360 Stitching from Dual-Fisheye Cameras Based on Feature Cluster Matching

In the past years, captures made by dual-fisheye lens cameras have been used for virtual reality, 360 broadcasting and many other applications. For these scenarios, to provide a good- quality experience, the alignment of the boundaries between the two images to be stitched must be done properly. However, due to the peculiar design of dual-fisheye cameras and the high variance between different captured scenes, the stitching process can be very challenging. In this work, we present a 360 stitching solution based on feature cluster matching. It is an adaptive stitching technique based on the extraction of feature cluster templates from the stitching region. It is proposed an alignment based on the template matching of these clusters, successfully reducing the discontinuities in the full-view panorama. We evaluate our method on a dataset built from captures made with an existing camera of this kind, the Samsung's Gear 360. It is also described how we can extend these concepts from image stitching to video stitching using the temporal information of the media. Finally, we show that our matching method outperforms a state-of-the-art matching technique for image and video stitching.