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.
翻译:立体视觉对许多应用非常重要。目前,来自两个相机的流的同步主要是使用硬件完成的。软件同步方法将减少整个系统的成本、重量和大小,并在构建这样的系统时提供更大的灵活性。为此,我们在这里呈现了不同基于深度学习的系统的比较,并证明有些对这样的任务足够高效和通用。这项研究为生产就绪的基于软件的视频同步系统铺平了道路。