Skeletal camera network embedded structure-from-motion for 3D scene reconstruction from UAV images.

Zhihua Xu, Lixin Wu, Markus Gerke, Ran Wang, Huachao Yang

Research output: Contribution to journalArticleAcademicpeer-review

27 Citations (Scopus)

Abstract

Structure-from-Motion (SfM) techniques have been widely used for 3D scene reconstruction from multi-view images. However, due to the large computational costs of SfM methods there is a major challenge in processing highly overlapping images, e.g. images from unmanned aerial vehicles (UAV). This paper embeds a novel skeletal camera network (SCN) into SfM to enable efficient 3D scene reconstruction from a large set of UAV images. First, the flight control data are used within a weighted graph to construct a topologically connected camera network (TCN) to determine the spatial connections between UAV images. Second, the TCN is refined using a novel hierarchical degree bounded maximum spanning tree to generate a SCN, which contains a subset of edges from the TCN and ensures that each image is involved in at least a 3-view configuration. Third, the SCN is embedded into the SfM to produce a novel SCN-SfM method, which allows performing tie-point matching only for the actually connected image pairs. The proposed method was applied in three experiments with images from two fixed-wing UAVs and an octocopter UAV, respectively. In addition, the SCN-SfM method was compared to three other methods for image connectivity determination. The comparison shows a significant reduction in the number of matched images if our method is used, which leads to less computational costs. At the same time the achieved scene completeness and geometric accuracy are comparable.
Original languageEnglish
Pages (from-to)113-127
JournalISPRS journal of photogrammetry and remote sensing
Volume121
DOIs
Publication statusPublished - 2016

Fingerprint Dive into the research topics of 'Skeletal camera network embedded structure-from-motion for 3D scene reconstruction from UAV images.'. Together they form a unique fingerprint.

Cite this