Generation of Upright Panoramic Image From Non-Upright Panoramic Image

Jingguo Liu, Heyu Chen, Shigang Li, Jianfeng Li; Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 2024, pp. 5261-5270

The inclination of a spherical camera results in nonupright panoramic images. To carry out upright adjustment, traditional methods estimate camera inclination angles firstly, and then resample the image in terms of the estimated rotation to generate upright image. Since sampling an image is a time-consuming processing, a lookup table is usually used to achieve a high processing speed; however, the content of a lookup table depends on the rotational angles and needs extra memory to store also. In this paper we propose a new approach for panorama upright adjustment, which directly generates an upright panoramic image from an input nonupright one without rotation estimation and lookup tables as an intermediate processing. The proposed approach formulates panorama upright adjustment as a pixelwise image-to-image mapping problem, and the mapping is directly generated from an input nonupright panoramic image via an end-to-end neural network. As shown in the experiment of this paper, the proposed method results in a lightweight network, as less as 163MB, with high processing speed, as great as 9ms, for a 256x512 pixel panoramic image.