed strong emphasis on modularity and scalability when designing the DIR module. Each degradation-specific sub-module is implemented in a plug-and-play fashion, making it easy to extend or replace to accommodate new degradation types in the future.\n\n---\n**Ans for Q3**: Thank you for your comment. The mACE metric evaluates the error between the estimated and ground truth corner displacements in homography estimation tasks. Therefore, it is only applicable to stitching methods based on homography transformations.\n\nHowever, many stitching methods—especially traditional, non-deep-learning approaches—do not rely on projective transformations as their warping models. This includes the comparison methods used in our manuscript, such as LPC, APAP, and UDIS2, all of which cannot be evaluated using the mACE metric.\n\nFor this reason, we did not use mACE in our comparative experiments. Instead, we relied on PSNR and SSIM, which are applicable to all stitching methods, as the evaluation metrics to compare the performance of the proposed approach.\n\n---\n**Ans for Q4**:  Thank you for your valuable question. The Table presents the average training time in each epoch of our proposed method compared with other learning-based image stitching networks included in the manuscript. For a fair comparison, all methods were trained on the same device (NVIDIA RTX 4090 GPU) with a unified batch size of 8.\nSince our framework additionally includes a restoration subtask, the overall training time is longer than that of existing stitching networks. Specifically, our main network was trained for 160 epochs, taking approximately 66 hours in total, which is longer than the compared methods. In future work, we plan to improve training efficiency while maintaining performance.\n\n|Method|VFIS|UDIS|UDIS2|Ours|     \n|-|-|-|-|-| \n| time (h)| 0.377 | 0.396  | 0.343  | 0.416  | \n\n---\n**Ans for L1**: \nThank you for your insightful comment. The proposed network requires deformation parameters of a fixed size as output. To this end, the input images are resized to 128×128 during training. However, the predicted deformation parameters can be upsampled to match the resolution of the original image, enabling accurate warping at the full scale. This strategy is consistent with common practices in representative learning-based image stitching and optical flow estimation methods. Therefore, our approach is capable of handling image stitching tasks across varying input resolutions."}, {"review_id": "MUPDxhkNFB", "reviewer": "Reviewer_kt8k", "summary": "The article proposes a robust deep image stitching network called ACDIS, which addresses the impact of adverse conditions such as complex weather, lighting changes, and dynamic scene motions in real - world scenarios on image stitching. It introduces a bidirectional consistency learning framework that combines differentiable image restoration with Gaussian - distributed homography estimation, achieving reliable alignment.", "questions": "Please see the weaknesses.", "limitations": "yes", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 3, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strengths：\n1. The authors constructed the first image stitching dataset named ASIS, which includes various adverse scenarios such as low - light conditions, foggy weather, and underwater environments. They conducted extensive experiments to verify the effectiveness of the proposed method.\n2. The proposed ASIS dataset provides a valuable benchmark for the evaluation of image stitching tasks under adverse conditions\n\nWeaknesses：\n1. Although the article introduces motion constraints in the seamless stitching network, the method may only be able to handle dynamic scene changes within a certain range. For more complex dynamic scenes, such as those with multiple moving objects or objects moving at high speeds, the article does not elaborate on how to more effectively address the image stitching problem under these complex dynamic scenarios. Further exploration and improvement of the motion constraint mechanism could be made to adapt to a wider range of dynamic scenes.\n2. The lack of interpretability is a notable drawback. Although the paper provides detailed technical descriptions, it falls short in offering in-depth explanations and analyses of the principles and processes of the core methods, such as the bidirectional-consistency learning framework and the recursive parameterized homography estimation module. For instance, the paper does not provide sufficient intuitive explanations regarding why the bidirectional optimization approach can enhance alignment reliability more effectively, as well as the underlying mechanisms of how the recursive parameterized homography estimation module gradually approximates the true homography transformation during the iterative process.\n3. The proposed ACDIS network, which includes multiple modules and iterative optimization processes, may suffer from relatively low computational efficiency. This could be a significant limitation in practical applications, especially in scenarios where real-time performance is required. The authors should consider optimizing the network structure or algorithmic flow to improve its speed and efficiency.", "was_revised": true, "final_justification": "The author solved my problem and I raised the score to 4.", "rebuttal": "Thank you for your valuable comments. In response to your suggestions and concerns, I will provide detailed explanations and revise the manuscript accordingly. Please find my responses below.\n\n---\n**Ans for W1**: Thank you for your feedback. The proposed motion constraint currently improves stitching performance in dynamic scenes to a certain extent. Taking pedestrians in road scenarios as an example: when a person moves during the acquisition of the image pair, our method can effectively suppress visual ghosting in the stitched result—but only if the motion remains within the overlapping region of the two images. If the pedestrian's movement exceeds this overlapping area, the network cannot recognize them as the same target, resulting in duplicated appearances in the final stitched image. This is because the core of our motion loss lies in capturing moving content within the overlap, and it cannot handle motion beyond that scope.\n\nAddressing high-speed motion with large displacements (beyond the overlapping area), while ensuring that only non-redundant targets remain in the final output, remains highly challenging. This may require integrating explicit object detection modules with generative models to inpaint the occluded background around repeated targets using contextual information. We will include a discussion of this limitation in the Limitations section of the manuscript.\n\n---\n**W2(1)**: “Why the bidirectional optimization approach can enhance alignment reliability more effectively”\n\n**Ans**: \nThe proposed optimization framework is proposed to address the issue of inconsistent restoration. In image alignment tasks under adverse conditions, we aim for the backbone network to produce restoration results that are equivariant under spatial transformations. This objective can be formally expressed as:\n\n$$\\Phi\\left(\\mathcal{W}\\left(I_{\\text{tar}} ; \\mathbf{H}\\right)\\right) = \\mathcal{W}\\left(\\Phi\\left(I_{\\text{tar}}\\right); \\mathbf{H}\\right),$$\n\nwhere $\\mathcal{W}$ denotes the warping operation based on homography $\\mathbf{H}$, $I_{\\text{tar}}$ is the target image, and $\\Phi$ is the restoration module.\n\nIn this ideal case, the restored features from corresponding positions would perfectly match. However, in practice, it is difficult for restoration networks to maintain equivariance under homography transformations, which involve translation, rotation, scaling, shearing, aspect ratio change, and perspective distortion. This leads to inconsistent features between corresponding points after enhancement \\[1], ultimately degrading the performance of homography estimation—an observation consistent with the results shown in Fig. 1(a) of the manuscript.\n\nTo address this issue, we depart from the conventional sequential paradigm of restoration followed by warping. Instead, we propose an alternating scheme within our recursive homography estimation framework, referred to as *restoration-guided warping*. As illustrated in Fig. 1(b), the image warping module and restoration module are interleaved during the iterative process, which can be formulated as:\n\n$$E_{\\text{tar}}^n = \\Phi\\left(\\mathcal{W}\\left(I_{\\text{tar}} ; \\hat{\\mathbf{H}}^n\\right)\\right), \\quad\n\\hat{\\mathbf{H}}^{n+1} = \\Psi\\left(I_{\\text{ref}} ; E_{\\text{tar}}^n\\right),$$\n\nwhere $\\Psi$ denotes the homography estimation module, and $E_{\\text{tar}}^n$ and $\\mathbf{H}^n$ represent the enhanced warped image and the estimated homography at the $n$-th iteration, respectively.\n\nAs the iteration proceeds, the warped image $I_{\\text{tar}}^w$ becomes increasingly aligned with the reference image $I_{\\text{ref}}$, allowing the DIR module to produce more consistent restoration in the overlapping regions, which in turn improves the accuracy of homography estimation. The effectiveness of this mechanism is demonstrated in Fig. 1(b) of the main text. It is also important to note that although the framework performs multiple iterations, the restoration network (DIR) shares weights across iterations, and thus does not incur any additional parameter overhead.\n\n[1] Gift: Learning transformation-invariant dense visual descriptors via group CNNs. \n\n---\n**W2(2)**: “How the homography estimation module gradually approximates the true homography transformation during the iterative process.”\n\n**Ans**: The iterative process in our homography estimation network can be summarized in three main steps to better clarify the mechanism:\n\n1. In each iteration, disparity candidates are sampled using the current mean $\\mu$ and standard deviation $\\sigma$. Specifically, at the $i$-th iteration, candidates are uniformly sampled within the interval $[ \\mu_i - \\sigma_i, \\mu_i + \\sigma_i ]$. For the initial iteration, $\\mu_0$ is initialized to 0 and $\\sigma_0$ is set to 32 (as mentioned in the experimental details of the manuscript).\n\n2. The matching cost is computed based on the sampled disparities of the current iteration, which is then used to predict the update step $\\Delta_i$.\n\n3. According to Equation (3) in the manuscript, the predicted step $\\Delta_i$ is used to update the current $\\mu$ and $\\sigma$ according to follows formulation:\n\n$$\\qquad\\qquad\\sigma_i^{t}  =\\sigma_i^{t-1}-\\frac{1}{2}\\left(\\frac{(\\sigma_i^{t-1})^2-\\sigma_G^2-(\\Delta^{t-1})^2}{ (\\sigma_i^{t-1})^3}-\\frac{1}{\\sigma_i^{t-1}}+\\frac{\\sigma_i^{t-1}}{\\sigma_G^2}\\right), \n\t\t\\mu_i^{t}  =\\mu_i^{t-1}-\\left(-\\frac{\\Delta^{t-1}}{2}\\left(\\frac{1}{(\\sigma_i^{t-1})^2}+\\frac{1}{\\sigma_G^2}\\right)\\right).$$\n &nbsp; &nbsp; &nbsp; where $\\sigma_G$ is a predefined parameter.\n\nAfter $N$ iterations, the final updated $\\mu_N$ is taken as the predicted displacement. A direct linear transformation is then applied to obtain the final homography matrix. Meanwhile, $\\sigma$ progressively decreases throughout the iterations, making the sampling more concentrated and thus improving the accuracy and stability of the final prediction.\n\nThe advantage of this approach lies in its ability to maintain differentiability throughout the sampling and matching process during training. Moreover, by introducing JS divergence, the distribution gradually converges toward a standard Gaussian, which further accelerates model convergence and enhances generalization. The optimization process can be formulated as:\n$$\\mathcal{J}_i=\\frac{1}{2}\\left(F\\left(\\mathcal{N}_G \\| \\mathcal{N}_i\\right)+F\\left( \\mathcal{N}_i \\| \\mathcal{N}_G\\right)\\right),$$\n\nwhere $\\mathcal{N}_i$ is the short form of $\\mathcal{N}\\left(\\mu_i, \\sigma_i^2\\right)$. $\\mathcal{N}_G=\\mathcal{N}\\left(\\mu_G, \\sigma^2_G\\right)$ denotes predefined Gaussian distribution, where $\\mu_G$ represents the ground truth offset. $F\\left( \\cdot  \\|\\cdot\\right)$ represents KL divergence. This combination of random modeling and differentiable optimization effectively addresses the limitations of traditional methods.\n\n---\n**Ans for W3**: Thank you for your suggestion. Multi-iteration optimization is a widely adopted strategy in current deformation estimation networks. Since image stitching tasks often involve handling large-baseline scenarios, a coarse-to-fine iterative deformation process is crucial for achieving satisfactory stitching performance[1-2]. Although single-iteration strategies can indeed improve runtime, they often lead to a significant drop in final stitching quality.\n\nTo ensure practical applicability, our method is designed to strike a balance between stitching accuracy and computational efficiency, aiming to meet real-time requirements. During the design phase, we introduced the following optimizations:\n\n*1. Convergence Acceleration via Gaussian-Encoded Optimization:*\nWe ensure the differentiability of the sampling and matching process during training. By introducing a JS divergence constraint, the distribution gradually converges to a standard Gaussian, which accelerates model convergence and improves generalization. This combination of probabilistic modeling and differentiable optimization effectively addresses the limitations of traditional methods.\n\n*2. Lightweight Image Restoration Module:*\nThe module is designed to be lightweight, consisting of only 5 convolutional layers. It is solely responsible for predicting the hyperparameters used in restoration. With a computational cost of just 0.07 Gflops.\n\nTo further validate the efficiency, we conducted runtime comparison experiments under identical settings.\nTables below show the processing time per image pair for all methods. The average runtime per pair for our method is 0.336s (low-light), 0.322s (underwater), and 0.344s (foggy). Across all three scenes, the proposed method performs competitively, with runtime only slightly higher than VFIS and its enhanced variant (restoration + VFIS). In future work, we will explore further simplifications of the module architecture and investigate low-iteration optimization strategies.\n\n||||LLI|||||HBLALS|||||NeRCo||||\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|| APAP|VFIS|LPC|UDIS|UDIS2| APAP|VFIS|&nbsp; LPC|UDIS|UDIS2|APAP|&nbsp; VFIS|&nbsp; LPC|UDIS|UDIS2|Ours|\n|time(s)|2.282|**0.283** | 1.550 | 0.692|0.351|2.313|*0.302*|&nbsp; 1.577|0.710|0.380|2.291|&nbsp; 0.299|&nbsp; 1.562|0.704|0.360|0.336|\n---\n\n||||UI|||||HBLALS|||||WFlow||||\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|| APAP|VFIS|LPC|UDIS|UDIS2|APAP|VFIS|&nbsp; LPC|UDIS|UDIS2|APAP|&nbsp; VFIS|&nbsp; LPC|UDIS|UDIS2|Ours|\n|time(s)|2.262|**0.304**|1.602|0.699|0.350|2.274|*0.313*|&nbsp;  1.623|0.710|0.366|2.382|&nbsp; 0.400|&nbsp;  1.695|0.804|0.362|0.322| \n---\n\n||||SI|||||HBLALS|||||WGWS||||\n|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|-|\n|| APAP|VFIS|LPC|UDIS|UDIS2|APAP|VFIS|&nbsp; LPC|UDIS|UDIS2|APAP|&nbsp; VFIS|&nbsp; LPC|UDIS|UDIS2|Ours|\n|time(s)|2.174|**0.262**|1.499|0.660|0.295|2.180|*0.273*|&nbsp; 0.520|0.679|0.348|2.262|&nbsp; 0.331|&nbsp; 1.573|0.759|0.390|0.334|\n---\n\n\n[1] Learning edge-preserved image stitching from multi-scale deep homography[J]. Neurocomputing, 2022.\n\n[2] Unsupervised deep image stitching: Reconstructing stitched features to images[J]. IEEE TIP, 2021."}, {"review_id": "T9iaOiqybx", "reviewer": "Reviewer_o2QN", "summary": "The authors propose an image stitching method designed for adverse conditions by leveraging a Gaussian distribution to encode the displacement space.\nTo address image degradation caused by severe weather, they introduce a differentiable image restoration module, whose output is subsequently integrated into MSC.\nAlthough image stitching has been studied under various conditions, the authors construct a dedicated dataset for extreme scenarios with insufficient visual cues, providing adverse weather image pairs along with corresponding ground truth for reliable evaluation.", "questions": "1. The authors should include a more thorough comparison with existing panoramic datasets, such as the UDIS dataset.\n\n2. The authors should analyze the proposed model’s performance under normal (non-adverse) conditions.", "limitations": "There is a lack of discussion on limitations or future work. To ensure the proposed model is applicable in a wide range of scenarios, it should be evaluated not only under extreme conditions such as adverse weather but also under normal conditions. However, such analysis is missing, making it difficult to identify the model’s limitations in general settings. The authors should address this gap by including a discussion of relevant limitations, which could help pave the way for future improvements.", "rating": 4, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths\n1. The paper presents a reliable method for image stitching in degraded conditions. The authors effectively integrate the image restoration and warping modules in an efficient and cohesive manner.\n2. A new dataset including images from adverse weather situations and their ground truth.\n\nWeaknesses\n1. Insufficient comparison with existing panoramic datasets, such as the UDIS dataset.\n2. Lack of analysis of the proposed model’s performance under normal (non-adverse) conditions.", "was_revised": true, "final_justification": "By reading the rebuttal, the concerns raised from the paper have been resolved. The fact that the proposed method performs well not only under adverse conditions but also in general normal scenarios suggests that it is a well-designed approach. Therefore, I am keeping my score unchanged.", "rebuttal": "Thank you for your valuable feedback. I have carefully considered your suggestions and concerns, and will revise the manuscript accordingly. Please find my detailed responses to your comments below:\n\n---\n**Answer for Q1&Q2**:\nThank you for your suggestion. Our method can also serve as a general-purpose image stitching method under normal conditions. To demonstrate this, we conducted additional comparison experiments on the standard UDIS dataset. As shown in Table 1, the proposed method is quantitatively compared with other stitching methods on the UDIS dataset.\n\nIt is worth noting that since the UDIS dataset features normal lighting conditions without rain or fog, it differs from the adverse environments discussed in the main paper. Therefore, unlike the comparisons in the main text that involve combining task-specific restoration methods with stitching algorithms, here we directly compare pure stitching methods.\n\nTo ensure a fair comparison, and in contrast to the experimental protocol described in the manuscript (which uses synthetically generated pairs from adverse-environment datasets), we re-trained our method on the UDIS dataset using the Adam optimizer. The training was conducted for 100 epochs with a learning rate of 1e-4, and the retrained model was used for testing to validate the effectiveness of the proposed method under normal conditions.\n\nDue to NIPS rebuttal policy restrictions, we are unable to provide visual comparisons. However, as shown in the table, our method still achieves a slight advantage under normal conditions, with a 1% improvement in PSNR, 2% in SSIM, 2% in NIQE, and 12% in SIQE compared to the best-performing existing methods. This demonstrates the generalizability of our proposed approach.\n\nTable 1: Quantitative comparison for image stitching under normal environment (UDIS dataset).\n\n---\n| Metric | APAP   | VFIS   | LPC    | UDIS     | UDIS2  | Ours       |      | APAP   | VFIS   | LPC    | UDIS  | UDIS2    | Ours       |\n| ------ | ------ | ------ | ------ | -------- | ------ | ---------- | ---- | ------ | ------ | ------ | ------ | -------- | ---------- |\n| PSNR  ($\\uparrow$)   | 23.79 | 20.04 | 22.59 | 21.17  | 25.43 | **25.69** | NIQE($\\downarrow$) | 4.95  | 4.25  | 4.47  | 4.31    | 4.45  | **4.16**  |\n| SSIM ($\\uparrow$)    | 0.79  | 0.58  | 0.73  | 0.64 | 0.83  | **0.85**  | SIQE ($\\uparrow$) | 14.80 | 24.00 | 14.47  | 30.29| 20.44 | **33.93** |\n---\n\n---\n**Answer for Limitation**:\nThank you for your suggestion. Regarding the applicability of our model in normal conditions, please refer to our response to Q1 and Q2. The quantitative results in Table 1 demonstrate the effectiveness of the proposed architecture for image stitching under normal conditions."}]

Author rebuttal:

Meta-review: This paper tackles the important and practical challenge of image stitching under adverse conditions, such as illumination changes, motion blur, and dynamic content. The authors propose a Bidirectional-Consistency Learning (BCL) framework, which enforces stitching consistency in both directions, leading to more robust alignment. In addition, they introduce a benchmark dataset of difficult stitching scenarios, providing a valuable resource for systematic evaluation.

The submission is well motivated and clearly presented, and reviewers highlighted the significance of the problem. For example, the BCL formulation is conceptually simple yet effective, and extensive experiments show consistent improvements over both classical and recent learning-based baselines. The benchmark is also a meaningful contribution that can support future research in this area. During the discussion, the authors clarified details of the proposed consistency formulation, provided additional quantitative evidence, and explained the coverage of the benchmark. These clarifications addressed most questions and strengthened confidence in the submission.

Overall, the paper offers an impactful contribution by combining a robust stitching framework with a dedicated benchmark. The work is expected to have clear value for the community. The final recommendation by the Area Chairs is to accept the submission.
Decision: Accept (poster)