Depth-Supervised Fusion Network for Seamless-Free Image Stitching
Authors: Zhiying Jiang
Ruhao Yan
Zengxi Zhang
Bowei Zhang
Jinyuan Liu
Venue: NeurIPS 2025 poster ()
Keywords: Image Fusion
Primary area: applications

Abstract: Image stitching synthesizes images captured from multiple perspectives into a single image with a broader field of view. The significant variations in object depth often lead to large parallax, resulting in ghosting and misalignment in the stitched results. To address this, we propose a depth-consistency-constrained seamless-free image stitching method. First, to tackle the multi-view alignment difficulties caused by parallax, a multi-stage mechanism combined with global depth regularization constraints is developed to enhance the alignment accuracy of the same apparent target across different depth ranges. Second, during the multi-view image fusion process, an optimal stitching seam is determined through graph-based low-cost computation, and a soft-seam region is diffused to precisely locate transition areas, thereby effectively mitigating alignment errors induced by parallax and achieving natural and seamless stitching results. Furthermore, considering the computational overhead in the shift regression process, a reparameterization strategy is incorporated to optimize the structural design, significantly improving algorithm efficiency while maintaining optimal performance. Extensive experiments demonstrate the superior performance of the proposed method against the existing methods. Code is available at https://github.com/DLUT-YRH/DSFN.

Depth-Supervised Fusion Network for Seamless-Free Image Stitching
Zhiying Jiang'
Ruhao Yan²
Zengxi Zhang² Bowei Zhang² Jinyuan 2
College of Information Science and Technology, Dalian Maritime University 2 School of Software Technology, Dalian University of Technology atlantie918@ho com
zyj 1yjia063000mm..... com
Abstract
Image stitching synthesizes images captured from multiple perspectives into a single image with a broader field of view. The significant variations in object depth often lead to large parallax, resulting in ghosting and misalignment in the stitched results. To address this, propose depth-consistency- connsstrncyy seamless feeee image stitching method. First, to tackle the multi-view alignment difficulties caused by parallax, a multi-stage mechanism combined with global depth regularization constraints is developed to enhance the alignment accuracy of the same apparent target across different depth ranges. Second, during the multi-view image fusion process, an optimal stitching seam is determined through graph-based low-cost computation, and soft-seam region is diffused precisely locate transition areas, thereby effectively mitigating alignment errors induced by parallax and achieving natural and seamless stitching results. Furthermore, considering the computational overhead in the shift regression process, reparameterization strategy is incorpo- rated optimize the structural design, significantly improving algorithm efficiency while maintaining optimal performance. Extensive experiments demonstrate the superior performance of the proposed method against the existing methods. Code is available at hattps://g/ thub. com/DLUT YRH/DSFN
Introduction
Image stitching is a fundamental task in computer vision, aiming to combine multiple images captured from different perspectives or positions into single, high-resolution image with an extended field of view. This task plays crucial role in various applications, such panoramic photography [1] 2). remote sensing 4 medical imaging [), and virtual reality g) where a comprehensive and
seamless representation of a scene is required.
Conventional stitching methods predominantly follow feature-driven paradigm, relying on hand- crafted local feature descriptors (c.g., SIFT [9]. ORB (10)) for feature detection and robust matching algorithms (e.g., RANSAC ( compute homography transformation matrices for reference and target images alignment. However, the planar scene assumption inherent to homography models fails accommodate the complex geometric relationships arising from multi-depth layers in real- world scenarios, resulting in ghosting artifacts and structural misalignment in stitched results. To mitigate these issues, conventional methods adopt two primary optimization strategies: (1) enhancing local alignment accuracy through region-adaptive deformation techniques (e.g., mesh warping [12 13 14)).
and (2) concealing residual artifacts by optimizing seam paths via energy functions [15] 16 While effective in most scenarios, their performance is critically constrained by feature density and quality, leading to failures low-texture, repetitive patterns, or large parallax scenarios.
"Corresponding author.
39th Conference on Neural Information Processing Systems (NcurIPS 2025).

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Recent advancements in deep learning have introduced novel solutions for image stitching. Convo- lutional Neural Network (CNN)-based methods enable end-to-end learning of implicit geometric correlations between images, such as directly predicting transformation parameters through deep homography networks or modeling non-rigid deformations using deformable convolutions 17 18. Some works further integrate spatial attention mechanisms to enhance robustness against dynamic objects [1 20 or employ unsupervised learning frameworks to address the scarcity of annotated real-world data (11]. Nevertheless, existing deep learning methods still face significant challenges: dependency on synthetic training data limits cross-domain generalization capabilities, while ensuring structural consistency in large-parallas scenarios remains challenging.
To address the aforementioned challenges, this paper proposes that focuses on resolving co-planar alignment in large-parallax scenarios and ensuring seamless consisteney transitions in multi-view overlapping regions. First, a two-stage depth- transforma- tion estimation mechanism is introduced for Varge-paralla, alignment. This mechanism leverages depth information differentiate feature disparities of identical objects across varyi ng depth layers, while recursive global-local deformation strategy integrates global homography estimation with localized adaptive warping, addressing the rigidity of conventional sigle-hhmogrrphy models in multi-plane scenes. During multi-view planar fusion, the optimal stitching seam is determined via graph- graph-structured low-cost computation. A diffusion-bad -baeed soft-seam propagation then generates pixel-wise confidence maps to define adaptive blending regions, effectively suppressing misalignment and ghosting artifacts caused by parallax. Additionally, we design a reparameterized strategy to optimize the shift regression model, ensuring the optimal effectiveness and the efficiency. The contributions are summarized as follows:
image stitching
We propose depth-supervised image stitching, which focuses on addressing the alignment challenges caused by large parallax of significant depth differences, enabling the seamless fusion of multi-view images.
The proposed method employs depth-aware two-stage transformation estimation, coupled with a reparameterization strategy, which significantly enhances alignment performance in scenarios with large parallax.
The determination of the soft-seam region enables flexible adjustment for multi-view fusion, effectively avoiding issues such as misalignment and ghosting.
Extensive experiments demonstrate that our method outperforms the ttate-of-the-arts, in terms of the large parallax alignment and seamless fusion.
2 Related Work
2.1 Image Stitching
Feature-Based Image Stitching, The core of manual fcature-based image stitching lies in achieving accurate alignment through effective feature extraction and matching, which relies on sufficient geometric features in the scene. Brown et al. [22) pioneered this field by employing scale-invariant feature extraction combined with random sampling consistency to establish global rigid transfor- mations. To address parallax issues, Li et al. [23] developed an analytical warp function based on point correspondences, enabling improved alignment through geometric constraints. Recognizing the limitations of single global transformations, Gao el al. [24] introduced dual-plan alignment by establishing separate warping models for distinct scene layers, though this approach faced challenges in complex environments with ambiguous planar divisions. Further advancing spatial adaptability,
Zaragoza et al. proposed As Projective As Possible (APAP) (25), which localized mesh- based projective transformations, significantly increasing parameter flexibility while introducing artifacts at depth-discooiinuous regions such as object boundaries.
The inherent alignment challenges in multi-view image stitching often manifest as ghosting artifacts within overlapping regions, necessitating sophisticated seam selection strategies. Zhang et al. [26] proposed dual-scale alignment framework that preserves global structural consistency through optimal homography while enabling local seam-driven adjustments. Subsequent approaches focused on optimizing seam placement through energy minimization principles, with Kwatra et al. [27] introducing graph-based segmentation techniques avoid object intersections. However, the compu- tational intensity of these pixel-level optimization methods presents significant practical limitations

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for real-world applications.
Deep Learning-Basee Image Stitching. While contemporary feature descriptors [28 29 30 demon- strate potential for learned representations, their isolated application within traditional pipelines has limited practical adoption, driving research toward fully learned stitching frameworks. Deep learning approaches circumvent manual feature engineering by learning semantic representations through supervised (Lai et al. [19), Kweon et al. (20)), weakly supervised (Song et al. [31]). or unsupervised (Nie et al. (32)) paradigms, offering enhanced robustness in complex scenes. However, supervised methods' reliance on labeled data constrains their effectiveness in high-parallan, scenarios. Nie et al. [2I] pioneered unsupervised frameworks with improved sross-scene generalization and parallax tolerance. though persistent plane misalignments in extreme depth-varying scenes reveal fundamental limitations of current learning architectures.
2.2 Single Image Depth Estimation
Single image depth estimation aims recover per-pixel depth from monocular visual data. Traditional approaches relied on geometric priors (33 or non-parametric depth transfer mechanisms (34), funda- mentally constrained by color consistency assumptions. The advent of CNNs revolutionized this field through data-driven feature learning. Li el al. [35] pioneered multi-scale superpixel-to-pixs mapping via shallow CNNs with CRF refinement, though limited by local receptive fields. Liu al. (36 advanced this by integrating CRF potentials within CNN frameworks, yet remained constrained by insufficient global context modeling. Eigen er al. [37 introduced a two-stage architecture that significantly enhanced spatial reasoning capabilities. Subsequent breakthroughs by Laina et al. [38 demonstrated the critical role of deep residual architectures capturing holistic scene geometry through expanded receptive fields. The recent emergence of Depth Anything (39 marks paradigm shift, establishing new state-of- ofte--o-thheart performance through unified representation learning.
The Proposed Method
Prior
Weighted Adáition
Warping Operation
Repar Regurameteriza Regression (RR)
ResNet S0
Depth-Awer Aware Roustormation Estimation
Multi-Viow Fusion
Iransurmation Extimation
Suft based Multi View Fasdon
Ohiginal
Depit Normalleation Iransfectmaion
Figure 1: Workflow of the proposed method. consists of two procedure: depth-aware transformation estimation and soft-seam based multi-view fusion. Besides, the transformation estimation process incorporates reparameterized regression to establish the optimal model.
As illustrated in Fig. T in the proposed method, we first feed the target image 44 and reference image I, into the ResNet50 [38 for feature encoding. The extracted features from both views are then processed through a depth-aware transformation estimation module to obtain the warping matrices. To address alignment challenges in large parallax scenarios, a two-step recursive strategy employed for the shift regression. Subsequently, the resulting transformations are applied to observed images for alignment, and soft-seam based multi-view fusion module is employed to blend the aligned images, producing vide-field-of-vieww result T, with natural transitions and no visible artifacts. The comprehensive description of each module is provided in the following.

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3.1 Depth-Aware Transformation Estimation
We employ ResNet50 38 initiate the multi-scale feature extraction from both reference and target images, generating feature pairs at 1/16 and 1/8 resolutions, denoted as {1 P 121/103 and [F F21/5.. Beginning with the coarser 1/ /61ele features, the Feature Correlation Aggrega- tion (FCA) block [40] computes inter-view correspondences through:
Cl5 = FCA(F, ,F,/10)
where Ci, is the correlation volume. regression block then predicts quadrilateral vertex off- sets Ap RAx22 from which coarse homography matrix He R3x3 is derived via Direct Linear Transformation (DLT) 41:
Hc arg min
1p H PAl12
(2)
Pk denotes the coordinate of the k-th point in the reference image, and p/P means the corresponding point of the target image. This initial alignment warps the target feature to f/1/8 = Hc
Subsequently, mesh-based refinement stage employs grid-wise offset estimation for sub-pixel precision. Let . = {(r4, y,)} define the mesh grid, with Radial Basis Function (RBF) interpolation generating the continuous deformation field:
M
A v) - Σ
wm wmo(ll(x, (2im Fmm 31))
m=1
where (0r) = (er)) denotes the Gaussian basis function with shape parameter e. The final dense warping field W combines coarse homography and residual deformation:
W(p) = Hc p (p)).
In the training process, we calculate the mean pixel error in the overlapping region after the coarse and residual transformations separately, which can be expressed
Calignment faliigmmnn f I,
=XI[A, My WH (H)
(5)
7/4 MH- - (1+)ll++
n|I4 My - (W))ll
where MH, M and My are homography transformation masks, inverse homography transforma- tion masks and residual masks, which are obtained through homography H. inverse homography H H-1 and residual transformation A. 7, 3) are balance weights. For ease of calculation, we choose to transform the mask, and since nonlinear transformations do not always support inverse operations, we do not design inverse losses.
To preserve structural consistency with the original scene, we impose shape- prepeepreeeriing constraint for the mesh. We design the mesh loss from the point of the edge size of single mesh and the offset of adjacent meshes. The number of control points is recorded as U x V, ew and ěn are the set of two adjacent edges the mesh and the edge loss of single mesh can be described as:
E ((( memes)) + (U () Σ Co((E.) - -2Hmehh) (6)
1
Ledge U UV (V
where and j are unit horizontal and vertical vectors, a--) is non-linear activation function, H and Wmesh are the length and width of a single mesh. By calculating the loss in the mesh, the mesh stretching is limited and the distortion reduced. We believe that the adjacent edges between meshes in the non-overlapping region should be as parallel as possible, so we constrain the mesh angle as:
Langle
1
Σ
8(1 cos0),
E.1.5a

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where a is the number of edge pairs, 5 is the region label, and is denoted as 1 when the edge pair is in the non-overlapping region, when the edge pair in the overlapping region, and 0 is the angle between the edge pairs. Considering the large parallax caused by significant depth variation, we incorporate the depth information as knowledge prior to supervise the learning of the transformation estimation. Specifically, we obtain the depth map through Depth Anything (39), characterizing the relative depth rather than the absolute depth. To this end, we perform normalization in the overlapping region of the reference and target images to reduce the relative error caused by the depth mutation of the non-overlapping region, expressed as:
Edepth falsgramen (Iár: Lat, X %X...n)
(8)
where Jar; la mean the depth maps of I, Jt. The total loss for depth-aware transformation estimation can be expressed as:
Lalignment + Jeeddge + C Caangte + &Ldepth-
3.2 Soft-Seam based Multi-View Fusion
Based on the results inferenced from the transformation estimation, obtain the aligned image pair Iur, Lut- However, precise alignment remains challenging in real-world parallax scenarios, and the multi-view image fusion process must additionally ensure authenticity and accurate reconstruction, such as preserving structures and achieving natural transitions between multi-view scenes. To address this, we relax the conventional definition of "seams" in the stitching, and suppose that any region requiring fusion within overlapping areas can be treated as a potential seam. We build upon the low-cost seam localization and establish soft-seam region diffused from the distinet seam serve as the adaptive fusion adjustment, aiming to resolve the ghosting and misalignment artifacts while enabling natural transitions.
Specifically, we calculate the corresponding region masks Mwr. Mut based on the aligned images. These masks are fed into a Soft-Seam Estimation (SSE) to obtain soft-seam mask M. within overlapping areas, serving as the candidate region for fusion. SSE module is built upon UNet architecture 42 43), in which 3 x convolutions are replaced with dilated convolutions, with dilation rates are set as 1,2, 3, 4, and 3. At the four skip connections, the same-scale features from both input images are first upsampled using nearest- neighbor interpolation and then passed through 1 x 1 convolution to reduce the number of channels. The difference map is first computed by subtracting the feature maps of the two images pixel by pixel. It is then concatenated with the upsampled features along the channel dimension, and the resulting representation is fed through two dilated convolution layers to further advance the decoding process.
M2 is then integrated with the original aligned image masks through a single filter and the sigmoid function, yielding two more flexible masks Mar, Mat with pixel-level regional adaptability. These adaptive masks are subsequently applied to weighted fusion processing of the aligned images, enabling refined fusion tailored to local pixel characteristics.
In the training process, we first need to determine the terminal points of the seam, expressed as:
Liermina! |(T, - Twr) (Mur ④ - + [I(T, Twt) (Mwt C -Mwe))l1-
(10)
combines the inverted and original masks via element wise multiplication to restrict the fusion mask boundary to the intersection area, controlling its endpoints. In which C represents the pixel-by-pixel calculation of the two masks. -M and -Mar represent the inverted and expanded mask. order to improve the sensitivity of the difference values, we choose the pixel square difference to construct the cost map and the cost loss is defined as:
Lrost Σ IM MM+1)((D + DI-1.) + IIME -M3++ (D'S + Dis+1), (11) 1.J
where D is the squared difference between the warped image Lur and Lut- In order to constrain the smoothness of the fused image, the smoothness loss, which calculates the smoothness penalty by measuring the distance between adjacent pixels within the fusion region of the stitched image, is also adopted:
Lemooth
Σ | Mi+1i - 11 + Σ M M (1 18511). (12)
5

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Furthermore, we also introduce the depth consistency to supervise the inference results. Specifically, the base depth maps are first aligned with the estimated transformation. Then, a secondary local regularization of the aligned depth images wwdr, wdt is performed to further calibrate the local relative depth, expressed as:
Lrey
M ( Iudi) f Iuthe
(13)
MM (F(uor Iwdi) 11s+t)).
F denotes the fusion process. expressed as:
The total loss for the soft-seam based muewi-view fusion can be
L pCterminal + Tooost &Lsmooth ofreg
(14)
3.3 Reparameterization Regression
In the realm of learning-based image stitching methodologies. the adoption of fully connected archi- tectures for shift regression often incurs significant computational costs while yielding suboptimal performance. To mitigate this issue, we leverage reparameterization techniques [4] identify the optimal structural configuration during the parameter regression process.
Although reparameterization techniques enhance feature diversity and structural flexibility through the introduction of Reparameterization Blocks (RepBlocks), existing reparameterized architectures fail to achieve robust performance improvements due to inherent limitations in RepBlocks 445). To address this, we propose a Reparameterization Block Adaption (RBA) algorithm, which dynamically adapts the model training process by selectively integrating either a RepBlock standard convolutional layer based on the specific requirements of the convolution layer.
Specifically, in the model forward process, different convolution structures provide distinct feature representations to extract diverse feature maps from input features. Therefore, in the initial model, following the research in [46], we replace the 3 x convolution in our regression block located behind the FCA block of the proposed depth-aware transformation estimation to a RepBlock consists of x convolution layer Conv' and 3 x 3 convolution layer Conv To evaluate the contribution of these two layers, we formulate a linear combination. Given a set of input feature maps fin E RCin x H W where Cin, H and W are the input channel number, height and width, the output features fout of RepBlock are calculated as:
Sout Re(w, X Conw (Sin) + Wa x (Jin bb)
(15)
where W1, W3 and RCou x1x1 1x1 are the weights and bias. Re denotes the ReLU function. four E RCCu XHX where Cout is the output channel number. In our formulation, W₂ and Wa can be trained evaluate the contribution of the two branches. The contribution of the Cone in the RepBlock is calculated as:
Coss E W1)
C1
C11
Caut (I W3)
(16)
Cout
The effect of our RBA is to prevent the output features of the Conv' layer from playing a damaging role in feature extraction. Therefore, in the training process, if c < ?, where presents the hyperparameter the minimum threshold, we cut the Conw by coupling it to the Conv3 layer, and the parameter weight Waew of the new 3 x layer 3.ew is calculated
(17) where W, and Wa are the weights of Comv and Cone?, and the pad operation indicates adding value 0 around the W, 44 After cutting the 1 x branch, ensure the model can be adapted to the training task while avoiding the feature degradation caused by the additional branches.
Whew W3 Wa + W1 (pad())).)
Experiments
4.1 Implementation Details
Our method is implemented using the PyTorch framework and executed on an NVIDIA RTX 3090 GPU. For training both the depth-aware transformation estimation and soft-seam based multi-view

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fusion models, we employ the Adam optimizer 47 and the learning rate decays exponentially, with an initial value of 10 -4 The transformation model trained for 100 epochs, with the hyperparame- ters X, 7, and 17 set to 3, 3. and 1, respectively. The values of X, 7' rf are identical to those of X, and 7)- 14, Ç. are set 10, 10 and 0.3. For the multi-view fusion model, we initially train the model for 50 epochs on the training set, with the hyperparameters p.
set to 10000, 1000, 1000, 10.
The UDIS-D training set [32] is employed as the training data. To enhance the reliability of the experimental results, we evaluate the model on the UDIS-D testing set and further validate it using real-world data from the IVSD dataset [40]
4.2 Performance Comparison
We compare our method with APAP 255, ELA 23 TRIS (50) and SRS [51, where each method adopts the pre-trained model and configuration parame- ters provided by the official.
LPC
48
SPW 44
UDIS 32
UDIS+ 211,
4.2.1 Qualitative Evaluation
The qualitative comparison on the UDIS-D dataset is shown in Fig. 2 In the first example, our method achieves clearer fusion result for the wall area, effectively avoiding issues such as blurring and ghosting artifacts. Additionally, within the region marked by the red framework, the proposed method successfully preserves the complete information the bicycle without any loss of content. In the second example, while other comparative methods exhibit ghosting content loss, our method delivers a stitching result that is both visually clear and realistic, demonstrating a better reconstruction quality. Furthermore, to compare the alignment accuracy of different methods, we visualize the alignment errors in the lower-right corner of the corresponding figures. It can be observed that the proposed method achieves significantly higher alignment precision compared to the others.
Visual results on the IVSD dataset are presented in Fig. The proposed method demonstrates superior stitching performance across varying depths of the captured scene, with alignment errors further confirming its effectiveness. The consistent performance across both datasets robustly validates the efficacy of the proposed method.
Input
APAP
ELA
LPC
SPW
UDIS
UDIS+
TRIS
SRS
Ours
Figure Visual comparison of stitched images from UDIS-D dataset. The alignment error is visualized in the lower right corner.
Input
APAP
ELA
LPC
SPW
UDIS
UDIS+
TRIS
SRS
Ours
Figure 3: Visual comparison of stitched images from IVSD dataset. The alignment error is visualized in the lower right corner.

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Table 1: Quantitative comparison on UDIS-D and IVSD datasets. marked in red and blue.
The best and second results are
UDIS-D
IVSD
Method
PSNR(1)
SSIM(1) SIQE(1) LPIPS(()
PSNR(1)
SSIM( SIQE(1)
LPIPS(()
APAP
25
23.792
0.794
41.707
0.472
22.904
0.681
39.281
0.454
ELA
23
24.012
0.808
41.781
0.470
23.452
0.701
37.186
0.435
LPC
22.595
0.736
43.616
0.467
20.996
0.641
37.517
0.447
SPW 49
21.606
0.687
41.060
0.466
18.868
0.575
36.156
0.449
UDIS 32
21.171
0.648
42.186
0.475
23.535
0.743
40.474
0,451
UDIS++ 21
25,426
0.837
43.184
0,469
26.649
0.819
46.383
0.439
TRIS 50
24.476
0.821
41.621
0.476
24.187
0.753
40.873
0.448
SRS 51
24.828
0.811
41.857
0.473
24.234
0.796
35.641
0.445
Ours
25.467
0.839
43.732
0.462
26.778
0.820
46.568
0.436
Table 2: Efficiency comparison against the state-of-the-an methods.
APAP
ELA
LPC
SPW
UDIS
UDIS++
TRIS
SRS
Methods
Ours
25
23
48
49
332
21
50
[51
Time (ms)
6683.14
8347.79
13435.47
11651.68
193.66
79.73
107.98
83.17
67.04
4.2.2 Quantitative Evaluation
We employ of evaluation metrics, including PSNR [521. SSIM [53). SIQE [54]. and LPIPS [55]
to conduct a comprehensive performance assessment. According the UDIS++ (21, the test sets of UDIS-D are categorized into three levels based on their complexity, and the corresponding quantitative results are summarized in Table Furthermore, to rigorously evaluate the generalization capability of the proposed method, quantitative results the IVSD dataset are also presented in Table I It is evident that the proposed method outperforms other methods across multiple metrics, substantiating its superiority further.
To further evaluate the efficiency of the proposed method, we present the processing time required by each comparative method for image stitching at size of 512 x 512, with the results summarized in Table 2 It can be observed that, although our method involves deep estimation and inference processes, the overall running time is still better than that of the other methods, making it more suitable for practical applications.
4.2.3 User Study
To evaluate the subjective performance of our method, we conducted a user study to assess the visual quality of the stitched images. The input and output images organized according their scene categories, and participants were presented with set of input images alongside the corresponding output images generated by each comparison method. Participants were asked evaluate quality from multiple perspectives, including ghosting, misalignment, structural accuracy, and realistic scene restoration. They were allowed to zoom in or out for detailed observation and were instructed to rate each image on scale of to 5 based on visual quality. The study involved 50 participants, including 30 researchers or students with background in computer vision and 20 individuals without specific expertise. The results of the user study, as illustrated in Fig, 4 demonstrate that our method consistently received higher ratings compared other methods.
4.3 Ablation Studies
Loss Function Evaluation. We conduct a comprehensive ablation study to analyze the impact of different constraints in the depth-aware transformation estimation and soft-seam based multi-

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PSNR
SSIM
25:45
0.80
Smoothh
45.40
0.888
100
25.5
0.36
vÇlo Lyest
25.30
2525
SIQE
LMPSS
volo Lyook
0:161
27
0.382
Ours
Figure 4: Visual quality survey.
Figure 5: Ablation study on different loss components.
view fusion modules, respectively. During the experiments, we would like to clarify that Lodge and Langle exhibit complementary effects, where retaining one constraint alone renders the other nearly ineffective. Therefore, combine these constraints into unified loss term, denoted as mesh loss Lmeah for ablation analysis. As shown in Fig. 6 the removal of mesh constraints in the transformation estimation leads to noticeable distortions in the deformed images, while the absence of fusion module constraints hinders the model's ability to compensate for stitching errors caused by insufficient local alignment. Furthermore, the lack of depth supervision not only increases the visual errors in both transformation and fusion but also degrades the overall accuracy of the method. The quantitative results across the entire UDIS-D dataset are presented in Fig. [] and Table. Although the ablation of mesh constraints marginally improves certain metrics by relaxing the image distortion limits, this improvement is not meaningful from visual perspective.
Soft-Seam Fusion Evaluation. We also perform an ablation study of the fusion strategy compared with average fusion and seam cutting [27]. As shown in Fig. 7 the first row exhibits the gradient results from different fusion strategies and the second row visualizes the corresponding fusion regions. We can see that larger fusion region increases the likelihood of ghosting artifacts. However, an excessively small fusion region may result in insufficient gradient smoothness. The proposed soft- seam fusion strategy adaptively preserves gradient continuity to the greatest extent, facilitating natural and seamless results.
Adaptive Mask Evaluation. The fusion mask in our method derived from an adaptive weight matrix based on the soft-seam region. A comparison with the absolute weight derived from traditional seam-based methods is illustrated Fig. [ The result produced by our method exhibits a more visually appealing effect and smoother region.
Reparameterization Evaluation. We test 10 thresholds for hyperparameter selection, where à represents the original model and è = 0 represents the model without RBA. Results shown in Fig, indicate that both the original model and the full reparameterizztion model cannot perform the best. The model with è 0.25 realizes the best performance, while maintaining efficient training.
Loss
PSNR(+)SSIM(() SIQE(T) ( LPIPS(.)
w/o Lemooth
25.431
0.833
43.156
0.466
w/o Leest
25.438
0.836
43.186
0.463
w/o Lsmooth
w/o Leost
w/o Lreg
w/o Lreg
25.432
0.837
43.651
0.463
w/o Emeot
25.473
0.840
43.701
0.463
w/o Ldepth
25.434
0.838
43.703
0.463
Ours
25.470
0.839
43.732
0.462
w/o Lmesh
w/o Ldepth
Ours
Figure Ablation study on loss function.
Table 3: Ablation study loss components of transfor- mation estimation and multi-view fusion models.

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Average Fusion
Seam Cutting
Ours
Figure 7: Ablation study of the fusion strategy. The corresponding fusion regions are visualized on the right part.
1.5
1:2
SIQE
1.32
1.29
Training Time
1.09
1,05
1.03
1.00
0.2
Threshold (C)
Input
Absolute Weight
Ours
Figure 9: Ablation study on the hyperparameter è in the RBA.
Figure 8: Visual comparison between the pro- posed adaptive mask and the absolute mask.
5
Limitations
The proposed method primarily designed for stitching two images and currently lacks full capability address the challenges associated with multi-image panoramic stitching. Key limitations include difficulties in maintaining loop consistency and mitigating global error propagation in complex scenarios. These issues can compromise the geometric coherence of the final output, thereby constraining the method's 's robustness and broader applicability.
Conclusion
This paper proposed depth-supervised image stitching method designed to address the alignment challenges in large parallax scenarios and achieve seamless wide field-of-view reconstruction. Firstly, depth-aware two-stage transformation estimation developed, which leverages priors to align targets across varying depth ranges. Secondly, soft- seam region diffusion strategy is introduced to accurately identify transition regions, enabling natural and smooth fusion while mitigating ghosting and misalignment issues. Additionally, the reparameterization strategy for shift regression enhances the adaptability and reduces computational overhead. Extensive experiments validate the effectiveness of the proposed method. Although method can improve multi-view alignment and fusion performance by leveraging depth consistency guidance, the presence dynamic elements in the scene poses challenges for obtaining accurate depth information. In the future, will focus on robust stitching under dynamic conditions to further enhance alignment robustness in such scenarios.
7 Acknowledgment
This work was supported in part by the National Natural Science Foundation of China under Grant 62302078, in part by the Fundamental Research Funds for the Central Universities under Grant 3132025276, and in part by China Postdoctoral Science Foundation under Grant 2023M730741.
10

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Reviews:
[{"review_id": "IFZFp5NB4Z", "reviewer": "Reviewer_xeFo", "summary": "This paper presents a depth-supervised, seam-free image stitching framework tailored for large-parallax scenarios. It introduces a depth-aware two-stage transformation estimation, a soft-seam multi-view fusion module for seamless transitions, and a reparameterization-based shift regression strategy to improve computational efficiency. Experiments show that this method has certain effectiveness.", "questions": "Please refer to the weaknesses proposed in the strength and weakness section.\nAnd I have some minor questions:\n1. What are the time and space complexities of the proposed method?\n2. All experiments in the paper are conducted on stitching two images. However, real-world applications such as 360° panoramic stitching often involve stitching 5 to 10 images and require addressing challenges like loop consistency and global error propagation. I’m curious about how the proposed method performs in such scenarios.", "limitations": "Yes", "rating": 4, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 3, "clarity": 3, "significance": 3, "originality": 2, "strengths_and_weaknesses": "Strengths\n1. The proposed depth-aware two-stage transformation estimation module effectively mitigates alignment issues caused by depth variations. The motivation is clear, and the design is well-justified.\n2. The soft-seam multi-view fusion module relaxes the definition of stitching seams, significantly improving the naturalness of image transitions.\n3. The ablation studies are thorough and systematic, clearly demonstrating the contributions of each module and loss component.\n\nWeaknesses\n1. The $L_{depth}$ relies entirely on the depth maps provided by Depth Anything. However, as a general-purpose model not specifically designed for image stitching tasks, Depth Anything may introduce biases from its training data. Consequently, the loss may misfire in regions with noisy or discontinuous depth maps. It can penalize correct predictions, introduce misleading supervision signals, and steer the main model in the wrong direction.\n2. All experiments are conducted on standard-sized images, whereas real-world stitching tasks often involve ultra-high-resolution images or multi-scale inputs. The paper lacks experiments under such practical and challenging scenarios.\n3. The paper does not include comparisons with image stitching methods based on generative models[1], which are becoming increasingly relevant in recent research.\n4. The paper uses a large number of symbols in its equations, but not all of them are clearly defined. For example, $p_k$ in Equ. (2). It is recommended that the authors include a table of symbol definitions to improve readability.\n5. There are some grammatical errors in the paper, such as \"a depth-supervised image stitching\" in line 43 and \"state-of-the-arts\" in line 64. It is recommended that the authors carefully review and correct the grammatical issues throughout the paper.\n\n[1] Recdiffusion: Rectangling for image stitching with diffusion models. CVPR 2024.", "was_revised": true, "final_justification": "My concerns are well addressed and I will keep the score.", "rebuttal": "## Summary of the reviews\n\nWe sincerely thank the reviewer for their thoughtful evaluation of our work. We appreciate the recognition of our paper's strengths in: (1) clear motivation and well-justified design, (2) improved transition naturalness enabled by the soft-seam fusion module, and (3) thorough analysis and superior performance of our method. We acknowledge the reviewer's concerns about (1) the impact of depth estimation quality on the stitching performance, (2) the effectiveness of our method when handling images of different resolutions; and (3) the performance in panoramic stitching. We will address these concerns in the following.\n\n\n### Weakness 1: Depth Anything for Depth Supervision\n**Response:** The goal of image stitching is to realistic and accurate wide-field reconstruction results. Therefore, multi-view alignment and multi-view fusion are the two core steps in the stitching task. In our method, to improve the accuracy of multi-view alignment, we introduce depth constraints provided by Depth Anything. However, since the Depth Anything model performs inference based on the single image, it may result in inconsistent depth values for the same object under different viewpoints. To this end, we regularize the depths from different views to ensure the depth consistency of the same object across multiple perspectives. In addition, to avoid the impact of depth bias on alignment errors and, consequently, on overall stitching performance, we propose a soft-seam region localization during the multi-view fusion phase. Instead of restricting the stitching seam to a fixed line, we extend it to a candidate region and calculate the optimal fusion within this area by cost evaluation. This further improves the realism and accuracy of the stitching results. \n\nTherefore, our method ensures realistic and accurate stitching outcomes through the combined effect of depth-based constraints and optimal soft-seam fusion. Even when the depth maps fail, the subsequent soft-seam fusion struggles to provide a certain degree of correction. For example, as shown in the third example in Figure 1 of the supplementary material, although the generated depth maps are not ideal in this case, our method still achieves satisfactory stitching results, demonstrating the tolerance for the depth error.\n\n### Weakness 2: Performance Across Different Resolutions\n**Response:** Thanks for your suggestion. We conducted performance evaluations on both low-resolution images (32×32) and high-resolution images (1024×1024), with both types processed from the IVSD dataset. Quantitative comparisons are shown in the table below, where the best results are highlighted in bold. We can see that, the proposed method is able to achieve outstanding performance even on data with extremely high or extremely low resolutions.\n| Size      | Metric | APAP  | ELA   | LPC   | SPW   | UDIS  | UDIS++ | TRIS  | SRS   | **Ours** |\n|-----------|--------|-------|-------|-------|-------|-------|--------|-------|-------|----------|\n| **32×32** | PSNR(↑)  | 22.551| 21.184| 22.915| 22.482| 24.618| 23.481 | 23.178| 22.481| **25.735**|\n|           | SSIM(↑)  | 0.726 | 0.717 | 0.764 | 0.805 | 0.755 | 0.722  | 0.804 | 0.815 | **0.882** |\n|           | SIQE(↑)  | 39.385| 38.843| 38.587| 37.433| 40.185| 42.013 | 43.465| 43.018| **44.627**|\n|           | LPIPS(↓) | 0.453 | 0.422 | 0.473 | 0.553 | 0.502 | 0.419  | 0.418 | 0.442 | **0.402** |\n| **1024×1024**| PSNR(↑)| 24.218| 25.354| 24.368| 24.352| 25.184| 24.389 | 23.171| 22.481| **26.101**|\n|           | SSIM(↑)  | 0.775 | 0.792 | 0.815 | 0.781 | 0.818 | 0.833  | 0.801 | 0.831 | **0.878** |\n|           | SIQE(↑)  | 42.433| 42.732| 41.834| 40.384| 43.152| 43.839 | 42.501| 43.437| **44.143**|\n|           | LPIPS(↓) | 0.444 | 0.432 | 0.465 | 0.487 | 0.415 | 0.425  | 0.422 | 0.416 | **0.410** |\n\n### Weakness 3: Evaluation on Recdiffusion\n**Response:** We have conducted a detailed investigation on Recdiffusion. This paper is specifically designed for rectangling stitched images. Therefore, we can compare the performance of different stitching algorithms by evaluating their results within the Recdiffusion framework. Specifically, we first selected from the DIR-D dataset (used by Recdiffusion) those image groups that correspond to those in the UDIS-D dataset, obtaining both input images and ground truth. We then manually constructed the required input and ground truth data pairs. In our experiment, we first obtained stitching results using different algorithms, and then assessed their rectangling performance using Recdiffusion. In addition to the commonly used PSNR and SSIM metrics, we also incorporated the FID metric to evaluate image quality. The results are shown in the table below. It can be observed that our method provides better support for the rectangling task compared to other methods.\n| Metric | APAP  | ELA   | LPC   | SPW   | UDIS  | UDIS++ | TRIS  | SRS   | **Ours** |\n|--------|-------|-------|-------|-------|-------|--------|-------|-------|----------|\n| PSNR(↑)  | 18.24 | 19.48 | 17.18 | 16.73 | 18.67 | 17.76 | 18.49 | 19.73 | **21.18** |\n| SSIM(↑)  | 0.661 | 0.673 | 0.701 | 0.586 | 0.671 | 0.627 | 0.677 | 0.691 | **0.733** |\n| FID(↓)  | 8.476 | 5.011 | 6.373 | 7.368 | 5.136 | 6.199 | 8.411 | 7.157| **4.347** |\n\n### Weakness 4: Symbol Readability\n**Response:** Thank you for your valuable suggestion. We have carefully reviewed all formulas and provided clear definitions for each symbol, as shown below. Additionally, we will include this symbol table in the paper to further improve readability.\n| Symbol | Definition |\n| --------- | --------- |\n| $I_{r}$ | Reference image |\n| $I_{t}$ | Target image |\n| $I_{wr}$ | Warped reference image |\n| $I_{wt}$ | Warped target image |\n| $M_{wr}$ | Warped reference image mask |\n| $M_{wt}$ | Warped target image mask |\n| $M_{sr}$ | Stitched reference image mask |\n| $M_{st}$ | Stitched target image mask |\n| $M_{s}$ | Seam area mask |\n| $I_{s}$ | Stitched image |\n| $p_k$ | The coordinate of the k-th point in the reference image |\n| $p_k'$ | The coordinates of $p_k$ at the corresponding point of the target image |\n| $I_{wdr}$ | Warped reference depth image |\n| $I_{wdt}$ | Warped target depth image |\n| $\\odot$ | Pixel-by-pixel multiplication |\n| $\\neg M$ | Reverse and expand the mask |\n\n### Weakness 5: Grammatical Errors\n**Response:**  We appreciate your careful review. We have corrected the errors such as “a depth-supervised image stitching” and “state-of-the-arts” as suggested. Additionally, we have thoroughly reviewed the entire manuscript to address and correct other grammatical problems to improve the overall clarity and readability.\n\n### Question 1: Time and Space Complexity\n**Response:** We have tested the time and space complexities of the proposed method. The computational cost is 125.8 GFLOPs and the memory access is 2624 MB.\n\n### Question 2: Performance on Panorama Stitching\n**Response:** To evaluate the performance of our method in 360° panoramic image stitching tasks, we conducted experiments on the MSGA [1] dataset. This dataset was captured using a binocular imaging device capable of infrared and visible imaging across various scenarios, encompassing a wide range of 360° scene content. In our experiments, we selected the visible images for panoramic stitching validation, with each scene containing 5 to 15 consecutive images. Since most existing image stitching algorithms support only two images as input, we adopted a pairwise stitching strategy. Specifically, we first stitch the first and second images, then use the stitched result as the reference image to stitch with the third image, and so on, until all images in the scene are stitched together. To ensure fairness, none of the methods involved additional processing such as loop closure verification or global cumulative error correction during the experiments.\n\nThe quantitative comparison results of various methods are shown in the table below. Your suggestions have provided important insights for our work. In the future, we will further optimize our method design by incorporating your suggestions, with particular focus on addressing key challenges such as loop consistency and global error propagation in panoramic stitching, so as to continuously improve the robustness and applicability of our method to multi-input panoramic stitching. Once again, thank you for your valuable feedback.\n\n[1] Jiang Z, Zhang Z, Liu J, et al. Multispectral image stitching via global-aware quadrature pyramid regression[J]. IEEE Transactions on Image Processing, 2024.\n\n| Metric | APAP  | ELA   | LPC   | SPW   | UDIS  | UDIS++ | TRIS  | SRS   | **Ours** |\n|--------|-------|-------|-------|-------|-------|--------|-------|-------|----------|\n| PSNR(↑)  | 20.167| 22.439| 21.697| 22.738| 22.133| 22.735 | 21.953| 22.921| **23.104**|\n| SSIM(↑)  | 0.735 | 0.791 | 0.733 | 0.805 | 0.767 | 0.791  | 0.752 | 0.801 | **0.811** |\n| SIQE(↑)  | 38.499| 39.714| 37.498| 39.134| 38.125| 38.326 | 37.053| 38.135| **40.137**|\n| LPIPS(↓) | 0.473 | 0.427 | 0.481 | 0.453 | 0.478 | 0.455  | 0.439 | 0.475 | **0.410** |"}, {"review_id": "K3sqmZ2dWR", "reviewer": "Reviewer_H8ZG", "summary": "This paper proposes a depth-supervised image stitching method, aiming to solve the stitching challenge of images with depth differences. This method achieves precise alignment of targets at different depths through two-stage deformation estimation combined with depth consistency constraints. Meanwhile, the strategy is determined through the soft seam area, and smooth fusion is achieved by combining the deep continuity constraint to alleviate the problems of ginning and misalignment. The re-parameterization strategy of shift regression further improves adaptability and reduces computational overhead.", "questions": "1. In cases where the input image exhibits no significant depth variation, is it still possible for this method to maintain accuracy? \n2. In figure2, why is the color difference at the junction of the second group of stitched images larger compared to some comparison methods? What could be the possible reasons for this phenomenon? Has this type of image been evaluated in a user study, and what were the rating results?\n3. Will the method introduced in this paper be made open-source? No relevant links or descriptions have been identified thus far.", "limitations": "Yes", "rating": 5, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 4, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. The introduction of deep supervision effectively addresses the influence of depth disparities on image alignment, showcasing a robust solution to this critical challenge.\n2. The soft seam fusion strategy significantly mitigates ghosting artifacts, resulting in superior visual quality and a more aesthetically pleasing output.\n3. The reparameterization approach minimizes computational overhead while enhancing the overall performance of the method, demonstrating its efficiency.\n4. The incorporation of depth maps to enhance the quality of image stitching represents a promising avenue for future research, highlighting its potential to advance the field.\n\nWeaknesses:\n1. It seems that the method proposed in this paper may have relatively high requirements regarding the generation quality of depth maps, which could potentially affect its applicability in certain scenarios. Could you provide more specific details on this aspect?\n2. Given the complexity of the depth estimation method, there may be notable challenges in optimizing it to meet the demands of real-time applications.", "was_revised": true, "final_justification": "My concerns are well addressed. I will keep my score.", "rebuttal": "## Summary of the reviews\n\nWe sincerely thank the reviewer for their thoughtful evaluation of our work. We appreciate the recognition of our paper’s strengths in: (1) the incorporation of depth maps, (2) the design of re-parameterization, and (3) the improvement in stitching performance brought by soft seam region fusion. We acknowledge the reviewer's concerns about (1) the performance in scenarios with insignificant depth variations, and (2) the improvement of quantitative results. We will address these concerns in the following.\n\n### Weakness 1: Dependence on Depth Quality\n**Response:** We would like to clarify that the proposed method improves multi-view alignment accuracy through the use of depth constraints and enables flexible fusion of multi-view scenes via soft- seam region localization. The combined effect of these two stages allows for accurate and realistic wide field-of-view reconstruction. Considering the limitations of monocular depth estimation, our method applies regularization to the depth maps from different viewpoints to ensure depth consistency for the same target across views. Therefore, any errors introduced due to the limited quality of the generated depth maps do not directly affect multi-view alignment performance. Furthermore, the subsequent fusion in the soft seam region further mitigates the impact of depth errors, ensuring the accuracy of the final stitching results.\n\n### Weakness 2: Real-Time Capability\n**Response:** Thanks for your feedback. We acknowledge that, at present, our method’s computational efficiency does not fully meet the demands of real-time applications. Addressing real-time performance is an important direction for our future research, and we plan to further optimize our algorithm to improve its efficiency for practical use. Possible strategies include model compression, network pruning, and quantization, as well as leveraging hardware acceleration and more lightweight network architectures. We believe that with these targeted improvements, the proposed approach can be effectively adapted to scenarios requiring higher computational speed.\n\n### Question 1: Performance with Insignificant Depth Variations\n**Response:** When the input images do not exhibit significant depth variation, it indicates that the objects in the scene lie at similar depths. Under such conditions, depth constraints remain valid, although their effect may be less pronounced than in scenes with strong depth variation. In addition to the depth constraint, the subsequent soft seam constraint continues to ensure the effectiveness of our method. For example, as shown in the second group of images in Figure 1 of the supplementary materials, the stitched image contains multiple planes with similar depths. In this case, the fine sign structures are barely distinguishable in the estimated depth maps. Nevertheless, our method still achieves successful stitching.\n\n### Question 2:  Color Difference\n**Response:** In this scenario, there are noticeable brightness differences between the target and reference images due to variations in shooting angles. Our method provides gradual compensation for color differences within the fusion regions during multi-view scene integration. However, we do not perform color adjustment across the entire image to avoid negatively affecting the color accuracy of other objects in the scene. As a result, compared to other approaches, our soft-seam based flexible fusion achieves smoother transitions in the multi-view overlap areas and effectively avoids abrupt color changes.\n\nDuring user studies, participants were asked to evaluate quality from multiple perspectives, including ghosting, misalignment, structural accuracy, and realistic scene restoration. Therefore, the results of the user study represent a comprehensive assessment of all these factors. Compared with other methods, the proposed method demonstrates superior overall performance based on this comprehensive evaluation.\n\n\n### Question 3: Open Source\n**Response:** Due to the rebuttal policy, we are not allowed to provide open-source links. We will release the code and resources as soon as the paper is accepted, to facilitate comparison and use by the researchers."}, {"review_id": "hCcHwfuIRe", "reviewer": "Reviewer_Hpo2", "summary": "This work aims for image stitching at challenging scenes with low textures and large parallax. The overall pipeline is consists of two stage; transformation estimation and multi-view fusion stage. At the transformation stage, image features from ResNet50 are aggregated with attention modules, and the regression network estimates the DLT parameters for coarse warping and a residual deformation field.\nIn this stage, the authors newly suggested the mesh loss (edge + angle) which preserves the shape consistency of the mesh, and the depth loss which reduces the difference between relative depths earned from DepthAnything in the overlapping region.\nAt the fusion stage, the authors proposed a soft-seam estimation module, which outputs soft-seam region from the mask of warped target and reference images. Depth values are again regularized to have similar values in warped pixels at this stage.\nLastly, authors suggested to use RepBlock (RepVGG) at the regression block of the transformation estimation stage. Instead of naively incorporating the RepVGG layer, they proposed a reparameterization algorithm that selectively integrates the RepBlock or a standard convolution layer.\nThe quality of the proposed method was evaluated on the UDIS-D dataset through quantitative, qualitative, and user studies.                    . \t`", "questions": "1. The alignment of two depth maps without camera positions of two views can not be trivially done with 2D warping. The minimum and maximum values taken for depth normalization should be carefully chosen depending on the distance between the surface and the camera, to make warped affine depth maps to be numerically similar. However, the paper does not include the depth normalization method in both the transformation estimation module and the fusion module. In ablation, there was no significant quality enhancement by the depth loss and the regression loss. I doubt the correctness and effectiveness of depth supervision modules.\n\n2. Most of the network architecture specifications are excluded in this paper. 1) There is no exact explanation of the soft-seam estimation module’s operation. 2) It’s hard to find where the RBA algorithm was adopted since the regression module's architecture was not described.\n\n3. The quantitative improvements by the regularizations are numerically weak (Table 2) compared to the noticeable quality enhancement in Figure 6. Please analyze this mismatch and supplement the effectiveness of the suggested contributions.\n\n4. What is the inherent limitation of RepBlocks? (line 193). Please explain the specific behavior. Why does it need to be selectively incorporated instead of completely removing it?", "limitations": "Yes", "rating": 4, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 2, "clarity": 1, "significance": 2, "originality": 3, "strengths_and_weaknesses": "Strength\n1. The authors adopted a monocular depth estimation network to resolve the large parallax caused by the big depth difference. The authors suggested a regularization loss that can incorporate prior depth knowledge into the end-to-end image stitching pipeline.\n2. The authors suggested a novel approach to consider seam as a candidate area and adopted a soft seam estimation module to estimate the blending regions. The qualitative result of ablation shows that such soft blending achieves a sharp yet smooth transition in the image gradient space.\n\nWeakness.\n1. In the transformation estimation stage, it’s hard to differentiate the contribution of this work and the contribution of previous studies (FCA block and alignment loss). Dividing the sections into preliminaries and the contribution would be helpful.\n2. There is no explanation of the operations or the architecture of the Soft-Seam-Estimation module which is the key contribution. The paper only demonstrates the input and output. Moreover,  the loss equations (10), (11), and (12) are not intuitive enough to understand their objective in the optimization. What kind of pixel calculation does ⊙? Why does this operation work on the inverted and non-inverted region mask?\n3. In the ablation study (Figure 5) the result of the full model does not excel in most evaluation metrics except SIQE. This violates the argument that adopting the regularization and depth prior will enhance the stitching quality. Is there a reason for the mismatch between qualitative results and quantitative results?", "was_revised": true, "final_justification": "After reading the other reviewers' comments and the authors' reply, my concerns regarding the clarification of contributions and their effectiveness have been resolved. Therefore, I am willing to raise my score to borderline accept.", "rebuttal": "## Summary of the reviews\n\nWe appreciate the recognition of our paper’s strengths in: (1) depth prior constraints, (2)  soft-seam candidate fusion, and (3) comprehensive ablation analysis. We acknowledge the reviewer's concerns about (1) the innovativeness of the transformation estimation, (2) the architectural design of the soft-seam fusion, (3) the necessity of the reparameterization, and (4) the inconsistency between quantitative and qualitative results. We will address these concerns in the following.\n\n### Weakness 1: Contribution of the Proposed Transformation Estimation\n**Response:** We would like to clarify that previous works perform correlation computations on image features and train the transformation network using self-supervised constraints between pre-aligned and post-aligned images. In these works, the reference information provided by image features is quite limited, especially in scenes with repetitive content, such as large surfaces of ground or buildings. In such cases, similar texture regions often exhibit significant depth variations. Relying solely on the appearance features for transformation estimation makes it difficult to accurately establish correspondences between different viewpoints. To this end, we introduce a regularized depth constraint, enabling more precise multi-view alignment by jointly constraining content and depth information in multiple dimensions. Furthermore, to enhance feature diversity, we employ a reparameterization strategy to provide additional frequency features, which further strengthens the representation of image features and leads to more accurate transformation estimation results.\n\nBased on your feedback, we would place greater emphasis on highlighting the differences between the proposed method and previous works to further underscore the contributions of our method.\n\n### Weakness 2: Architecture and Equations\n**Response:** The Soft-Seam Estimation module is designed to predict a pixel-level adaptive weighted fusion mask within the overlapping region of two aligned images, enabling fine-grained fusion based on local pixel features. This module is built upon a UNet architecture, in which 3×3 convolutions are replaced with dilated convolutions, with dilation rates are set as 1, 2, 3, 4, and 5. At the four skip connections, the same-scale features from both input images are first upsampled using nearest-neighbor interpolation and then passed through a 1×1 convolution to reduce the number of channels. The difference map is then obtained by subtracting the features from the two images pixel by pixel. This difference map is concatenated with the upsampled features along the channel dimension and fed into two layers of dilated convolutions (with the same dilation rate), continuing the decoding process. In this way, the inconsistency information of the overlapping region is explicitly injected into the network, enabling more precise seam localization.\n\nEq (10) combines the inverted and original masks via element-wise multiplication to restrict the fusion mask boundary to the intersection area, controlling its endpoints. ⊙ denotes element-wise multiplication.\nEq (11) computes the squared Euclidean distance between corresponding pixels of the two warped images within the fusion region.\nEq (12) calculates the smoothness penalty by measuring the distance between adjacent pixels within the fusion region of the stitched image.\n\n### Weakness 3: Quantitative Result Analysis\n**Response:**  In the ablation study, we evaluated performance using PSNR, SSIM, SIQE, and LPIPS. Higher PSNR, SSIM, and SIQE, and lower LPIPS indicate better quality. As shown in Fig. 5, our method achieves the best SIQE and LPIPS scores, and is second in PSNR and SSIM (w/o mesh is highest). This is partly because existing methods, including ours, compute PSNR and SSIM only over the common regions, due to the lack of ground truth for full stitched images. The w/o mesh variant achieves high alignment in these regions by allowing unrestricted deformation, which often causes severe distortion elsewhere. In contrast, our mesh constraint avoids excess deformation, ensuring better structure preservation and seamless results in the stitched output. Thus, a slightly lower PSNR and SSIM is reasonable, and our method achieves a better balance between quantitative scores and actual visual quality.\n\n### Question 1: Depth Consistency and Improvement\n**Response:** \n- **[Depth consistency]** The depth estimation adopted in our work is based on direct estimation from a single image. As such, the resulting depth map only reflects the relative depth variations within the depicted scene, rather than representing the true absolute depth of the scene. Therefore, after obtaining depth maps from two different viewpoints, we perform a normalization operation (as described in lines 152 and 181 of the submitted manuscript) to unify the depth values across both maps. This ensures depth consistency for the same object across different viewpoints, which is essential for the accurate transformation estimation and ensuring the multi-view alignment precision.\n\n- **[Performance improvement]** In Fig. 6, the results obtained from w/o $L_{smooth}$, w/o $L_{cost}$, w/o $L_{reg}$ and w/o $L_{depth}$ exhibit structural breaks in the door area and label ghosting, while the result from w/o $L_{mesh}$ shows wall discontinuities. In contrast, our method achieves clear and accurate stitching results, demonstrating significant advantages over the ablated versions. \nFurthermore, in Table 2, our method achieves the best performance in SIQE and LPIPS metrics, while it is only slightly outperformed by w/o $L_{mesh}$ in PSNR and SSIM. Since ground-truth stitched images are unavailable, PSNR and SSIM are calculated only on the overlapping regions. The w/o mesh method allows excessive deformation for higher alignment in these areas, often distorting non-overlapping regions. In contrast, our mesh constraint limits such deformations and prevents unreasonable distortion. Thus, our slightly lower PSNR and SSIM are reasonable.\n\nSince the goal of image stitching is to ensure seamless results while preserving the integrity and correctness of the original structures, excessive pursuit of alignment in the common region at the cost of unwanted distortions is undesirable. In summary, considering both the visual and quantitative results, our method is more reasonable and effective compared to other ablated versions.\n### Question 2: Architecture and Algorithm\n**Response:** \n\n- **[Soft-Seam estimation]** Due to space limitations, kindly refer to the first paragraph of our response to weakness 2 for further details.\n\n- **[RBA algorithm]** In the transformation regression process, we propose the Reparameterization Regression(RR) to enhance the network's fitting ability and prevent issues such as overfitting. As shown in Fig. 1(c), for RR, we use RepBlock as the basic operation block during the reparameterization process. Based on the proposed RBA algorithm, RepBlocks can be adaptively applied by pruning the 1×1 branch of the RepBlock according to its contribution. This approach helps to prevent noise or overfitting risks introduced during training. As a result, the model is adaptively simplified while avoiding redundant computations or harmful feature interactions.\n\n### Question 3: Quantitative Improvement\n**Response:** In image stitching, it is essential to prevent issues such as ghosting, structural distortion, and misalignment after integrating scene content from different viewpoints. These problems serve as key factors in assessing the effectiveness of various stitching algorithms. However, the regions where such issues occur only occupy a small portion of the total pixel count in the generated wide-view images. Consequently, when evaluating performance using metrics that are calculated on a per-pixel basis, such as PSNR and SSIM, there is usually only a small difference among the results of different methods. This also explains why the values of different versions reported in Table 2 are relatively close. Despite this, problems like ghosting, structural distortion, and misalignment are highly perceptible in visual performance, leading to significant differences in visual quality between different methods, as shown in Fig. 6.\n\n### Question 4: Reparameterization Design\n**Response:** RepBlock adds an extra auxiliary 1×1 convolution branch to the original 3×3 convolution branch, which provides improved feature diversity during training. During inference, these two branches are reparameterized into a single-path 3×3 convolution structure. This approach enhances feature representation capability, but the multi-branch structure can introduce complex coupling during the training process and may result in unstable optimization. In the image stitching task, convolutional layers for edge sensitivity and fine structure localization within our RR module may not require such structural complexity. Using RepBlock in these layers can introduce unnecessary noise or increase the risk of overfitting. As a result, not every convolutional layer equally benefits from this architectural complexity. To address this issue and improve performance while eliminating structural redundancy, we propose the RBA algorithm. This algorithm dynamically chooses between RepBlock and a standard 3×3 convolutional layer. By evaluating the contribution of the 1×1 and 3×3 branches in each RepBlock, the RBA algorithm prunes the 1×1 branch if it proves to have negative effects on feature extraction. Experimental results in Figure 9 demonstrate that selectively applying RepBlocks to intermediate and high-level semantic layers leads to better performance than applying them throughout the entire network."}, {"review_id": "IKtHnynOtS", "reviewer": "Reviewer_zXpV", "summary": "This paper presents a novel two-stage image stitching method enhanced with deep supervision, targeting the challenge of stitching images with significant parallax. By combining linear and nonlinear deformations alongside soft seam region fusion, the method achieves high-quality, seamless image stitching. Reparameterization further boosts computational efficiency. Experimental results validate the effectiveness of this method.", "questions": "1. Does the method proposed in this paper have a strong dependence on the generation quality of depth maps? Will the absence of an effective depth map lead to errors in the stitched image?\n2. Why is the method of combining linear transformation and nonlinear transformation adopted for image alignment? Have you ever attempted to directly achieve alignment through nonlinear deformation?\n3. In the ablation experiment, why do some indicators show some improvement when the mesh constraint is removed? Does it mean that the mesh constraint is meaningless?", "limitations": "Yes.", "rating": 6, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "", "weaknesses": "", "quality": 4, "clarity": 3, "significance": 3, "originality": 3, "strengths_and_weaknesses": "Strengths:\n1. This paper presents a two-stage image stitching method designed to achieve high-quality stitching of large parallax images. This method introduces deep supervision to address the challenges associated with aligning images that have significant differences in perspective and depth.\n2.  Reparameterization is employed to enhance the computational performance of the stitching process.  This technique not only improves the efficiency of the method but also reduces the consumption of computing resources, making it more practical for real-world applications.\n3. This paper also incorporates depth information into the image stitching.  By leveraging depth cues, the method optimizes the stitching of large parallax images, leading to more accurate and visually pleasing results. \n\nWeakness:\nThe manuscript presents a multifaceted evaluation of the proposed method. However, the visual comparisons are limited, and the robustness under different imaging conditions requires further investigation. Additional experimental results are recommended.", "was_revised": true, "final_justification": "Through the authors' rebuttal, my concerns have been well addressed. The proposed depth-supervised image fusion method demonstrates excellent fusion performance and good generalization ability, and it is novel to the image fusion field.", "rebuttal": "## Summary of the reviews\n\nWe appreciate the recognition of our paper’s strengths in: (1) a two-stage image stitching method to handle large parallax and depth differences, (2)  reparameterization strategy, and (3) depth constraint on challenging images. We acknowledge the reviewer's concerns about (1)generalization, (2) depth dependence, (3) linear-nonlinear transformation estimation (4) constraint improvement. We will address these concerns in the following.\n\n### Weakness: Generalization\n**Response:** Thank you for your suggestion. Due to page limitations, we have provided more visual comparison results in the supplementary materials. The input images cover a wide range of environments, including nighttime, low-light conditions, large wall surfaces, expansive ground areas, and scenes with large depth of field. In the future, we will also upload the supplementary materials alongside the main text to allow for a more comprehensive performance comparison.\n\n### Question 1: Depth Dependence\n**Response:** We would like to clarify that our method utilizes depth information as a constraint to improve multi-view alignment accuracy, and exploits soft seam localization to enable reliable wide-field image reconstruction. While high-quality depth maps can facilitate the production of better stitching results, they are not the sole determining factor for the effectiveness of our method.\n\nDuring the transformation estimation stage, we leverage the relative depth variations between objects in the image to guide more precise alignment, as depth differences may exist among objects. In the fusion stage, since the depth variation within a single object tends to be regular, we extend the stitching seam to create a flexible space and perform adaptive fusion to achieve accurate wide-field reconstruction.\nTherefore, our method is fundamentally guided by depth maps to enable improved image stitching, rather than relying on depth maps to directly complete stitching. The combination of depth constraint and soft-seam localization ensures robust and high-quality stitching performance.\n\n\n### Question 2:  Linear-nonlinear Transformation Estimation\n**Response:** Linear transformations facilitate global and consistent deformation of the image, helping to preserve the overall structure and maintain shape integrity. Nonlinear transformations, meanwhile, allow for more flexible local adjustments, which can improve the alignment of fine details in multi-view scenarios. While directly applying more flexible nonlinear transformations for multi-view alignment may seem intuitive, we find that relying solely on nonlinear transformations achieves good alignment only when the parallax between images is small. When there is significant parallax, this approach can easily lead to a loss of shape preservation, resulting in stretching or distortion and thus a decline in visual quality. Therefore, we adopt a coarse-to-fine strategy that combines linear and nonlinear transformations for image alignment. Specifically, we first use linear transformation for global initial alignment, and then apply nonlinear transformation to locally refine the result. This method ensures the overall structural consistency while also improving the alignment accuracy in local regions.\n\n### Question 3: Constraint Improvement\n**Response:** In our method, the mesh constraint is introduced to prevent excessive stretching and to preserve the intrinsic shape of the mesh. As illustrated in Fig. 6 of the submitted manuscript, removing the mesh constraint (denoted as w/o mesh) can improve alignment in the overlapping regions, but it also lessens the effectiveness of control point displacement, resulting in wave-like and irregular distortions at the image boundaries. Due to the unavailability of ground truth for stitched images in real-world scenarios, previous works typically evaluate PSNR and SSIM metrics only within overlapping regions, rather than across the entire image. This explains why the w/o mesh configuration achieves the best performance on these metrics. In contrast, the SIQE and LPIPS metrics consider image quality on a global scale, and our method exhibits clear advantages on these global metrics. Taken together, both the quantitative and qualitative results demonstrate that the design of our method is reasonable and effective."}]

Author rebuttal:

Meta-review: The paper received positive reviews (2 borderline accept, 1 accept, 1 strong accept).

The reviewers praised the approach (the two stage deformation estimation and stitching with depth consistency constraints) finding that the results were strong. Reviewers suggested that the breadth of the experiments could be expanded (such as 360 pano stitching) as well as more thorough comparisons (such as generative techniques).

I also advocate for acceptance but would strongly encourage the authors to edit the manuscript in adherence with the suggestions of Reviewer xeFo and Reviewer Hpo2.
Decision: Accept (poster)