Low-Overlap Range Image Registration for Archaeological Applications

Abstract

In digital Archaeology, the 3D modeling of physical objects from range views is an important issue. Generally, the applications demand a great number of views to create a precise 3D model through a registration process. Most range image registration techniques are based on variants of the ICP (Iterative Closest Point) algorithm. The ICP algorithm has two main drawbacks: the possibility of convergence to a local minimum, and the need to prealign the images. Genetic Algorithms (GAs) were recently applied to range image registration providing good convergence results without the constraints observed in the ICP approaches. To improve range image registration, we explore the use of GAs and develop a novel approach that combines a GA with hillclimbing heuristics (GH). The experimental results show that our method is effective in aligning low overlap views and yield more accurate registration results than either ICP or standard GA approaches. Our method is highly advantageous in archaeological applications, where it is necessary to reduce the number of views to be aligned because data acquisition is expensive and also to minimize error accumulation in the 3D model. We also present a new measure of surface interpenetration with which to evaluate the registration and prove its utility with experimental results.