Robust 3D Segmentation of Multiple Moving Objects Under Weak Perspective
Robust 3D Segmentation of Multiple Moving Objects Under Weak Perspective
复制标题
弱透视下多个运动物体的鲁棒 3D 分割
DOI：
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发表时间：
2006
期刊：
影响因子：
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通讯作者：
D. Chetverikov
中科院分区：
文献类型：
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作者：
Levente Hajder;D. Chetverikov
在弱透视相机模型下，考虑了包含多个独立运动的、可能遮挡的刚性物体的场景。我们获得了一组特征点跟踪跨多个帧和解决的问题的三维运动分割的对象存在的测量噪声和离群值。我们将运动的鲁棒结构（SfM）方法[5]扩展到3D运动分割，并将其应用于具有遮挡的真实污染跟踪数据。已经提出了许多3D运动分割的方法[3，6，14，15]。然而，它们中的大多数并不是针对实践中经常出现的噪声和异常损坏的数据开发和测试的。由于在所有关键步骤中始终使用强大的技术，我们的方法可以科普这样的数据，如合成和真实的图像序列的一些测试所示。
A scene containing multiple independently moving, possibly occluding, rigid objects is considered under the weak perspective camera model. We obtain a set of feature points tracked across a number of frames and address the problem of 3D motion segmentation of the objects in presence of measurement noise and outliers. We extend the robust structure from motion (SfM) method [5] to 3D motion segmentation and apply it to realistic, contaminated tracking data with occlusion. A number of approaches to 3D motion segmentation have already been proposed [3, 6, 14, 15]. However, most of them were not developed for, and tested on, noisy and outlier-corrupted data that often occurs in practice. Due to the consistent use of robust techniques at all critical steps, our approach can cope with such data, as demonstrated in a number of tests with synthetic and real image sequences.