Despite the long history of image and video stitching research, existing academic and commercial solutions still produce strong artifacts. In this work, we propose a wide-baseline video stitching algorithm for linear camera arrays that is temporally stable and tolerant to strong parallax. Our key insight is that stitching can be cast as a problem of learning a smooth spatial interpolation between the input videos. To solve this problem, inspired by pushbroom cameras, we introduce a fast pushbroom interpolation layer and propose a novel pushbroom stitching network, which learns a dense flow field to smoothly align the multiple input videos for spatial interpolation. Our approach outperforms the state-of-the-art by a significant margin, as we show with a user study, and has immediate applications in many areas such as virtual reality, immersive telepresence, autonomous driving, and video surveillance.

@inproceedings{lai2019video,
    author    = {Lai, Wei-Sheng and Gallo, Orazio and Gu, Jinwei and Sun, Deqing and Yang, Ming-Hsuan and Kantz, Jan}, 
    title     = {Video Stitching for Linear Camera Arrays}, 
    booktitle = {BMVC},
    year      = {2019}
}

• • Video Stitching With Spatial-Temporal Content-Preserving Warping, CVPR Workshops, 2015.
• • AutoPano Video.
• • NVIDIA VRWorks.