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Hyperlapse generation of omnidirectional videos by adaptive sampling based on 3D camera positions

机译:通过基于3D摄像机位置的自适应采样来生成全向视频的超间隔拍摄

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Capturing a city with an omnidirectional camera can produce a continuous motion street view and provides the view of the moving person. However, watching such a video is not necessarily pleasant because the videos are often excessively lengthy. In this paper, we propose a method for shortening an omnidirectional video. Subsampling a video captured by a hand-held camera displays a significant amount of destabilization resulting from shaking. This prompted us to propose an adaptive subsampling scheme that selects optimal frames by minimizing our cost function based on 3D camera positions. This optimal selection suppresses only the translational camera instabilities. The rotational instabilities are initially ignored and later compensated for. This approach allowed us to successfully generate a subsampled and stable omnidirectional video. In addition, we propose an alternative measure of translational instability to evaluate our frame selection.
机译:使用全向摄像机捕获城市可以产生连续运动的街道视图,并提供移动人员的视野。但是,观看此类视频不一定令人愉快,因为视频通常太长。在本文中,我们提出了一种缩短全向视频的方法。对由手持摄像机捕获的视频进行二次采样会显示由于晃动而导致的大量不稳定现象。这促使我们提出了一种自适应子采样方案,该方案通过基于3D摄像机位置的成本函数最小化来选择最佳帧。该最佳选择仅抑制平移相机的不稳定性。旋转不稳定性最初被忽略,随后得到补偿。这种方法使我们能够成功生成二次采样且稳定的全向视频。此外,我们提出了一种翻译不稳定性的替代方法来评估我们的框架选择。

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