EVALUATING DENSE 3D RECONSTRUCTION SOFTWARE PACKAGES FOR OBLIQUE MONITORING OF CROP CANOPY SURFACE

2016 ◽

Vol XLI-B5 ◽

pp. 785-789

Author(s):

S. Brocks ◽

G. Bareth

Keyword(s):

3D Reconstruction ◽

Field Experiment ◽

Point Clouds ◽

Crop Growth ◽

Multiple Time ◽

Smart Cameras ◽

Growth Variability ◽

Growing Period ◽

Software Packages ◽

Dense 3D Reconstruction

Crop Surface Models (CSMs) are 2.5D raster surfaces representing absolute plant canopy height. Using multiple CMSs generated from data acquired at multiple time steps, a crop surface monitoring is enabled. This makes it possible to monitor crop growth over time and can be used for monitoring in-field crop growth variability which is useful in the context of high-throughput phenotyping. This study aims to evaluate several software packages for dense 3D reconstruction from multiple overlapping RGB images on field and plot-scale. A summer barley field experiment located at the Campus Klein-Altendorf of University of Bonn was observed by acquiring stereo images from an oblique angle using consumer-grade smart cameras. Two such cameras were mounted at an elevation of 10 m and acquired images for a period of two months during the growing period of 2014. The field experiment consisted of nine barley cultivars that were cultivated in multiple repetitions and nitrogen treatments. Manual plant height measurements were carried out at four dates during the observation period. The software packages Agisoft PhotoScan, VisualSfM with CMVS/PMVS2 and SURE are investigated. The point clouds are georeferenced through a set of ground control points. Where adequate results are reached, a statistical analysis is performed.

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Evaluating the potential of consumer-grade smart cameras for low-cost stereo-photogrammetric Crop-Surface Monitoring

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xl-7-43-2014 ◽

2014 ◽

Vol XL-7 ◽

pp. 43-49 ◽

Cited By ~ 2

Author(s):

S. Brocks ◽

G. Bareth

Keyword(s):

Plant Height ◽

Precision Agriculture ◽

Low Cost ◽

Image Acquisition ◽

Coefficient Of Determination ◽

Average Height ◽

Smart Cameras ◽

Growth Variability ◽

Growing Period ◽

Surface Models

Crop-Surface-Models (CSMs) are a useful tool for monitoring in-field crop growth variability, thus enabling precision agriculture which is necessary for achieving higher agricultural yields. This contribution provides a first assessment on the suitability of using consumer-grade smart cameras as sensors for the stereoscopic creation of crop-surface models using oblique imagery acquired from ground-based positions. An application that automates image acquisition and transmission was developed. Automated image acquisition took place throughout the growing period of barley in 2013. For three dates where both automated image acquisition and manual measurements of plant height were available, CSMs were generated using a combination of AgiSoft PhotoScan and Esri ArcGIS. The coefficient of determination R2 between the average of the manually measured plant heights per plots and the average height of the developed crop surface models was 0.61 (n = 24). The overall correlation between the manually measured heights and the CSM-derived heights is 0.78. The average per plot of the manually measured plant heights in the timeframe covered by the generated CSMs range from 19 to 95 cm, while the average plant height per plot of the generated CSMs range from 2.1 to 69 cm. These first results show that the presented approach is feasible.

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Rectification Based Single-Shot Structured Light for Accurate and Dense 3D Reconstruction

Journal of Computational Vision and Imaging Systems ◽

10.15353/jcvis.v6i1.3561 ◽

2021 ◽

Vol 6 (1) ◽

pp. 1-3

Author(s):

Sina Farsangi ◽

Mohamed A. Naiel ◽

Mark Lamm ◽

Paul Fieguth

Keyword(s):

3D Reconstruction ◽

Structured Light ◽

Target Surface ◽

Point Clouds ◽

Single Shot ◽

Camera Systems ◽

3D Point Clouds ◽

Random Arrays ◽

Dense 3D Reconstruction

Structured Light (SL) patterns generated based on pseudo-random arrays are widely used for single-shot 3D reconstruction using projector-camera systems. These SL images consist of a set of tags with different appearances, where these patterns will be projected on a target surface, then captured by a camera and decoded. The precision of localizing these tags from captured camera images affects the quality of the pixel-correspondences between the projector and the camera, and consequently that of the derived 3D shape. In this paper, we incorporate a quadrilateral representation for the detected SL tags that allows the construction of robust and accurate pixel-correspondences and the application of a spatial rectification module that leads to high tag classification accuracy. When applying the proposed method to single-shot 3D reconstruction, we show the effectiveness of this method over a baseline in estimating denser and more accurate 3D point-clouds.

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MULTI-VIEW STEREO WITH SEMANTIC PRIORS

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-2-w15-1135-2019 ◽

2019 ◽

Vol XLII-2/W15 ◽

pp. 1135-1140 ◽

Cited By ~ 2

Author(s):

E.-K. Stathopoulou ◽

F. Remondino

Keyword(s):

3D Reconstruction ◽

Large Scale ◽

Depth Map ◽

Point Clouds ◽

Stereo Pair ◽

Semantic Constraints ◽

Depth Maps ◽

Semantic Coherence ◽

3D Scene ◽

Dense 3D Reconstruction

Abstract. Patch-based stereo is nowadays a commonly used image-based technique for dense 3D reconstruction in large scale multi-view applications. The typical steps of such a pipeline can be summarized in stereo pair selection, depth map computation, depth map refinement and, finally, fusion in order to generate a complete and accurate representation of the scene in 3D. In this study, we aim to support the standard dense 3D reconstruction of scenes as implemented in the open source library OpenMVS by using semantic priors. To this end, during the depth map fusion step, along with the depth consistency check between depth maps of neighbouring views referring to the same part of the 3D scene, we impose extra semantic constraints in order to remove possible errors and selectively obtain segmented point clouds per label, boosting automation towards this direction. In order to reassure semantic coherence between neighbouring views, additional semantic criterions can be considered, aiming to eliminate mismatches of pixels belonging in different classes.

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