Local self-similarity descriptor for point-of-interest reconstruction of real-world scenes

Abstract

Scene reconstruction is utilized commonly in close-range photogrammetry, with diverse applications in fields such as industry, biology, and aerospace industries. Presented surfaces or wireframe three-dimensional (3D) model reconstruction applications are either too complex or too inflexible to accommodate various types of real-world scenes, however. This paper proposes an algorithm for acquiring point-of-interest (referred to throughout the study as POI) coordinates in 3D space, based on multi-view geometry and a local self-similarity descriptor. After reconstructing several POIs specified by a user, a concise and flexible target object measurement method, which obtains the distance between POIs, is described in detail. The proposed technique is able to measure targets with high accuracy even in the presence of obstacles and non-Lambertian surfaces. The method is so flexible that target objects can be measured with a handheld digital camera. Experimental results further demonstrate the effectiveness of the algorithm.