To the Graduate Council: I am submitting herewith a dissertation ...

Chapter 2: Related Work 121. Start with an initial transformation R , ) .(0t02. For k = 1,...,kmaxor until stopping criteria met do:k −12.1. Compute si= Rk−1si+ tk−1.k −1k −1k −12.2. Build the correspondence set C = U{(si,arg min si− m )}.2.3. Using the pairs C compute the transformation that minimizes the sum ofsquared distances [Horn87].s ∈Sim∈MThe ICP algorithm was shown to converge monotonically to a local minimum.Therefore, the initial estimate of the transformation should be sufficiently close to thecorrect registration. Another limitation of the original version is that it requires largeoverlap between the datasets to be aligned. Step 2.3 in the algorithm is commonlysolved by one of the feature matching techniques presented in 2.1.1. Independently,Chen and Medioni [Chen92] developed an algorithm similar to ICP. Using orientationinformation, they devised a least squares matching metric based on the distance, in thedirection of the normal to a scene point, to the tangent plane at the model. Thisapproach allowed the incorporation of local shape information, as well as the handlingof partially overlapping datasets.Active research on the free-form registration problem gave a rise to a proliferation ofimproved ICP algorithms. Zhang [Zhang94] proposed a method based on robuststatistics, allowing for better handling of outliers, occlusions, and partial overlap. Thealgorithm used heuristics to remove inconsistent matches. While the ICP algorithmwas first used in the context of registration of clouds of points, Turk and Levoy[Turk94] devised a modified registration metric that dealt with polygon meshes. Theirtechnique is able to register partially overlapping views, by imposing mesh-basedconstraints on the selection of nearest points. In other improvements of ICP, Masuda