Non-Rigid structure from motion (NRSfM), is a long standing and central problem in computer vision, allowing us to obtain 3D information from multiple images when the scene is dynamic. A main issue regarding the further development of this important computer vision topic, is the lack of high quality data sets. We here address this issue by presenting of data set compiled for this purpose, which is made publicly available, and considerably larger than previous state of the art. To validate the applicability of this data set, and provide and investigation into the state of the art of NRSfM, including potential directions forward, we here present a benchmark and a scrupulous evaluation using this data set. This benchmark evaluates 16 different methods with available code, which we argue reasonably spans the state of the art in NRSfM. We also hope, that the presented and public data set and evaluation, will provide benchmark tools for further development in this field.

中文翻译：

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基于运动的非刚性结构的基准和评估

来自运动的非刚性结构 (NRSfM) 是计算机视觉中长期存在的核心问题，它使我们能够在场景动态时从多个图像中获取 3D 信息。关于这个重要的计算机视觉主题的进一步发展的一个主要问题是缺乏高质量的数据集。我们在这里通过提供为此目的编译的数据集来解决这个问题，该数据集是公开可用的，并且比以前的技术水平大得多。为了验证此数据集的适用性，并提供和调查 NRSfM 的最新技术，包括潜在的前进方向，我们在此使用此数据集提供基准和严谨的评估。该基准测试使用可用代码评估 16 种不同的方法，我们认为这些方法合理地跨越了 NRSfM 的现有技术。我们也希望，