从范围图像的拉链多边形网格

Greg Turk, M. Levoy
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引用次数: 1569

摘要

距离成像为三维物体的形状数字化提供了一种廉价而精确的方法。由于大多数物体都是自遮挡的,所以没有一张距离图像足以描述整个物体。我们提出了一种方法,将一组范围图像组合成一个多边形网格,该网格完全描述了从外部可见的物体。我们的方法的步骤是:1)使用改进的迭代最近点算法将网格彼此对齐;2)将相邻网格拼接在一起,形成一个正确捕获物体拓扑结构的连续曲面;3)计算所有网格上表面位置的局部加权平均值,形成一致的表面几何形状。我们的系统不同于以前的方法,因为它是增量的;每次获取和合并一个扫描。这种方法允许我们以最小的存储开销获取和组合大量的扫描。我们最大的模型包含多达360,000个三角形。使用我们的系统,只需5分钟的用户交互和几个小时的计算时间,就可以完成数字化对象所需的所有步骤,这些步骤需要多达10次范围扫描。我们展示了使用我们的方法创建的两个模型,这些模型使用了采用激光条纹技术的商用测距仪的距离数据。
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Zippered polygon meshes from range images
Range imaging offers an inexpensive and accurate means for digitizing the shape of three-dimensional objects. Because most objects self occlude, no single range image suffices to describe the entire object. We present a method for combining a collection of range images into a single polygonal mesh that completely describes an object to the extent that it is visible from the outside. The steps in our method are: 1) align the meshes with each other using a modified iterated closest-point algorithm, 2) zipper together adjacent meshes to form a continuous surface that correctly captures the topology of the object, and 3) compute local weighted averages of surface positions on all meshes to form a consensus surface geometry. Our system differs from previous approaches in that it is incremental; scans are acquired and combined one at a time. This approach allows us to acquire and combine large numbers of scans with minimal storage overhead. Our largest models contain up to 360,000 triangles. All the steps needed to digitize an object that requires up to 10 range scans can be performed using our system with five minutes of user interaction and a few hours of compute time. We show two models created using our method with range data from a commercial rangefinder that employs laser stripe technology.
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