Aeronutronic Division, Ford Aerospace and Communications Corporation, Newport Beach, CA 92660.
IEEE Trans Pattern Anal Mach Intell. 1984 Mar;6(3):340-51. doi: 10.1109/tpami.1984.4767527.
A three-dimensional scene analysis system for the shape matching of real world 3-D objects is presented. Various issues related to representation and modeling of 3-D objects are addressed. A new method for the approximation of 3-D objects by a set of planar faces is discussed. The major advantage of this method is that it is applicable to a complete object and not restricted to single range view which was the limitation of the previous work in 3-D scene analysis. The method is a sequential region growing algorithm. It is not applied to range images, but rather to a set of 3-D points. The 3-D model of an object is obtained by combining the object points from a sequence of range data images corresponding to various views of the object, applying the necessary transformations and then approximating the surface by polygons. A stochastic labeling technique is used to do the shape matching of 3-D objects. The technique matches the faces of an unknown view against the faces of the model. It explicitly maximizes a criterion function based on the ambiguity and inconsistency of classification. It is hierarchical and uses results obtained at low levels to speed up and improve the accuracy of results at higher levels. The objective here is to match the individual views of the object taken from any vantage point. Details of the algorithm are presented and the results are shown on several unknown views of a complicated automobile casting.
提出了一种用于真实世界三维物体形状匹配的三维场景分析系统。讨论了与三维物体表示和建模相关的各种问题。讨论了一种通过一组平面来逼近三维物体的新方法。该方法的主要优点是它适用于完整的物体，而不受限于三维场景分析中之前工作的限制，即单个范围视图。该方法是一种顺序区域增长算法。它不适用于范围图像，而是适用于一组三维点。通过将来自对应于物体各个视图的一系列范围数据图像的物体点进行组合、应用必要的变换，然后用多边形来近似表面，从而获得物体的三维模型。使用随机标记技术来执行三维物体的形状匹配。该技术将未知视图的面与模型的面进行匹配。它基于分类的模糊性和不一致性显式最大化准则函数。它是分层的，并使用较低级别获得的结果来加速和提高较高级别结果的准确性。这里的目标是匹配从任意有利位置拍摄的物体的各个视图。介绍了算法的细节，并在复杂汽车铸件的几个未知视图上展示了结果。
