We show non-invasive 3D plant disease imaging using automated monocular vision-based structure from motion. We optimize the number of key points in an image pair by using a small angular step size and detection in the...
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We show non-invasive 3D plant disease imaging using automated monocular vision-based structure from motion. We optimize the number of key points in an image pair by using a small angular step size and detection in the extra green channel. Furthermore, we upsample the images to increase the number of key points. With the same setup, we obtain functional fluorescence information that we map onto the 3D structural plant image, in this way obtaining a combined functional and 3D structural plant image using a single setup.
Airborne thermal cameras are a valuable source of information for energy analyses at city scale. The generation of accurate high-resolution thermal orthomosaics is a necessary but still challenging task, especially wh...
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Airborne thermal cameras are a valuable source of information for energy analyses at city scale. The generation of accurate high-resolution thermal orthomosaics is a necessary but still challenging task, especially when a thermal camera is the only imaging sensor on-board, because of the peculiar characteristics of thermal imagery (i.e. low dynamic range and poor detail definition), large geometric distortions induced by the optical system and weak acquisition geometry. This paper discusses potentials and limitations of structure from motion approach for the automated generation of thermal orthomosaics, with the aim to define the best practices and assess the achievable accuracy. After processing with different strategies two thermal flights over a 10 km(2) area in Bologna city (Italy), it can be concluded that the absolute planimetric accuracy can be in the order of 3-4 pixels and the best results are obtained when computing camera calibration on a smaller subset of images, with a limited number of ground control points and an adaptive fitting algorithm. The analysis of generated point clouds (compared with reference LiDAR data) and calibration reports, in addition to check point residuals, proved to be crucial for a proper accuracy assessment.
This paper presents an approach for structure from motion (SfM) for unorganized complex image sets. To achieve high accuracy and robustness, image triplets are employed and an (approximate) internal camera calibration...
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This paper presents an approach for structure from motion (SfM) for unorganized complex image sets. To achieve high accuracy and robustness, image triplets are employed and an (approximate) internal camera calibration is assumed to be known. The complexity of an image set is determined by the camera configurations which may include wide as well as weak baselines. Wide baselines occur for instance when terrestrial images and images from small Unmanned Aerial Systems (UAS) are combined. The resulting large (geometric/radiometric) distortions between images make image matching difficult possibly leading to an incomplete result. Weak baselines mean an insufficient distance between cameras compared to the distance of the observed scene and give rise to critical camera configurations. Inappropriate handling of such configurations may lead to various problems in triangulation-based SfM up to total failure. The focus of our approach lies on a complete linking of images even in case of wide or weak baselines. We do not rely on any additional information such as camera configurations, Global Positioning System (GPS) or an Inertial Navigation System (INS). As basis for generating suitable triplets to link the images, an iterative graph-based method is employed formulating image linking as the search for a terminal Steiner minimum tree in the line graph. SIFT (Lowe, 2004) descriptors are embedded into Hamming space for fast image similarity ranking. This is employed to limit the number of pairs to be geometrically verified by a computationally and more complex wide baseline matching method (Mayer et al., 2012). Critical camera configurations which are not suitable for geometric verification are detected by means of classification (Michelini and Mayer, 2019). Additionally, we propose a graph-based approach for the optimization of the hierarchical merging of triplets to efficiently generate larger image subsets. By this means, a complete, 3D reconstruction of the scene is obtaine
Accurate and up-to-date 3D maps, often represented as point clouds, are crucial for autonomous vehicles. Crowd-sourcing has emerged as a low-cost and scalable approach for collecting mapping data utilizing widely avai...
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Accurate and up-to-date 3D maps, often represented as point clouds, are crucial for autonomous vehicles. Crowd-sourcing has emerged as a low-cost and scalable approach for collecting mapping data utilizing widely available dashcams and other sensing devices. However, it is still a non-trivial task to utilize crowdsourced data, such as dashcam images and video, to efficiently create or update high-quality point clouds using technologies like structure from motion (SfM). This study assesses and compares different image matching options available in open-source SfM software, analyzing their applicability and limitations for mapping urban scenes in different practical scenarios. Furthermore, the study analyzes the impact of various camera setups (i.e., the number of cameras and their placement) and weather conditions on the quality of the generated 3D point clouds in terms of completeness and accuracy. Based on these analyses, our study provides guidelines for creating more accurate point clouds.
High-resolution topographic modeling has become more accessible due to the development of structure from motion (SfM)-image-matching algorithms in digital photogrammetry. Large archival databases of historical aerial ...
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High-resolution topographic modeling has become more accessible due to the development of structure from motion (SfM)-image-matching algorithms in digital photogrammetry. Large archival databases of historical aerial photographs are available in university, public, and government libraries, commonly as paper copies. The photographs can be in poor condition (i.e., deformed by humidity, scratched, or annotated). In addition, the negatives, as well as metadata, may be missing. Processing such photographs using classic stereo-photogrammetry is difficult and, in many instances, impossible. SfM can be applied to these photosets to access the valuable archive of geomorphic changes over the past century. In this paper, we illustrate the utility of the SfM technique using 568 digitized vertical aerial photographs of Mount Meager volcano, located in southwestern British Columbia, Canada. We use the aerial photographs, which span the period from 1947 to 2006, to track glaciers and glacier-landslide interactions on the volcano. Over this period, glaciers have thinned and retreated, interrupted by minor advances in the 1960s and 1970s. Landslides are frequent on the volcano and contribute to debris cover on the glaciers affecting the ablation process. SfM processing of the aerial photographs allowed us to unlock geomorphic information and reconstruct landscape change that would otherwise have been impossible. The results from SfM provide a visually effective way of presenting landscape change to a broad public audience, as a form of virtual geoheritage. The approach can thus be broadly applied in scientific and professional practices for improving land planning and hazard management.
Snowmelt from mountain forests is critically important for water resources and hydropower generation. More than 75% of surface water supply originates as snowmelt in mountainous regions, such as the western U.S. Remot...
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Snowmelt from mountain forests is critically important for water resources and hydropower generation. More than 75% of surface water supply originates as snowmelt in mountainous regions, such as the western U.S. Remote sensing has the potential to measure snowpack in these areas accurately. In this research, we combine light detection and ranging (lidar) from crewed aircraft (currently, the most reliable way of measuring snow depth in mountain forests) and structure from motion (SfM) remotely piloted aircraft systems (RPAS) for cost-effective multi-temporal monitoring of snowpack in mountain forests. In sparsely forested areas, both technologies give similar snow depth maps, with a comparable agreement with ground-based snow depth observations (RMSE similar to 10 cm). In densely forested areas, airborne lidar is better able to represent snow depth than RPAS-SfM (RMSE similar to 10 cm vs similar to 10-20 cm). In addition, we find the relationship between RPAS-SfM and previous lidar snow depth data can be used to estimate snow depth conditions outside of relatively small RPAS-SfM monitoring plots, with RMSE's between these observed and estimated snow depths on the order of 10-15 cm for the larger lidar coverages. This suggests that when a single airborne lidar snow survey exists, RPAS-SfM may provide useful multi-temporal snow monitoring that can estimate basin-scale snowpack, at a much lower cost than multiple airborne lidar surveys. Doing so requires a pre-existing mid-winter or peak-snowpack airborne lidar snow survey, and subsequent well-designed paired SfM and field snow surveys that accurately capture substantial snow depth variability.
In this paper, the structure from motion (SfM) problem is addressed using sequential Monte Carlo methods. A new SfM algorithm based on random sampling is derived to estimate the posterior distributions of camera motio...
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In this paper, the structure from motion (SfM) problem is addressed using sequential Monte Carlo methods. A new SfM algorithm based on random sampling is derived to estimate the posterior distributions of camera motion and scene structure for the perspective projection camera model. Experimental results show that challenging issues in solving the SfM problem, due to erroneous feature tracking, feature occlusion, motion/structure ambiguity, mixed-domain sequences, mismatched features, and independently moving objects, can be well modeled and effectively addressed using the proposed method.
We describe an algorithm for reconstructing three-dimensional structure and motion causally, in real time from monocular sequences of images. We prove that the algorithm is minimal and stable, in the sense that the es...
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We describe an algorithm for reconstructing three-dimensional structure and motion causally, in real time from monocular sequences of images. We prove that the algorithm is minimal and stable, in the sense that the estimation error remains bounded with probability one throughout a sequence of arbitrary length. We discuss a scheme for handling occlusions (point features appearing and disappearing) and drift in the scale factor. These issues are crucial for the algorithm to operate in real time on real scenes. We describe in detail the implementation of the algorithm, which runs on a personal computer and has been made available to the community. We report the performance of our implementation on a few representative long sequences of real and synthetic images. The algorithm, which has been tested extensively over the course of the past few years, exhibits honest performance when the scene contains at least 20-40 points with high contrast, when the relative motion is "slow" compared to the sampling frequency of the frame grabber (30Hz), and the lens aperture is "large enough" (typically more than 30degrees of visual field).
We present a novel simple formulation of the problem of 3D object reconstruction from images. In this formulation, the object is seen as lying at the intersection of the projection of orbits of custom built Lie group ...
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We present a novel simple formulation of the problem of 3D object reconstruction from images. In this formulation, the object is seen as lying at the intersection of the projection of orbits of custom built Lie group actions. The group parameters correspond to unknown irrelevant quantities such as the camera orientation, the depth parameters of the object with respect to the camera, and the focal length. We then use an algorithmic method based on moving frames a la Fels-Olver to obtain a fundamental set of invariants of these group actions. The invariants are used to de. ne a set of equations determining the 3D object, thus providing a mathematical formulation of the problem where the irrelevant parameters do not appear.
We present a novel structure from motion pipeline, which estimates motion and wiry 3D structurefrom imaged line segments across multiple views. Although the position and orientation of line segments can be determined...
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We present a novel structure from motion pipeline, which estimates motion and wiry 3D structurefrom imaged line segments across multiple views. Although the position and orientation of line segments can be determined more accurately than point features, the instability of their endpoints and the fact that lines are not constrained by epipolar geometry diverted most research focus away to point-based methods. In our approach, we tackle the problem of instable endpoints by utilizing relaxed constraints on their positions, both during matching and as well in the following bundle adjustment stage. Furthermore, we gain efficiency in estimating trifocal image relations by decoupling rotation and translation. To this end, a novel linear solver for relative translation estimation given rotations from five line correspondences in three views is introduced. Extensive experiments on long image sequences show that our line-based structure from motion pipeline advantageously complements point-based methods, giving more meaningful 3D representation for indoor scenarios.
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