基于运动管道结构的视频帧选择深度学习方法

F. Banterle, R. Gong, M. Corsini, F. Ganovelli, L. Gool, Paolo Cignoni
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引用次数: 2

摘要

使用视频序列帧的运动结构(SfM)可能是一项具有挑战性的任务,因为有大量冗余信息,计算时间随帧数呈二次增长,会出现低质量图像(例如,模糊帧),从而降低重建的最终质量等。为了克服所有这些问题,我们提出了一种新的深度学习架构,旨在通过使用预测的子采样频率选择帧来加速SfM。这种架构是通用的,可以学习/提取任何算法的知识,用于从视频中选择帧以生成高质量的重建。一个关键的优势是我们可以实时运行我们的架构,节省计算,同时保持高质量的结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Deep Learning Method for Frame Selection in Videos for Structure from Motion Pipelines
Structure-from-Motion (SfM) using the frames of a video sequence can be a challenging task because there is a lot of redundant information, the computational time increases quadratically with the number of frames, there would be low-quality images (e.g., blurred frames) that can decrease the final quality of the reconstruction, etc. To overcome all these issues, we present a novel deep-learning architecture that is meant for speeding up SfM by selecting frames using predicted sub-sampling frequency. This architecture is general and can learn/distill the knowledge of any algorithm for selecting frames from a video for generating high-quality reconstructions. One key advantage is that we can run our architecture in real-time saving computations while keeping high-quality results.
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