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Using Local Refinements on 360 Stitching from Dual-fisheye Cameras

Full panoramic images have several applications, ranging from virtual reality to 360◦ broadcasting. Such visualization method is growing, especially after the popularization of dual-fisheye cameras, which are compact and easy-to-use 360◦ imaging devices, and low-cost platforms that allow immersive experiences. However, low-quality registration and compositing in which artifacts are noticeable in the stitching area can harm the user experience. Although it is challenging to compose such images due to their narrow overlap area, recent works can provide good results when performing a global alignment. Nevertheless, they often cause artifacts since global alignment is not able to address every aspect of an image. In this work, we present a stitching method that performs local refinements to improve the registration and compositing quality of 360◦ images and videos. It builds on a feature clustering approach for global alignment. The proposed technique applies seam estimation and rigid moving least squares to remove undesired artifacts locally. Finally, we evaluate both to select the best result between them using a seam evaluation metric. Experiments showed that our method reduced the stitching error in at least 42.56% for images and 49.45% for videos when compared with existing techniques. Moreover, it provided the best results in all tested images and in 94.52% of the video