通过快速多边形近似建模密集范围图像

H. Pedrini
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引用次数: 5

摘要

提出了一种结合三角形网格和曲率信息的距离图像逼近方法。首先,基于表面曲率估计对原始距离图像进行自适应滤波;这将生成一个3D点的集合,这些点将被三角化以生成初始网格。然后通过有效的Delaunay三角剖分算法对网格进行细化。采用一种新的局部误差度量来选择插入三角测量的点。点在平面区域趋向分散,在高变化区域趋向集中。该方法允许在变量精度级别上检索表示,从而提供了一种自然的多分辨率建模方法。实验结果表明,该方法可以有效地表示距离图像。
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Modeling dense range images through fast polygonal approximations
A method for approximating range images by integrating triangular meshes and curvature information is presented. First, an adaptive filtering technique is applied to the original range image based on estimations of the surface curvature. This produces a collection of 3D points, which are triangulated in order to produce an initial mesh. The mesh is then refined through an efficient Delaunay triangulation algorithm. A new local error measure is used to select points to be inserted into the triangulation. Points tend to scatter in planar areas and to concentrate in high variation areas. The method allows representations to be retrieved at variables levels of accuracy, providing a natural way of multiresolution modeling. Some experimental results are presented to show that the proposed technique is effective to represent range images.
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