ReWiTe: Realistic Wide-angle and Telephoto Dual Camera Fusion Dataset via Beam Splitter Camera Rig

Venue: acmmm_org_ACMMM_2024_Conference
Published: 2024-03-04 06:09:57

Abstract: The fusion of images from dual camera systems featuring a wide-angle and a telephoto camera has become a hotspot problem recently. By integrating simultaneously captured wide-angle and telephoto images from these systems, the resulting fused image achieves a wide field of view (FOV) coupled with high-definition quality. Existing approaches are mostly deep learning methods, and predominantly rely on supervised learning, where the training dataset plays a pivotal role. However, current datasets typically adopt a data synthesis approach generate input pairs of wide-angle and telephoto images alongside ground-truth images. Notably, the wide-angle inputs are synthesized rather than captured using real wide-angle cameras, and the ground-truth image is captured by wide-angle camera whose quality is substantially lower than that of input telephoto images captured by telephoto cameras. To address these limitations, we introduce a novel hardware setup utilizing a beam splitter to simultaneously capture three images, i.e. input pairs and ground-truth images, from two authentic cellphones equipped with wide-angle and telephoto dual cameras. Specifically, the wide-angle and telephoto images captured by cellphone 2 serve as the input pair, while the telephoto image captured by cellphone 1, which is calibrated to match the optical path of the wide-angle image from cellphone 2, serves as the ground-truth image, maintaining quality on par with the input telephoto image. Experiments validate the efficacy of our newly introduced dataset, named ReWiTe, significantly enhances the performance of various existing methods for real-world wide-angle and telephoto dual image fusion tasks.

Main paper: 
 introduction: 
 The deployment of dual camera systems with a wide-angle (W) camera and a telephoto (T) camera have become widespread in mainstream smartphones, e.g. iPhone 15, HuaWei Mete 60, OPPO find x7. As Fig. 1 shows, the images from the W camera have wide field of view (FOV), and the images from the T camera have high definition quality. It is a natural idea to let the dual cameras simultaneously shoot images and fuse these two images to generate the result with both wide FOV and high definition quality. Consequently, this problem has emerged as a recent hotspot in the field [20] .In existing state-of-the-art approaches, some utilize the traditional image blending framework [6] . However, this method is limited because the input T image has a smaller field of view (FOV) than the input W image. Consequently, it can only enhance the center overlapping regions and cannot enhance the un-overlapping regions. Deep learning based super-resolution methods [14, 20] exhibit strong capabilities and substantial potential in addressing this problem. The mainstream methods among them rely on supervised learning, where the training data plays a crucial role in training a robust model.The current W and T dual camera fusion datasets are primarily synthetic, e.g. CameraFusion [20] . As shown in Fig. 2 Figure 2 : A comparison between our ReWiTe dataset and the synthesized CameraFusion dataset. In ReWiTe, all input pairs of telephoto (T) and wide-angle (W) images are captured using real cameras, with the ground-truth (GT) image captured by a real T camera as well. In contrast, in CameraFusion, the input W image is synthesized from the GT image through artificial degradation, and the GT image is captured by a W camera, which has significantly lower quality compared to the T camera. 'Wide' is short for the wide-angle camera, and 'Tele' is short for the telephoto camera.image serves as the input T image, while the shot W image serves as the ground-truth (GT) image. To synthesize the input W image, the GT image undergoes distortions such as downsampling and noise addition. However, since the GT image is shot by the W camera, the quality of input T image is significantly higher than that of the GT image. This quality difference can confuse the network during training, leading it to produce outputs similar to the GT image despite the reference T image which has superior quality. Consequently, the trained model may incorrectly utilize the highquality reference to generate lower-quality results, which is not the intended outcome. In addition, the quality difference between the synthesized input W image and the GT image is artificially introduced, through operations like downsampling and noise addition. These synthetic distortions can hardly accurately simulate the real quality distortion occurring throughout the complex imaging pipeline, including processes like optical lens effects, demosaicing, tone adjustment, image enhancement, denoising, etc.As mentioned by [11] , due to the inherent characteristics of deep learning networks and the limitations associated with synthetic datasets, the necessity for realistic training data in this domain becomes apparent. This motivates us to construct a realistic dataset in this paper. Our approach involves using a real dual-camera system to capture the input pair of W and T images, ensuring that their qualities remain realistic without artificial degradation. Additionally, we employ another T camera to capture an image with the same optical path as the W image, which serves as the GT image for the W input. This approach guarantees that the GT image is realistic and maintains a quality level as high as the reference T image. By doing so, we encourage the training of deep models to fully leverage the high-quality details present in the T input to enhance the W input. Example images from ReWiTe are shown in Fig. 3 .To build the ReWiTe dataset, we build a hardware, as shown in Fig. 4 . Our hardware includes two cellphones, a beamsplitter, and a fixation device. The captured images from the T camera of cellphone No.1 is used as the GT image. The W and T cameras of cellphone No. 2 capture images as the input W and T images. The beamsplitter divides the optical path into a 50%-50% split, allowing the T camera of cellphone No. 1 and the W camera of cellphone No. 2 to capture images from the same optical path. We conduct the calibration for image alignment and mitigating their differences in color and scale.In addition to constructing the ReWiTe dataset, we conduct experiments to evaluate the performance of various state-of-the-art (SOTA) methods on ReWiTe. Furthermore, we utilize the training data provided by ReWiTe to retrain these SOTA methods and evaluate their performance once more. Quantitative and qualitative results demonstrate that our ReWiTe dataset can enhance the accuracy of various state-of-the-art algorithms compared to models trained on synthetic datasets such as CameraFusion [20] .Contributions: To the best of our knowledge, this is the first realistic dataset for the telephoto and wide-angle dual camera image fusion task. It provides 342 sets of authentic input telephoto images, input wide-angle images and ground-truth images for training and testing purposes. 
related work 2.1 related datasets: 
 In related image enhancement problems for camera imaging, a common method to build datasets is to generate synthetic datasets for training and testing, such as CUFED5 [27] for reference-based super-resolution, DIV2K [3] , Set5 [4] , Set10 [29] , Urben100 [8] for single image super resolution, CameraFusion [20] and MiddleBury [17] for W and T dual camera fusion. In these datasets, the ground truth images are artificially degraded to generate the input images. The degradation process involves operations like downsampling, adding blur, introducing random noise, and applying compression noise. However, the manual degradation process can hardly simulate the imaging differences among cameras with varying imaging pipelines and parameters, leading to a disconnect between training data and real-world application scenarios.To the best of our knowledge, the problems with accessible real annotated data include 1) obtaining real annotated data for single image denoising through shooting a burst of images and then average [1, 2] , and 2) obtaining real annotated data for single-image super-resolution through a beam splitter system [11] . The latter one motivates us to also use a beam splitter to build the ReWiTe dataset.Table 1 shows the summary of exiting multi-image and singleimage super resolution datasets. Among the existing datasets, none of them can provide all the real images of T, W, and GT. This motivates us to establish real annotated data for the emerging problem of W and T dual camera fusion.Table 1 : Summary of existing image fusion and superresolution datasets. While realistic datasets exist for the single-image super-resolution task, e.g. RealSR [5] and Im-agePairs [11] , there is a lack of realistic datasets for the W and T dual camera fusion task. The proposed ReWiTe dataset aims to address this limitation.Telephoto (T) Wide-angle (W) Ground-truth (GT) DIV2K [3] N/A Synthetic Real Urban100 [8] N/A Synthetic Real RealSR [5] N/A Real Real ImagePairs [11] N/A Real Real CUFED5 [27] Real Synthetic Real CameraFusion [20] Real Synthetic Real MiddlyBury [17] Real Synthetic Real Our ReWiTe Real Real RealAlthough the W and T dual-camera image fusion problem does not need to enhance the pixel resolution, most of existing methods are super-resolution methods.The mainstream existing methods are supervised methods, including DCSR [20] and FaceDeblurSig [12] , TTSR [24] , SRNTT [26] , MASA [16] and Shim20 [18] . They have to be trained on synthetic datasets currently due to the lack of real data for supervision. Additionally, they exhibit insufficient control over artifacts. Self-supervised learning, e.g. Selfedzsr [28] and Zedusr [23] , address the issue of lacking real data in supervised learning. They propose warping T images to W perspectives as ground truth for network training. However, the inherent occlusion challenges in native T and W images remain unresolved. In addition, these methods often impose certain requirements on the model. Not all backbone models can be easily integrated into self-supervised networks.Stereo super-resolution methods, like StereoSR [9] and PASSR [19] , use homogeneous dual-camera configurations as input, deviating from the current mainstream camera setups. The enhancement potential of homogeneous configurations is less than that of heterogeneous configurations, and they also face the challenge of lacking real datasets.In single-image super-resolution methods, benefiting from real datasets such as ImagePairs [11] , models can have effective training. In self-supervised/unsupervised learning methods, GANs [21, 22] surpass the quality limits of ground truth theoretically but are prone to generating artifacts, conflicting with the requirements of mobile imaging applications. To solve the problem in this paper, single-image super-resolution methods fail to use the high-quality reference T images. 
rewite dataset: 
 The building of the ReWiTe dataset includes 1) the beam splitter camera rig based hardware design, and 2) the calibration for the built hardware and the shot images.The hardware design objective is to enable three cameras to simultaneously capture images and ensure that the shot input W image and the shot GT image share the same optical path. The primary challenge lies in constructing the beam splitter-based camera rig.One possible approach for spectral division involves inserting a beamsplitter between the lens and the sensor [7] . However, this method cannot use commercial phone cameras as the camera of the dataset system because it requires a complete reconstruction of the entire camera components, including the lens, sensor, ISP, etc. The advantage of this approach is that the calibration of the spectral division system becomes somewhat simpler. However, the imaging effects produced by these reconstructed components are difficult to maintain consistent with the quality of real commercial cellphones. Because the final usage of dual camera fusion is mostly for phone camera systems, we did not adopt this approach.Another approach to spectral division is to split the light before the lenses of commercially available cellphone cameras, which is the approach we have taken. This increases the difficulty of calibration as each of the three hardware components-the two cellphones and the beamsplitter-has six degrees of freedom in motion. They need to be jointly calibrated to the accurate spectral path. Despite the calibration challenges, this approach allows to obtain the most authentic images because the entire imaging process occurs within the commercially available cellphones. Apart from a halving of light intensity due to spectral division, the rest of the imaging process is identical to capturing images using a cellphone in real-world scenarios. Thus, we adopted this approach.Our hardware consists of two cellphones, a beamsplitter, and a fixation device. The optical path of the three cameras on the two cellphones using our hardware is shown in Fig. 4 . The 3D model of our hardware is shown in Fig. 5 , and the real hardware is shown in Fig. 6 . The important parts of our hardware are detailed below.• Cellphones: We use two OPPO Find X6 cellphones. The camera parameters are shown in We utilize the W and T cameras of the OPPO Find X6 cellphones for our study, without considering other industrial cameras or DSLR cameras. This decision is driven by the widespread usage of smartphone cameras today. The potential application of W and T dual-camera image fusion is predominantly in smartphones, which already possess the necessary dual-camera systems, computational resources, and high demand from users. Different camera choices would result in varying quality differences between dual-camera images. To ensure that our dataset corresponds to practical scenarios, we opt to use phone cameras to capture the images and construct the dataset.We have root access on the OPPO Find X6 cellphones that are used in our hardware, and we deliberately refrain from letting the cellphones run the enhancement methods inside the phone's imaging software to avoid them changing the image quality. In the scale alignment, we calculate the overlap regions between the original Wide-angle (W) and GT images. The overlap regions of the original W image is upsampled to the size of the original GT image, so as to make the calibrated W image and the GT image have the same FOV. The overlap region is duplicated for the original Telephoto (T) image to perform its scale alignment, so as to keep the FOV differences of the W and T images not changed after the calibration. In the color alignment, to address tonal differences between the calibrated W image and the original GT image, using the calibrated W image as the guide, the original GT image is tone mapped to obtain the calibrated GT image.After setting up the hardware, we proceed with calibration to generate images for the ReWiTe dataset. Firstly, we conduct coarse and fine hardware alignment to ensure that the W camera of cellphone 2 capturing the input W image and the T camera of cellphone 1 capturing the GT image share precisely the same optical path, and that there are no occlusions between the captured W and GT images. We select scenes with rich occlusions to perform the coarse and fine alignment, as Fig. 7 shows. Secondly, as illustrated in Fig. 8 , we perform scale alignment and color alignment, to ensure that the calibrated W and GT images have the same FOV and color tone, while maintaining consistent FOV differences between the calibrated W and T images as observed in the original W and T images. Thirdly, as demonstrated in Fig. 9 , we conduct manual annotation to mark problematic regions where the GT image provides incorrect annotation for the input W image.• Coarse alignment: The fundamental purpose of the alignment is to make the input W image and the ground truth image without occlusion. We perform coarse alignment by using the knobs to move the position and shooting angle of the cellphone No. 1 and cellphone No. 2. As shown in Figure 7 , we select scenes with rich occlusions to perform the alignment. • Fine alignment: After the coarse alignment, we fix the cellphone No. 2 and continue to adjust the knobs of cellphone No. 1. By observing the occlusion information between W 2 and T 1 at different knob positions, we continuously adjust the position of cellphone No. 1, until the occlusions do not appear. • Scale alignment: Through the operations in the above two steps, we have obtained occlusion-free W 2 and T 1 . However, since they are captured by cameras with different FOVs, the image scales are different, and, in the un-overlapping regions, the T 1 image cannot provide the corresponding GT information for the input W image. We perform scale alignment to make the sizes of the same objects in W 2 and T 1 consistent so that the GT image can provide the corresponding annotation for every pixel of the calibrated W image. We perform this alignment by calculating SIFT features [15] on both W 2 and T 1 , finding overlap regions between similar features, and computing a projection transformation matrix to enlarge the overlap region of W 2 . To keep the FOV differences between the calibrated W and T images consistent with the FOV differences between the original W and T images, we duplicate the overlap region to the original T image and enlarge the overlap region of the original T image with the same transformation of the original W image. • Color alignment: Due to differences of the capturing devices, W 2 and T 1 exhibit substantial color variations. We employed a 3D color tone adjustment method, calculating a 256 × 256 × 256 color mapping matrix to adjust the color tone of T 1 .EQUATIONEQUATIONwhere 𝜔 (k) is the set of pixels in T 1 whose intensities are equal to k, and |𝜔 (k)| is the number of pixels in the set 𝜔 (k). • Image cropping: The images captured by cellphones have a resolution of 4K. However, the boundary regions of these images may exhibit darkening due to vignetting effects. To mitigate the impact of these effects on image quality, we crop out the boundary regions of the captured images, resulting in a cropped resolution of 3496 × 2472, as shown in Table 3 . • Manual annotation: As shown in the Figure 9 , our image capture process may encounter situations where some regions are not calibrated correctly, such as fast-moving objects, calibration errors, or defocusing of T and GT images. To prevent these regions from impacting the quality of our training data, we conduct manual annotation to mark out these problematic areas.In scale alignment, there are two approaches: upsampling the overlap region of the W image to match the size of the GT image or downsampling the GT image to match the size of the overlap region of the W image. Both methods accomplish scale alignment, but the downsampling approach sacrifices details in the GT image, resulting in smaller quality differences between the W input and GT images compared to the upsampling approach. To ensure that the training data reflects higher quality differences between the input W and GT images, we select the upsampling approach. 
comparison algorithms: 
 The comparison methods we select are the SOTA dual camera image fusion/super-resolution methods, including DCSR [20] , SelfDZSR [28] , ZeDUSR [23] , the SOTA reference-based super-resolution methods, including C2-matching [10] , , MASA [16] , TTSR [24] , SRNTT [26] , and the SOTA single-image super resolution methods, including RCAN [25] , EDSR [14] , Real-ESRGAN [22] .For dual-camera imaging in cellphones, the task primarily belongs to image fusion and enhancement. Both the input W and T images have the same resolution, such as 4K, and the desired output should also maintain this resolution, e.g. 4K. There is typically no requirement from users to upscale the resolution to 8K, 16K, or higher. Therefore, when constructing the ReWiTe dataset, we ensure that the input W, input T, and the GT images have identical resolutions, as indicated in Table 3 .However, in many comparison algorithms discussed in Section 4.1, the models default to increasing the pixel resolution of the input image, such as 2x or 4x upsampling, and the upsampling ratio remains fixed unless the model structure is modified.For fair comparison, as depicted in Fig. 11 , we adhere to these methods by downsampling the input W images by a factor of four to generate inputs for the comparison algorithms. Table 4 and Fig. 10 show the quantitative and qualitative results of different comparison algorithms. We also show the example results of algorithms trained on CameraFusion vs. ReWiTe in Fig. 12 .As shown, when the training data transfers from the synthetic dataset of CameraFusion to our realistic dataset of ReWiTe, all the comparison algorithms obtain positive quality improvements. This verifies the benefits of the proposed ReWiTe dataset for various algorithms in fusing real W and T images. Among the results, the performances of dual camera image fusion/super-resolution methods, i.e. DCSR, SelfDZSR, and ZeDUSR, unsurprisingly achieve the best results. They utilize both the input W image and the input T image to estimate the output and are typically designed to solve this task.Single-image super-resolution algorithms, i.e. RCAN, EDSR, Real-ESRGAN, and SwinIR, are not competitive to dual camera image fusion/super-resolution methods. This is because these algorithms only utilize the input W image to super-resolve the output without leveraging the input T image at all. This observation underscores the advantages of incorporating the input T image in addressing this problem.Reference-based super-resolution algorithms, i.e. C2-matching, MASA, TTSR, and SRNTT, utilize both input W images and input T images to estimate the output. However, the their performances fall below dual-camera image fusion algorithms, and are even lower than some single-image super-resolution algorithms. The reason is that, due to the assumptions of input and reference images shot in different scenes, positions, and time, they usually try to transfer more high-quality details from the reference image into the input low-resolution image, even if the textures of the two images are not exactly the same. While this can usually improve the visual quality, the structure change of textures or even artifacts may be introduced into the results, leading to loss of PSNR/SSIM values. 
conclusions: 
 This paper introduces a realistic W and T dual camera fusion dataset, named ReWiTe. It is created using the W and T cameras of one cellphone to capture the input pair of W and T images, while the T camera of another cellphone is used to capture the GT image. A hardware setup employing a beam-splitter is designed, and a series of calibration processes are conducted to ensure that the input W image and the GT image share the same optical path. Consequently, the GT image maintains consistent quality with the input T image and provides annotations for the input W image at every pixel. Experimental results demonstrate the effectiveness of the ReWiTe dataset in enhancing various existing methods for the real-world W and T dual camera fusion task.  
rewite: realistic wide-angle and telephoto dual camera fusion dataset via beam splitter camera rig: 
 Anonymous AuthorsFigure 1 : Examples of the input pair of telephoto (T) and wide-angle (W) images from the dual cameras, along with the ground-truth (GT) image in the proposed ReWiTe dataset. Notably, all three images are captured by real cameras and none of them are synthesized. The quality of the GT image is as high as that of the input T image. In addition, the GT image shares the same optical path and field of view (FOV) with the input W image, providing annotations for the input W image at every pixel. The red boxes indicate the enlarged image regions.The fusion of images from dual camera systems featuring a wideangle and a telephoto camera has become a hotspot problem recently. By integrating simultaneously captured wide-angle and telephoto images from these systems, the resulting fused image achieves a wide field of view (FOV) coupled with high-definition quality. Existing approaches are mostly deep learning methods, and predominantly rely on supervised learning, where the training dataset plays a pivotal role. However, current datasets typically adopt a data synthesis approach, where the wide-angle inputs are synthesized rather than captured using real wide-angle cameras, and the ground-truth image is captured by wide-angle cameras whose quality is substantially lower than that of input telephoto images captured by telephoto cameras. To address these limitations, we introduce a novel hardware setup utilizing a beam splitter to simultaneously capture three images, i.e. input pairs and groundtruth images, from two authentic cellphones equipped with wideangle and telephoto dual cameras. Specifically, the wide-angle and telephoto images captured by cellphone 2 serve as the input pair, while the telephoto image captured by cellphone 1, which is calibrated to match the optical path of the wide-angle image from cellphone 2, serves as the ground-truth image, maintaining quality on par with the input telephoto image. Experiments validate the efficacy of our newly introduced dataset, named ReWiTe, which can 
 
 Appendix:  


Decision: This paper received 1 Weak Accept, 1 Reject, 1 Borderline Reject in the first round of review. After rebuttal, one reviewer remained unchanged (Weak Accept), and two reviewer slightly raised the scores from Reject to Borderline Reject, and Borderline Reject to Borderline Accept. The concerns remaining are on the realistic value of the dataset, and the appropriateness of the experiment comparsion. Given the situation of mixed scores, AC carefully examined all materials at hand. In addition to the issues pointed out by reviewers, AC also noticed the minor color discrepancy between input W and the created GT. By discussing this paper with SAC, AC made a hard decision to accept it. Yet, the authors are required to improve the manuscript substantially, as they said in the rebuttal.

Reviews: review: paper_summary: paper_summary: The fusion of combining images from wide FoV and telephoto lenses results in high quality images with wide FoV. This kind of fusion is especially beneficial of mobile devices. Although many deep learning based method utilize synthetic dataset to train the model, and the real word data is mostly kept internally within the companies developing the smartphones. To speed up the development in this field, the authors have devised a setup to capture the dataset to train these models using commercially available smartphone camera, and a highly precise beam splitter mounting station which coupled with the calibration gives accurate ground truth and image pairs.
strength_weakness: strength_weakness: **1.** Its the first realistic dataset designed specifically for the telephoto and wide-angle dual-camera image fusion task. This is significant in a field where most datasets are synthetic and may not adequately capture real-world variations. Thus, the dataset is novel.

**2.** The beam-splitter camera rig to ensure that the ground truth (GT) image shares the same optical path as the wide-angle (W) image is a sophisticated technique. This ensures high-quality, realistic GT data that is directly comparable to the input data, enhancing the reliability of the dataset for training and evaluation. 

**3.** The detailed calibration process to align images and mitigate differences in color and scale demonstrates a thorough approach to dataset creation. 

**4.** As shown in results in the paper as well as visual results in supplementary materials training on the ReWiTe dataset improves the performance of various state-of-the-art methods compared to synthetic datasets. This practical validation highlights the dataset's potential to advance the field significantly. 

**5.** The dataset consists of 342 sets of images, providing a substantial amount of data for both training and testing purposes. This volume and variety enhance the dataset's utility and applicability.
limitations: limitations: **1. (Minor)** The creation of the dataset relies heavily on a specific hardware setup involving multiple cellphones and a beam-splitter. This complexity could limit the replicability of the dataset creation process (Unless the 3D structure is opensourced) 

**2.** Although 342 image sets provide a good starting point (especially for fine tuning an already trained model), the dataset size might still be relatively small for training deep learning models that require large amounts of data. Increasing the dataset size could improve the robustness and generalizability of models trained on it. Although authors can iteratively add more images in future to increase the size of dataset.
paper_summary: paper_summary: This paper introduces a hardware setup utilizing a beam splitter to simultaneously capture three images. It captures wide-angle and telephoto images, as well as ground-truth images, from two authentic cell phones equipped with cameras. The contribution is the hardware setup.
strength_weakness: strength_weakness: The contribution is the hardware setup that can capture three images together. The hardware includes two cellphones, a beamsplitter,
and a fixation device. With the hardware, they construct a realistic dataset.
limitations: limitations: 1. I cannot understand how to use the beamsplitter to get the same scene. From fig.4, it looks like two cameras are perpendicular to each other, then how to get the same contents?
2. It is more like an engineering work with some tricks. 
3. In Table 4, when the training data transfers from the synthetic dataset of CameraFusion to our realistic dataset of ReWiTe, all the comparison algorithms obtain positive quality improvements. I think it is obvious that when training and testing are the same(ReWiTe), there will be gains. But, we cannot conclude that these gains are provided by the real data.
paper_summary: paper_summary: This paper introduces the ReWiTe dataset, a new method for fusing wide-angle and telephoto images using a novel hardware setup involving a beam splitter and dual cellphones. Unlike existing approaches that rely on synthetic datasets, ReWiTe captures high-quality, realistic images, providing accurate pixel-level annotations. Experiments demonstrate that models trained on ReWiTe perform better in image fusion and super-resolution tasks, highlighting the dataset's potential to improve real-world applications.
strength_weakness: strength_weakness: 1. The ReWiTe dataset is the first to provide realistic wide-angle and telephoto images, addressing the limitations of synthetic datasets and improving model training for dual-camera image fusion.

2. Using an interesting setup, including beam splitter and dual cellphones, the dataset captures telephoto and wide-angle images along with a high-quality ground-truth image, ensuring accurate pixel-level annotations and consistent image quality.
limitations: limitations: 1. The need for precise calibration to ensure accurate alignment of images could introduce variability and errors, affecting the consistency and reliability of the dataset.
2. Although innovative, the dataset contains only 342 image sets, which might be insufficient for training deep learning models at scale.
3. The evaluation provided may not sufficiently emphasize the dataset's contribution, lacking comprehensive comparisons with a wide range of existing methods and datasets to solidify its impact.
4. It is unclear whether the dataset will be made open source.