Genetic algorithms for free-form surface matching

The free-form surface matching problem is important in several practical applications, such as reverse engineering. An accurate, robust and fast solution is, therefore, of great significance. Recently genetic algorithms have attracted great interest for their ability to robustly solve hard optimization problems. In this work we investigate the performance of such an approach for finding the initial guess of the transformation, a translation and a rotation, between the object and the model surface. This is followed by a local gradient descent method, such as iterative closest point, to refine the estimate. Promising results are demonstrated on accurate real data.