An efficient method for organizing unordered images in 3D reconstruction

Modeling 3D objects from unordered image data sets is being researched for several years. In this paper we investigate the problem of inefficiency in organizing unordered image sets into clusters of related. With the increase in number of images, computation for pairwise matching becomes more expensive. Practically considerable algorithms are more focused on the organization of images. Instead of saving time from the process of arrangement of views, we emphasize on the time-consuming part "comparison". This process is ahead of establishment of the relationship among views which includes building of the spanning tree or skeletal graph. The proposed algorithm achieves similar results to that of pairwise matching algorithm. More importantly it computationally saves more time by the introduction of a requirement before pairwise comparison which reduces computation time for two-view matching. The method we present has been tested on image sets both on the Internet and shot by ourselves. And it has been observed that more widely separated views lead to much better results.