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      Intercomparison of photogrammetry software for three-dimensional vegetation modelling

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          Abstract

          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 a priori and is thus conducive to a quantitative comparison and (ii) using series of in situ 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 in situ-based reconstructions.

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          Most cited references11

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          Accurate, dense, and robust multiview stereopsis.

          This paper proposes a novel algorithm for multiview stereopsis that outputs a dense set of small rectangular patches covering the surfaces visible in the images. Stereopsis is implemented as a match, expand, and filter procedure, starting from a sparse set of matched keypoints, and repeatedly expanding these before using visibility constraints to filter away false matches. The keys to the performance of the proposed algorithm are effective techniques for enforcing local photometric consistency and global visibility constraints. Simple but effective methods are also proposed to turn the resulting patch model into a mesh which can be further refined by an algorithm that enforces both photometric consistency and regularization constraints. The proposed approach automatically detects and discards outliers and obstacles and does not require any initialization in the form of a visual hull, a bounding box, or valid depth ranges. We have tested our algorithm on various data sets including objects with fine surface details, deep concavities, and thin structures, outdoor scenes observed from a restricted set of viewpoints, and "crowded" scenes where moving obstacles appear in front of a static structure of interest. A quantitative evaluation on the Middlebury benchmark shows that the proposed method outperforms all others submitted so far for four out of the six data sets.
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            Modeling the World from Internet Photo Collections

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              Optimal Altitude, Overlap, and Weather Conditions for Computer Vision UAV Estimates of Forest Structure

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                Author and article information

                Journal
                R Soc Open Sci
                R Soc Open Sci
                RSOS
                royopensci
                Royal Society Open Science
                The Royal Society Publishing
                2054-5703
                July 2018
                11 July 2018
                11 July 2018
                : 5
                : 7
                : 172192
                Affiliations
                [1 ]Department of Biology, University of Washington , Seattle, WA, USA
                [2 ]USDA Forest Service, Pacific Northwest Research Station, Portland, OR, USA
                [3 ]Department of Mathematics and Statistics, Washington State University Vancouver , Vancouver, WA, USA
                Author notes
                Author for correspondence: Nikolay Strigul e-mail: nick.strigul@ 123456wsu.edu
                Author information
                http://orcid.org/0000-0001-5851-1469
                Article
                rsos172192
                10.1098/rsos.172192
                6083669
                d9225d8d-949c-4335-8059-8e5cd7b10a23
                © 2018 The Authors.

                Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original author and source are credited.

                History
                : 12 December 2017
                : 8 June 2018
                Funding
                Funded by: U.S. Forest Service, http://dx.doi.org/10.13039/100006959;
                Award ID: Evaluation of Visual Structure from Motion Technol
                Funded by: Simons Foundation, http://dx.doi.org/10.13039/100000893;
                Award ID: no. 283770
                Categories
                1003
                168
                1004
                69
                107
                Computer Science
                Research Article
                Custom metadata
                July, 2018

                remote sensing,photogrammetry,forest modelling,simulation,vegetation three-dimensionalre constructions,tree crown geometry

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