Weakly-Supervised Stitching Network for Real-World Panoramic Image Generation

Autor:	Song, Dae-Young, Lee, Geonsoo, Lee, HeeKyung, Um, Gi-Mun, Cho, Donghyeon
Rok vydání:	2022
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
Popis:	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 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. Comment: Accepted by ECCV2022 (poster)
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2209.05968 Zobrazit plný text záznamu View this record from Arxiv