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首页> 外文会议>IEEE International Conference on Image Processing >Scene segmentation from depth and color data driven by surface fitting
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Scene segmentation from depth and color data driven by surface fitting

机译：根据曲面拟合驱动的深度和颜色数据进行场景分割

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Scene segmentation is a very challenging problem for which color information alone is often not sufficient. Recently the introduction of consumer depth cameras has opened the way to novel approaches exploiting depth data. This paper proposes a novel segmentation scheme that exploits the joint usage of color and depth data together with a 3D surface estimation scheme. Firstly a set of multi-dimensional vectors is built from color and geometry information and normalized cuts spectral clustering is applied to them in order to coarsely segment the scene. Then a NURBS model is fitted on each of the computed segments. The accuracy of the fitting is used as a measure of the plausibility that the segment represents a single surface or object. Segments that do not represent a single surface are split again into smaller regions and the process is iterated until the optimal segmentation is obtained. Experimental results show how the proposed method allows to obtain an accurate and reliable scene segmentation.
机译：场景分割是一个非常具有挑战性的问题，仅颜色信息通常不足以解决这一问题。最近，消费者深度相机的推出为利用深度数据的新颖方法开辟了道路。本文提出了一种新颖的分割方案，该方案利用了颜色和深度数据的联合使用以及3D表面估计方案。首先，从颜色和几何信息中构建一组多维矢量，然后对它们应用归一化的割谱聚类，以粗略地分割场景。然后，将NURBS模型拟合到每个计算的分段上。拟合的准确性用作该线段代表单个表面或对象的合理性的度量。不再将代表单个表面的线段再次拆分为较小的区域，然后重复该过程，直到获得最佳分割为止。实验结果表明，所提出的方法如何获得准确而可靠的场景分割。

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