面向快速原型制造的海量数据模型重建(英文)

根据已有实物的测量数据进行模型重建 ,在机械产品逆向建模、计算机视觉、基于二维轮廓数据的生物外形重建等领域中具有重要应用价值。随着坐标测量设备的发展 ,获取包含被测物体更多细节的海量数据已非常方便 ,但大量的测量点却给模型重建带来了困难。本文首先提出了精度可控的海量数据自动简化算法。为了提高算法的效率 ,文中提出了一个数据集空间划分策略。根据简化后的数据集 ,应用步进立方体方法重建模型的三角网格曲面表示。由于种种原因 ,重建的三角网格模型常常含有不希望有的孔洞。为此 ,本文给出了一个算法产生形状优化的三角片以修补网格模型中的孔洞。经过孔洞修补 ,完全封闭的三角网格模型可以直接输出为快速原型制造中广泛应用的 STL文件。应用实例说明了本文的方法的可行性

MODEL RECONSTRUCTION FROM CLOUD DATA FOR RAPID PROTOTYPE MANUFACTURING

Model reconstruction from points scanned on existing physical objects is much important in a variety of situations such as reverse engineering for mechanical products, computer vision and recovery of biological shapes from two dimensional contours. With the development of measuring equipment, cloud points that contain more details of the object can be obtained conveniently. On the other hand, large quantity of sampled points brings difficulties to model reconstruction method. This paper first presents an algorithm to automatically reduce the number of cloud points under given tolerance. Triangle mesh surface from the simplified data set is reconstructed by the marching cubes algorithm. For various reasons, reconstructed mesh usually contains unwanted holes. An approach to create new triangles is proposed with optimized shape for covering the unexpected holes in triangle meshes. After hole filling, watertight triangle mesh can be directly output in STL format, which is widely used in rapid prototype manufacturing. Practical examples are included to demonstrate the method.