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.
|Publication status||Published - 2020|
|Event||30th British Machine Vision Conference, BMVC 2019 - Cardiff, United Kingdom|
Duration: 2019 Sep 9 → 2019 Sep 12
|Conference||30th British Machine Vision Conference, BMVC 2019|
|Period||19/9/9 → 19/9/12|
Bibliographical notePublisher Copyright:
© 2019. The copyright of this document resides with its authors.
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition