Deep learning-based stereo camera multi-video synchronization
This work addresses the need for cost-effective and flexible stereo camera systems, though it appears incremental as it compares existing deep learning methods rather than introducing a new one.
The paper tackled the problem of synchronizing video streams from stereo cameras, which is typically done with hardware, by comparing deep learning-based software methods and demonstrated that some are efficient and generalizable for this task.
Stereo vision is essential for many applications. Currently, the synchronization of the streams coming from two cameras is done using mostly hardware. A software-based synchronization method would reduce the cost, weight and size of the entire system and allow for more flexibility when building such systems. With this goal in mind, we present here a comparison of different deep learning-based systems and prove that some are efficient and generalizable enough for such a task. This study paves the way to a production ready software-based video synchronization system.