概要

In this thesis, we consider the geometric and photometric modeling of large-scale and intricately shaped objects, such as cultural heritage objects. When modeling such objects, new issues occurred during the modeling steps which had not been considered in the previous research conducted on the modeling of small, indoor objects. Since the entire data of these objects cannot be obtained by a single observation, the main theme of this thesis is the “merging ” of multiple observations. The first issue is creating a detailed model from a huge amount of data. When modeling a large-scale and intricately shaped object, a huge amount of data is required to model the object. We would like to propose three approaches to handling this amount of data: the parallel processing of merging range images, the effective nearest neighbor search, and the adaptive algorithm of merging range images. We developed a parallel computation algorithm using a PC cluster which consists of two components, the distributed allocation of range images to multiple PCs, and the parallel traversal of subtrees of octree. We also propose a novel method for searching the nearest neighbor by extending the search algorithm using a k-d tree. We constructed a merged model in adaptive resolution according to