Probst Alexandra, Gatziolis Demetrios, Strigul Nikolay
Department of Biology, University of Washington, Seattle, WA, USA.
USDA Forest Service, Pacific Northwest Research Station, Portland, OR, USA.
R Soc Open Sci. 2018 Jul 11;5(7):172192. doi: 10.1098/rsos.172192. eCollection 2018 Jul.
Photogrammetry-based three-dimensional reconstruction of objects is becoming increasingly appealing in research areas unrelated to computer vision. It has the potential to facilitate the assessment of forest inventory-related parameters by enabling or expediting resource measurements in the field. We hereby compare several implementations of photogrammetric algorithms (CMVS/PMVS, CMPMVS, MVE, OpenMVS, SURE and Agisoft PhotoScan) with respect to their performance in vegetation assessment. The evaluation is based on (i) a virtual scene where the precise location and dimensionality of objects is known and is thus conducive to a quantitative comparison and (ii) using series of acquired photographs of vegetation with overlapping field of view where the photogrammetric outcomes are compared qualitatively. Performance is quantified by computing receiver operating characteristic curves that summarize the type-I and type-II errors between the reference and reconstructed tree models. Similar artefacts are observed in synthetic- and -based reconstructions.
基于摄影测量的物体三维重建在与计算机视觉无关的研究领域中越来越有吸引力。它有可能通过在实地进行资源测量或加快测量速度,来促进与森林清查相关参数的评估。在此,我们比较了几种摄影测量算法(CMVS/PMVS、CMPMVS、MVE、OpenMVS、SURE和Agisoft PhotoScan)在植被评估方面的性能。评估基于:(i)一个虚拟场景,其中物体的精确位置和尺寸是已知的,因此有利于进行定量比较;(ii)使用一系列具有重叠视场的植被采集照片,对摄影测量结果进行定性比较。通过计算接收者操作特征曲线来量化性能,该曲线总结了参考树模型和重建树模型之间的I型和II型错误。在基于合成和基于的重建中观察到类似的伪像。