论文标题
非刚性结构中的有机先验
Organic Priors in Non-Rigid Structure from Motion
论文作者
论文摘要
本文主张在运动（NRSFM）中使用有机先验的经典非刚性结构。通过有机先验，我们的意思是无价的中间信息与NRSFM矩阵分解理论固有的固有信息。结果表明，这种先验居住在分解的矩阵中，而且令人惊讶的是，现有方法通常会忽略它们。该论文的主要贡献是提出一种简单，有条不紊，实用的方法，该方法可以有效利用这种有机先验来解决NRSFM。所提出的方法除了在低级别形状上流行的方法外，没有其他假设，并为NRSFM提供了可靠的解决方案。我们的工作表明，有机先验的可访问性与摄像机运动和形状变形类型无关。除此之外，本文还提供了对NRSFM分解的见解 - 无论是在形状和运动方面 - 是显示NRSFM单旋均益处的第一种方法。此外，我们概述了如何使用所提出的基于有机先验的方法有效地恢复运动和非刚性3D形状，并证明结果表现出明显的余量优于先前的无NRSFM性能。最后，我们通过在几个基准数据集上进行广泛的实验和评估来介绍我们方法的好处。
This paper advocates the use of organic priors in classical non-rigid structure from motion (NRSfM). By organic priors, we mean invaluable intermediate prior information intrinsic to the NRSfM matrix factorization theory. It is shown that such priors reside in the factorized matrices, and quite surprisingly, existing methods generally disregard them. The paper's main contribution is to put forward a simple, methodical, and practical method that can effectively exploit such organic priors to solve NRSfM. The proposed method does not make assumptions other than the popular one on the low-rank shape and offers a reliable solution to NRSfM under orthographic projection. Our work reveals that the accessibility of organic priors is independent of the camera motion and shape deformation type. Besides that, the paper provides insights into the NRSfM factorization -- both in terms of shape and motion -- and is the first approach to show the benefit of single rotation averaging for NRSfM. Furthermore, we outline how to effectively recover motion and non-rigid 3D shape using the proposed organic prior based approach and demonstrate results that outperform prior-free NRSfM performance by a significant margin. Finally, we present the benefits of our method via extensive experiments and evaluations on several benchmark datasets.
