Full panoramic images is becoming popular, 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. 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.
Full panoramic images is becoming popular, 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. 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.
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