A Convex Approach for Non-rigid Structure from Motion Via Sparse Representation
A Convex Approach for Non-rigid Structure from Motion Via Sparse Representation
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DOI：
10.5220/0006078603330339
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发表时间：
2017
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影响因子：
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通讯作者：
Junjie Hu;T. Aoki
中科院分区：
文献类型：
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作者：
Junjie Hu;T. Aoki
本文提出了一种基于稀疏表示的凸解，用于从二维图像序列中同时恢复三维非刚性结构和摄像机运动。大多数现有的方法依赖于低秩假设。但是，对于局部变形较强的物体，重建效果较差。此外，当摄像机运动是未知的，有没有凸的解决方案，从运动的非刚性结构（NRSfM）。为了解决这个问题，我们估计非刚性结构的稀疏表示。在本文中，我们估计相机运动通过稀疏谱范数最小化方法，然后引入一个快速l1范数最小化算法来重建三维结构。它们都是凸的，因此，我们的方法给出了一个全局最优解。我们的方法可以处理强局部变形的对象，也不需要低秩先验。实验结果表明，我们的方法达到了国家的最先进的重建性能CMU基准数据集。
This paper presents a convex solution for simultaneously recovering 3D non-rigid structures and camera motions from 2D image sequences based on sparse representation. Most existing methods rely on low rank assumption. However, it will lead to poor reconstruction for objects with strong local deformation. Also, when camera motion is unknown, there is no convex solution for non-rigid structure from motion (NRSfM). In order to solve this problem, we estimate non-rigid structures by sparse representation. In this paper, we estimate camera motions through a sparse spectral-norm minimization approach, and then a fast l1-norm minimization algorithm is introduced to reconstruct 3D structures. Both of them are convex, therefore, our method gives a global optimum. Our method can handle objects with strong local deformation and also doesn’t need low rank prior. Experimental results show that our method achieves state-of-the-art reconstruction performance on CMU benchmark dataset.