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학술대회 Weakly-Supervised Stitching Network for Real-World Panoramic Image Generation
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저자
송대영, 이건수, 이희경, 엄기문, 조동현
발행일
202210
출처
European Conference on Computer Vision (ECCV) 2022 (LNCS 13676), pp.54-71
DOI
https://dx.doi.org/10.1007/978-3-031-19787-1_4
협약과제
22GH1300, [전문연구실] 이머시브 미디어 전문연구실, 서정일
초록
Recently, there has been growing attention on an end-to-end deep learning-based stitching model. However, the most challenging point in deep learning-based stitching is to obtain pairs of input images with a narrow field of view and ground truth images with a wide field of view captured from real-world scenes. To overcome this difficulty, we develop a weakly-supervised learning mechanism to train the stitching model without requiring genuine ground truth images. In addition, we propose a stitching model that takes multiple real-world fisheye images as inputs and creates a 360 ?닔 output image in an equirectangular projection format. In particular, our model consists of color consistency corrections, warping, and blending, and is trained by perceptual and SSIM losses. The effectiveness of the proposed algorithm is verified on two real-world stitching datasets.
KSP 제안 키워드
End to End(E2E), Field of view(FOV), Fisheye images, Image generation, Learning-based, Real-world, Weakly supervised learning, Wide field, deep learning(DL), ground truth, learning mechanism