Straightforward reconstruction of 3D surfaces and topography with a camera: Accuracy and geoscience application
Topographic measurements for detailed studies of processes such as erosion or mass movement are usually acquired by expensive laser scanners or rigorous photogrammetry. Here, we test and use an alternative technique based on freely available computer vision software which allows general geoscientist...
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Published in | Journal of Geophysical Research: Earth Surface Vol. 117; no. F3 |
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Main Authors | , |
Format | Journal Article |
Language | English |
Published |
Washington, DC
Blackwell Publishing Ltd
01.09.2012
American Geophysical Union |
Subjects | |
Online Access | Get full text |
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Abstract | Topographic measurements for detailed studies of processes such as erosion or mass movement are usually acquired by expensive laser scanners or rigorous photogrammetry. Here, we test and use an alternative technique based on freely available computer vision software which allows general geoscientists to easily create accurate 3D models from field photographs taken with a consumer‐grade camera. The approach integrates structure‐from‐motion (SfM) and multiview‐stereo (MVS) algorithms and, in contrast to traditional photogrammetry techniques, it requires little expertise and few control measurements, and processing is automated. To assess the precision of the results, we compare SfM‐MVS models spanning spatial scales of centimeters (a hand sample) to kilometers (the summit craters of Piton de la Fournaise volcano) with data acquired from laser scanning and formal close‐range photogrammetry. The relative precision ratio achieved by SfM‐MVS (measurement precision: observation distance) is limited by the straightforward camera calibration model used in the software, but generally exceeds 1:1000 (i.e., centimeter‐level precision over measurement distances of 10 s of meters). We apply SfM‐MVS at an intermediate scale, to determine erosion rates along a ∼50‐m‐long coastal cliff. Seven surveys carried out over a year indicate an average retreat rate of 0.70 ± 0.05 m a−1. Sequential erosion maps (at ∼0.05 m grid resolution) highlight the spatiotemporal variability in the retreat, with semivariogram analysis indicating a correlation between volume loss and length scale. Compared with a laser scanner survey of the same site, SfM‐MVS produced comparable data and reduced data collection time by ∼80%.
Key Points
Computer vision techniques can be used to derive DEMs from photographs
Surface models of coastal cliffs permit geostatistical analysis of erosion
Model precision ratios generally exceed 1:1000 thus are useful in geosciences |
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AbstractList | Topographic measurements for detailed studies of processes such as erosion or mass movement are usually acquired by expensive laser scanners or rigorous photogrammetry. Here, we test and use an alternative technique based on freely available computer vision software which allows general geoscientists to easily create accurate 3D models from field photographs taken with a consumer‐grade camera. The approach integrates structure‐from‐motion (SfM) and multiview‐stereo (MVS) algorithms and, in contrast to traditional photogrammetry techniques, it requires little expertise and few control measurements, and processing is automated. To assess the precision of the results, we compare SfM‐MVS models spanning spatial scales of centimeters (a hand sample) to kilometers (the summit craters of Piton de la Fournaise volcano) with data acquired from laser scanning and formal close‐range photogrammetry. The relative precision ratio achieved by SfM‐MVS (measurement precision: observation distance) is limited by the straightforward camera calibration model used in the software, but generally exceeds 1:1000 (i.e., centimeter‐level precision over measurement distances of 10 s of meters). We apply SfM‐MVS at an intermediate scale, to determine erosion rates along a ∼50‐m‐long coastal cliff. Seven surveys carried out over a year indicate an average retreat rate of 0.70 ± 0.05 m a−1. Sequential erosion maps (at ∼0.05 m grid resolution) highlight the spatiotemporal variability in the retreat, with semivariogram analysis indicating a correlation between volume loss and length scale. Compared with a laser scanner survey of the same site, SfM‐MVS produced comparable data and reduced data collection time by ∼80%.
Key Points
Computer vision techniques can be used to derive DEMs from photographs
Surface models of coastal cliffs permit geostatistical analysis of erosion
Model precision ratios generally exceed 1:1000 thus are useful in geosciences Topographic measurements for detailed studies of processes such as erosion or mass movement are usually acquired by expensive laser scanners or rigorous photogrammetry. Here, we test and use an alternative technique based on freely available computer vision software which allows general geoscientists to easily create accurate 3D models from field photographs taken with a consumer-grade camera. The approach integrates structure-from-motion (SfM) and multiview-stereo (MVS) algorithms and, in contrast to traditional photogrammetry techniques, it requires little expertise and few control measurements, and processing is automated. To assess the precision of the results, we compare SfM-MVS models spanning spatial scales of centimeters (a hand sample) to kilometers (the summit craters of Piton de la Fournaise volcano) with data acquired from laser scanning and formal close-range photogrammetry. The relative precision ratio achieved by SfM-MVS (measurement precision: observation distance) is limited by the straightforward camera calibration model used in the software, but generally exceeds 1:1000 (i.e., centimeter-level precision over measurement distances of 10 s of meters). We apply SfM-MVS at an intermediate scale, to determine erosion rates along a 50-m-long coastal cliff. Seven surveys carried out over a year indicate an average retreat rate of 0.70 ± 0.05 m a-1. Sequential erosion maps (at 0.05 m grid resolution) highlight the spatiotemporal variability in the retreat, with semivariogram analysis indicating a correlation between volume loss and length scale. Compared with a laser scanner survey of the same site, SfM-MVS produced comparable data and reduced data collection time by 80%. Key Points Computer vision techniques can be used to derive DEMs from photographs Surface models of coastal cliffs permit geostatistical analysis of erosion Model precision ratios generally exceed 1:1000 thus are useful in geosciences Topographic measurements for detailed studies of processes such as erosion or mass movement are usually acquired by expensive laser scanners or rigorous photogrammetry. Here, we test and use an alternative technique based on freely available computer vision software which allows general geoscientists to easily create accurate 3D models from field photographs taken with a consumer‐grade camera. The approach integrates structure‐from‐motion (SfM) and multiview‐stereo (MVS) algorithms and, in contrast to traditional photogrammetry techniques, it requires little expertise and few control measurements, and processing is automated. To assess the precision of the results, we compare SfM‐MVS models spanning spatial scales of centimeters (a hand sample) to kilometers (the summit craters of Piton de la Fournaise volcano) with data acquired from laser scanning and formal close‐range photogrammetry. The relative precision ratio achieved by SfM‐MVS (measurement precision: observation distance) is limited by the straightforward camera calibration model used in the software, but generally exceeds 1:1000 (i.e., centimeter‐level precision over measurement distances of 10 s of meters). We apply SfM‐MVS at an intermediate scale, to determine erosion rates along a ∼50‐m‐long coastal cliff. Seven surveys carried out over a year indicate an average retreat rate of 0.70 ± 0.05 m a −1 . Sequential erosion maps (at ∼0.05 m grid resolution) highlight the spatiotemporal variability in the retreat, with semivariogram analysis indicating a correlation between volume loss and length scale. Compared with a laser scanner survey of the same site, SfM‐MVS produced comparable data and reduced data collection time by ∼80%. Computer vision techniques can be used to derive DEMs from photographs Surface models of coastal cliffs permit geostatistical analysis of erosion Model precision ratios generally exceed 1:1000 thus are useful in geosciences Topographic measurements for detailed studies of processes such as erosion or mass movement are usually acquired by expensive laser scanners or rigorous photogrammetry. Here, we test and use an alternative technique based on freely available computer vision software which allows general geoscientists to easily create accurate 3D models from field photographs taken with a consumer-grade camera. The approach integrates structure-from-motion (SfM) and multiview-stereo (MVS) algorithms and, in contrast to traditional photogrammetry techniques, it requires little expertise and few control measurements, and processing is automated. To assess the precision of the results, we compare SfM-MVS models spanning spatial scales of centimeters (a hand sample) to kilometers (the summit craters of Piton de la Fournaise volcano) with data acquired from laser scanning and formal close-range photogrammetry. The relative precision ratio achieved by SfM-MVS (measurement precision: observation distance) is limited by the straightforward camera calibration model used in the software, but generally exceeds 1:1000 (i.e., centimeter-level precision over measurement distances of 10 s of meters). We apply SfM-MVS at an intermediate scale, to determine erosion rates along a 50-m-long coastal cliff. Seven surveys carried out over a year indicate an average retreat rate of 0.70 ± 0.05 m a1. Sequential erosion maps (at 0.05 m grid resolution) highlight the spatiotemporal variability in the retreat, with semivariogram analysis indicating a correlation between volume loss and length scale. Compared with a laser scanner survey of the same site, SfM-MVS produced comparable data and reduced data collection time by 80%. |
Author | Robson, S. James, M. R. |
Author_xml | – sequence: 1 givenname: M. R. surname: James fullname: James, M. R. email: m.james@lancaster.ac.uk, m.james@lancaster.ac.uk organization: Lancaster Environment Centre, Lancaster University, Lancaster, UK – sequence: 2 givenname: S. surname: Robson fullname: Robson, S. organization: Department of Civil, Environmental and Geomatic Engineering, UCL, London, UK |
BackLink | http://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=26507120$$DView record in Pascal Francis |
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Keywords | algorithms cliffs mass movements laser methods maps software photogrammetry topography accuracy cartography erosion rates coastal zone correlation collections Photogrammetric survey semivariograms three-dimensional models spatiotemporal variations erosion craters calibration computers |
Language | English |
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PublicationTitle | Journal of Geophysical Research: Earth Surface |
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SubjectTerms | Bundler Cameras coastal erosion Data collection Data reduction DEM Earth science Earth sciences Earth, ocean, space Erosion rates Exact sciences and technology Geology Geomorphology Hydrology Photogrammetry Scientific apparatus & instruments Soil erosion structure from motion surface model Volcanoes |
Title | Straightforward reconstruction of 3D surfaces and topography with a camera: Accuracy and geoscience application |
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