ReCapture: AR-Guided Time-lapse Photography

要旨

We present ReCapture, a system that leverages AR-based guidance to help users capture time-lapse data with hand-held mobile devices. ReCapture works by repeatedly guiding users back to the precise location of previously captured images so they can record time-lapse videos one frame at a time without leaving their camera in the scene. Building on previous work in computational re-photography, we combine three different guidance modes to enable parallel hand-held time-lapse capture in general settings. We demonstrate the versatility of our system on a wide variety of subjects and scenes captured over a year of development and regular use, and explore different visualizations of unstructured hand-held time-lapse data.

著者
Ruyu Yan
Cornell University, Ithaca, New York, United States
Jiatian Sun
Cornell University, Ithaca, New York, United States
Longxiulin Deng
Cornell University, Ithaca, New York, United States
Abe Davis
Cornell University, Ithaca, New York, United States
論文URL

https://doi.org/10.1145/3526113.3545641

会議: UIST 2022

The ACM Symposium on User Interface Software and Technology

セッション: XR Applications

6 件の発表
2022-11-01 18:00:00
2022-11-01 19:30:00