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

作　　者：Zhihua XU Lingling QU 

机构地区：[1]College of Geoscience and Surveying Engineering,China University of Mining and Technology(Beijing),Beijing 100083,China [2]State Key Laboratory of Coal Resources and Safe Mining,China University of Mining and Technology(Beijing),Beijing 100083,China

出　　处：《Journal of Geodesy and Geoinformation Science》2022年第4期23-37,共15页测绘学报（英文版）

基　　金：National Natural Science Foundation of China(No.41701534);Open Fund of State Key Laboratory of Coal Resources and Safe Mining(No.SKLCRSM19KFA01);Ecological and Smart Mine Joint Foundation of Hebei Province(No.E2020402086);State Key Laboratory ofGeohazard Prevention and Geoenvironment Protection(No.SKLGP2019K015)

摘　　要：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.

关 键 词：3D geometry reconstruction geometric factors skeletal camera network STRUCTURE-FROM-MOTION tie-point matching terrestrial stereo images 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]