A Skeletal Camera Network for Close-range Images with a Data Driven Approach in Analyzing Stereo Configuration

Abstract

Structure-from-Motion (SfM) techniques have been widely used for 3D geometry reconstruction from multi-view images. Nevertheless, the efficiency and quality of the reconstructed geometry depends on multiple factors, i.e., the base-height ratio, intersection angle, overlap, and ground control points, etc., which are rarely quantified in real-world applications. To answer this question, in this paper, we take a data-driven approach by analyzing hundreds of terrestrial stereo image configurations through a typical SfM algorithm. Two main meta-parameters with respect to base-height ratio and intersection angle are analyzed. Following the results, we propose a Skeletal Camera Network (SCN) and embed it into the SfM to lead to a novel SfM scheme called SCN-SfM, which limits tie-point matching to the remaining connected image pairs in SCN. The proposed method was applied in three terrestrial datasets. Experimental results have demonstrated the effectiveness of the proposed SCN-SfM to achieve 3D geometry with higher accuracy and fast time efficiency compared to the typical SfM method, whereas the completeness of the geometry is comparable.

Keywords

• |3d geometry reconstruction|geometric factors|skeletal camera network|structure-from-motion|tie-point matching|terrestrial stereo images