Simultaneous motion segmentation and Structure from Motion

L. Zappella, A. D. Bue, X. Lladó, J. Salvi
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引用次数: 6

Abstract

This paper presents a novel approach to simultaneously compute the motion segmentation and the 3D reconstruction of a set of 2D points extracted from an image sequence. Starting from an initial segmentation, our method proposes an iterative procedure that corrects the misclassified points while reconstructing the 3D scene, which is composed of objects that move independently. This optimization procedure is made by considering two well-known principles: firstly, in multi-body Structure from Motion the matrix describing the 3D shape is sparse, secondly, the segmented 2D points must give a valid 3D reconstruction given the rotational metric constraints. Our formulation results in a bilinear optimization where sparsity and metric constraints are enforced at each iteration of the algorithm. The final result is the corrected segmentation, the 3D structure of the moving objects and an orthographic camera matrix for each motion and each frame. Results are shown on synthetic sequences and a preliminary application on real sequences of the Hopkins 155 database is presented.
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同步运动分割和基于运动的结构
本文提出了一种同时计算从图像序列中提取的一组二维点的运动分割和三维重建的新方法。从初始分割开始,我们的方法提出了一个迭代过程,在重建由独立运动的物体组成的3D场景时纠正错误分类的点。该优化过程考虑了两个众所周知的原则:首先,在多体运动结构中,描述三维形状的矩阵是稀疏的;其次,在给定旋转度量约束的情况下,分割的二维点必须给出有效的三维重构。我们的公式导致双线性优化,其中在算法的每次迭代中强制执行稀疏性和度量约束。最终的结果是经过校正的分割,运动物体的3D结构和每个运动和每帧的正交相机矩阵。给出了在合成序列上的结果,并介绍了在Hopkins 155数据库真实序列上的初步应用。
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