Benchmarking Close-range Structure from Motion 3D Reconstruction Software Under Varying Capturing Conditions

Nikolov, Ivan Adriyanov; Madsen, Claus B.

doi:10.1007/978-3-319-48496-9_2

Cited by 52 publications

(37 citation statements)

References 15 publications

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“…Lighting and the background of the object to be rendered with SfM plays a significant role in the accuracy of the reproduction process [71,72], particularly the lack of features and low light (called "bad lighting" by Koutsoudis et al [71]). Considering that all images were acquired under canopy where limited light is present, we have chosen only spring and summer bright days, with almost no cloud coverage and no rain for three days.…”

Section: Discussionmentioning

confidence: 99%

Stem Measurements and Taper Modeling Using Photogrammetric Point Clouds

Fang

Strîmbu

2017

Remote Sensing

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Abstract:The estimation of tree biomass and the products that can be obtained from a tree stem have focused forest research for more than two centuries. Traditionally, measurements of the entire tree bole were expensive or inaccurate, even when sophisticated remote sensing techniques were used. We propose a fast and accurate procedure for measuring diameters along the merchantable portion of the stem at any given height. The procedure uses unreferenced photos captured with a consumer grade camera. A photogrammetric point cloud (PPC) is produced from the acquired images using structure from motion, which is a computer vision range imaging technique. A set of 18 loblolly pines (Pinus taeda Lindl.) from east Louisiana, USA, were photographed, subsequently cut, and the diameter measured every meter. The same diameters were measured on the point cloud with AutoCAD Civil3D. The ground point cloud reconstruction provided useful information for at most 13 m along the stem. The PPC measurements are biased, overestimating real diameters by 17.

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Section: Discussionmentioning

confidence: 99%

Stem Measurements and Taper Modeling Using Photogrammetric Point Clouds

Fang

Strîmbu

2017

Remote Sensing

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“…The accuracy of positioning the images captured under the canopy is irrelevant, because the main purpose is to ensure the nadir view of the point clouds. We think that tree matching is possible to be executed in real-time if SLAM algorithms [61][62][63] are used for 3D tree rendering instead of structure-from-motion algorithms [57,64,65]. Furthermore, accurate geo-referencing of tree stems could help improve species identification when analysis is executed data fusing spectral information with locational data [66][67][68].…”

Section: Discussionmentioning

confidence: 99%

Accurate Geo-Referencing of Trees with No or Inaccurate Terrestrial Location Devices

2019

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Accurate and precise location of trees from data acquired under-the-canopy is challenging and time-consuming. However, current forestry practices would benefit tremendously from the knowledge of tree coordinates, particularly when the information used to position them is acquired with inexpensive sensors. Therefore, the objective of our study is to geo-reference trees using point clouds created from the images acquired below canopy. We developed a procedure that uses the coordinates of the trees seen from above canopy to position the same trees seen below canopy. To geo-reference the trees from above canopy we captured images with an unmanned aerial vehicle. We reconstructed the trunk with photogrammetric point clouds built with a structure-from-motion procedure from images recorded in a circular pattern at multiple locations throughout the stand. We matched the trees segmented from below canopy with the trees extracted from above canopy using a non-rigid point-matching algorithm. To ensure accuracy, we reduced the number of matching trees by dividing the trees segmented from above using a grid with 50 m cells. Our procedure was implemented on a 7.1 ha Douglas-fir stand from Oregon USA. The proposed procedure is relatively fast, as approximately 600 trees were mapped in approximately 1 min. The procedure is sensitive to the point density, directly impacting tree location, as differences larger than 2 m between the coordinates of the tree top and the bottom part of the stem could lead to matching errors larger than 1 m. Furthermore, the larger the number of trees to be matched the higher the accuracy is, which could allow for misalignment errors larger than 2 m between the locations of the trees segmented from above and below.needed. Stand level information is particularly needed for intermediate cuts, when the decision is taken considering all the trees not only a portion of them. Although total height of each tree in a stand can be relatively easy estimated from point clouds, lidar or photogrammetric, there are at least two attributes that are difficult to estimate accurately and precisely for all trees: dbh and location. Many algorithms have been developed to estimate dbh from above canopy data (e.g., point clouds or images) with significant success [4][5][6][7]. However, while expected accuracy is in many instances achieved, the precision of estimating dbh from above canopy is limited, which restricts its operational usage. The effort placed in estimation of tree location mirror the one for tree size, but the success was clearly less impressive, as the accuracy is still measured in meters [8].Point clouds play a significant role in forest operations, particularly in road design and road maintenance [9,10]. Point clouds generated without an active sensor, such as lidar, or stereopsis are sometimes labeled photogrammetric point clouds [11] or phodar [12]. Photogrammetric point clouds (PPC) made inroads in harvesting, but they were employed similarly with aerial laser scanning, as the applications were based on t...

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“…286 pictures of 5760 x 3840 in RAW, radiometrically equalized and normalized were used to reconstruct the point cloud. Different software benchmarked in (Nikolov and Madsen, 2016) were tested and the final image-based point cloud reconstruction (IPC) was made using ContextCapture, for its higher 3D reconstruction's precision over the mosaic. Looking at the complexity our subject poses, a pipeline to correctly georeference the IPC is mandatory for further analysis.…”

Section: Point Cloud From Dense Image Matchingmentioning

confidence: 99%

Point Cloud Classification of Tesserae From Terrestrial Laser Data Combined With Dense Image Matching for Archaeological Information Extraction

Poux

Neuville

Billen

2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Reasoning from information extraction given by point cloud data mining allows contextual adaptation and fast decision making. However, to achieve this perceptive level, a point cloud must be semantically rich, retaining relevant information for the end user. This paper presents an automatic knowledge-based method for pre-processing multi-sensory data and classifying a hybrid point cloud from both terrestrial laser scanning and dense image matching. Using 18 features including sensor's biased data, each tessera in the highdensity point cloud from the 3D captured complex mosaics of Germigny-des-prés (France) is segmented via a colour multi-scale abstraction-based featuring extracting connectivity. A 2D surface and outline polygon of each tessera is generated by a RANSAC plane extraction and convex hull fitting. Knowledge is then used to classify every tesserae based on their size, surface, shape, material properties and their neighbour's class. The detection and semantic enrichment method shows promising results of 94% correct semantization, a first step toward the creation of an archaeological smart point cloud.

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Benchmarking Close-range Structure from Motion 3D Reconstruction Software Under Varying Capturing Conditions

Cited by 52 publications

References 15 publications

Stem Measurements and Taper Modeling Using Photogrammetric Point Clouds

Stem Measurements and Taper Modeling Using Photogrammetric Point Clouds

Accurate Geo-Referencing of Trees with No or Inaccurate Terrestrial Location Devices

Point Cloud Classification of Tesserae From Terrestrial Laser Data Combined With Dense Image Matching for Archaeological Information Extraction

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