Pixel-wise Deep Image Stitching

• URL: http://arxiv.org/abs/2112.06171v1
• Date: Sun, 12 Dec 2021 07:28:48 GMT
• Title: Pixel-wise Deep Image Stitching
• Authors: Hyeokjun Kweon, Hyeonseong Kim, Yoonsu Kang, Youngho Yoon, Wooseong Jeong and Kuk-Jin Yoon
• Abstract summary: Image stitching aims at stitching the images taken from different viewpoints into an image with a wider field of view. Existing methods warp the target image to the reference image using the estimated warp function. We propose a novel deep image stitching framework exploiting the pixel-wise warp field to handle the large-parallax problem.
• Score: 21.824319551526294
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image stitching aims at stitching the images taken from different viewpoints into an image with a wider field of view. Existing methods warp the target image to the reference image using the estimated warp function, and a homography is one of the most commonly used warping functions. However, when images have large parallax due to non-planar scenes and translational motion of a camera, the homography cannot fully describe the mapping between two images. Existing approaches based on global or local homography estimation are not free from this problem and suffer from undesired artifacts due to parallax. In this paper, instead of relying on the homography-based warp, we propose a novel deep image stitching framework exploiting the pixel-wise warp field to handle the large-parallax problem. The proposed deep image stitching framework consists of two modules: Pixel-wise Warping Module (PWM) and Stitched Image Generating Module (SIGMo). PWM employs an optical flow estimation model to obtain pixel-wise warp of the whole image, and relocates the pixels of the target image with the obtained warp field. SIGMo blends the warped target image and the reference image while eliminating unwanted artifacts such as misalignments, seams, and holes that harm the plausibility of the stitched result. For training and evaluating the proposed framework, we build a large-scale dataset that includes image pairs with corresponding pixel-wise ground truth warp and sample stitched result images. We show that the results of the proposed framework are qualitatively superior to those of the conventional methods, especially when the images have large parallax. The code and the proposed dataset will be publicly available soon.

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