Relevant bibliographies by topics / Plant 3D reconstruction

Academic literature on the topic 'Plant 3D reconstruction'

Author: Grafiati
Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Plant 3D reconstruction"

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Ando, Ryuhei, Yuko Ozasa, and Wei Guo. "Robust Surface Reconstruction of Plant Leaves from 3D Point Clouds." Plant Phenomics 2021 (April 2, 2021): 1–15. http://dx.doi.org/10.34133/2021/3184185.

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The automation of plant phenotyping using 3D imaging techniques is indispensable. However, conventional methods for reconstructing the leaf surface from 3D point clouds have a trade-off between the accuracy of leaf surface reconstruction and the method’s robustness against noise and missing points. To mitigate this trade-off, we developed a leaf surface reconstruction method that reduces the effects of noise and missing points while maintaining surface reconstruction accuracy by capturing two components of the leaf (the shape and distortion of that shape) separately using leaf-specific properties. This separation simplifies leaf surface reconstruction compared with conventional methods while increasing the robustness against noise and missing points. To evaluate the proposed method, we reconstructed the leaf surfaces from 3D point clouds of leaves acquired from two crop species (soybean and sugar beet) and compared the results with those of conventional methods. The result showed that the proposed method robustly reconstructed the leaf surfaces, despite the noise and missing points for two different leaf shapes. To evaluate the stability of the leaf surface reconstructions, we also calculated the leaf surface areas for 14 consecutive days of the target leaves. The result derived from the proposed method showed less variation of values and fewer outliers compared with the conventional methods.
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Wang, Jizhang, Yun Zhang, and Rongrong Gu. "Research Status and Prospects on Plant Canopy Structure Measurement Using Visual Sensors Based on Three-Dimensional Reconstruction." Agriculture 10, no. 10 (October 8, 2020): 462. http://dx.doi.org/10.3390/agriculture10100462.

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Three-dimensional (3D) plant canopy structure analysis is an important part of plant phenotype studies. To promote the development of plant canopy structure measurement based on 3D reconstruction, we reviewed the latest research progress achieved using visual sensors to measure the 3D plant canopy structure from four aspects, including the principles of 3D plant measurement technologies, the corresponding instruments and specifications of different visual sensors, the methods of plant canopy structure extraction based on 3D reconstruction, and the conclusion and promise of plant canopy measurement technology. In the current research phase on 3D structural plant canopy measurement techniques, the leading algorithms of every step for plant canopy structure measurement based on 3D reconstruction are introduced. Finally, future prospects for a standard phenotypical analytical method, rapid reconstruction, and precision optimization are described.
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Yin, Kangxue, Hui Huang, Pinxin Long, Alexei Gaissinski, Minglun Gong, and Andrei Sharf. "Full 3D Plant Reconstruction via Intrusive Acquisition." Computer Graphics Forum 35, no. 1 (August 25, 2015): 272–84. http://dx.doi.org/10.1111/cgf.12724.

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Yang, Myongkyoon, and Seong-In Cho. "High-Resolution 3D Crop Reconstruction and Automatic Analysis of Phenotyping Index Using Machine Learning." Agriculture 11, no. 10 (October 15, 2021): 1010. http://dx.doi.org/10.3390/agriculture11101010.

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Beyond the use of 2D images, the analysis of 3D images is also necessary for analyzing the phenomics of crop plants. In this study, we configured a system and implemented an algorithm for the 3D image reconstruction of red pepper plant (Capsicum annuum L.), as well as its automatic analysis. A Kinect v2 with a depth sensor and a high-resolution RGB camera were used to obtain more accurate reconstructed 3D images. The reconstructed 3D images were compared with conventional reconstructed images, and the data of the reconstructed images were analyzed with respect to their directly measured features and accuracy, such as leaf number, width, and plant height. Several algorithms for image extraction and segmentation were applied for automatic analysis. The results showed that the proposed method showed an error of about 5 mm or less when reconstructing and analyzing 3D images, and was suitable for phenotypic analysis. The images and analysis algorithms obtained by the 3D reconstruction method are expected to be applied to various image processing studies.
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Sun, Guoxiang, and Xiaochan Wang. "Three-Dimensional Point Cloud Reconstruction and Morphology Measurement Method for Greenhouse Plants Based on the Kinect Sensor Self-Calibration." Agronomy 9, no. 10 (September 28, 2019): 596. http://dx.doi.org/10.3390/agronomy9100596.

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Plant morphological data are an important basis for precision agriculture and plant phenomics. The three-dimensional (3D) geometric shape of plants is complex, and the 3D morphology of a plant changes relatively significantly during the full growth cycle. In order to make high-throughput measurements of the 3D morphological data of greenhouse plants, it is necessary to frequently adjust the relative position between the sensor and the plant. Therefore, it is necessary to frequently adjust the Kinect sensor position and consequently recalibrate the Kinect sensor during the full growth cycle of the plant, which significantly increases the tedium of the multiview 3D point cloud reconstruction process. A high-throughput 3D rapid greenhouse plant point cloud reconstruction method based on autonomous Kinect v2 sensor position calibration is proposed for 3D phenotyping greenhouse plants. Two red–green–blue–depth (RGB-D) images of the turntable surface are acquired by the Kinect v2 sensor. The central point and normal vector of the axis of rotation of the turntable are calculated automatically. The coordinate systems of RGB-D images captured at various view angles are unified based on the central point and normal vector of the axis of the turntable to achieve coarse registration. Then, the iterative closest point algorithm is used to perform multiview point cloud precise registration, thereby achieving rapid 3D point cloud reconstruction of the greenhouse plant. The greenhouse tomato plants were selected as measurement objects in this study. Research results show that the proposed 3D point cloud reconstruction method was highly accurate and stable in performance, and can be used to reconstruct 3D point clouds for high-throughput plant phenotyping analysis and to extract the morphological parameters of plants.
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Hartley, Zane K. J., Aaron S. Jackson, Michael Pound, and Andrew P. French. "GANana: Unsupervised Domain Adaptation for Volumetric Regression of Fruit." Plant Phenomics 2021 (October 8, 2021): 1–11. http://dx.doi.org/10.34133/2021/9874597.

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3D reconstruction of fruit is important as a key component of fruit grading and an important part of many size estimation pipelines. Like many computer vision challenges, the 3D reconstruction task suffers from a lack of readily available training data in most domains, with methods typically depending on large datasets of high-quality image-model pairs. In this paper, we propose an unsupervised domain-adaptation approach to 3D reconstruction where labelled images only exist in our source synthetic domain, and training is supplemented with different unlabelled datasets from the target real domain. We approach the problem of 3D reconstruction using volumetric regression and produce a training set of 25,000 pairs of images and volumes using hand-crafted 3D models of bananas rendered in a 3D modelling environment (Blender). Each image is then enhanced by a GAN to more closely match the domain of photographs of real images by introducing a volumetric consistency loss, improving performance of 3D reconstruction on real images. Our solution harnesses the cost benefits of synthetic data while still maintaining good performance on real world images. We focus this work on the task of 3D banana reconstruction from a single image, representing a common task in plant phenotyping, but this approach is general and may be adapted to any 3D reconstruction task including other plant species and organs.
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Schunck, David, Federico Magistri, Radu Alexandru Rosu, André Cornelißen, Nived Chebrolu, Stefan Paulus, Jens Léon, et al. "Pheno4D: A spatio-temporal dataset of maize and tomato plant point clouds for phenotyping and advanced plant analysis." PLOS ONE 16, no. 8 (August 18, 2021): e0256340. http://dx.doi.org/10.1371/journal.pone.0256340.

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Understanding the growth and development of individual plants is of central importance in modern agriculture, crop breeding, and crop science. To this end, using 3D data for plant analysis has gained attention over the last years. High-resolution point clouds offer the potential to derive a variety of plant traits, such as plant height, biomass, as well as the number and size of relevant plant organs. Periodically scanning the plants even allows for performing spatio-temporal growth analysis. However, highly accurate 3D point clouds from plants recorded at different growth stages are rare, and acquiring this kind of data is costly. Besides, advanced plant analysis methods from machine learning require annotated training data and thus generate intense manual labor before being able to perform an analysis. To address these issues, we present with this dataset paper a multi-temporal dataset featuring high-resolution registered point clouds of maize and tomato plants, which we manually labeled for computer vision tasks, such as for instance segmentation and 3D reconstruction, providing approximately 260 million labeled 3D points. To highlight the usability of the data and to provide baselines for other researchers, we show a variety of applications ranging from point cloud segmentation to non-rigid registration and surface reconstruction. We believe that our dataset will help to develop new algorithms to advance the research for plant phenotyping, 3D reconstruction, non-rigid registration, and deep learning on raw point clouds. The dataset is freely accessible at https://www.ipb.uni-bonn.de/data/pheno4d/.
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Wang, Feiyi, Xiaodan Ma, Meng Liu, and Bingxue Wei. "Three-Dimensional Reconstruction of Soybean Canopy Based on Multivision Technology for Calculation of Phenotypic Traits." Agronomy 12, no. 3 (March 12, 2022): 692. http://dx.doi.org/10.3390/agronomy12030692.

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Precise reconstruction of the morphological structure of the soybean canopy and acquisition of plant traits have great theoretical significance and practical value for soybean variety selection, scientific cultivation, and fine management. Since it is difficult to obtain all-around information on living plants with traditional single or binocular machine vision, this paper proposes a three-dimensional (3D) method of reconstructing the soybean canopy for calculation of phenotypic traits based on multivision. First, a multivision acquisition system based on the Kinect sensor was constructed to obtain all-around point cloud data of soybean in three viewpoints, with different fertility stages of soybean as the research object. Second, conditional filtering and K-nearest neighbor filtering (KNN) algorithms were used to preprocess the raw 3D point cloud. The point clouds were matched and fused by the random sample consensus (RANSAC) and iterative closest point (ICP) algorithms to accomplish the 3D reconstruction of the soybean canopy. Finally, the plant height, leafstalk angle and crown width of soybean were calculated based on the 3D reconstruction of soybean canopy. The experimental results showed that the average deviations of the method was 2.84 cm, 4.0866° and 0.0213 m, respectively. The determination coefficients between the calculated values and measured values were 0.984, 0.9195 and 0.9235. The average deviation of the RANSAC + ICP was 0.0323, which was 0.0214 lower thanthe value calculated by the ICP algorithm. The results enable the precise 3D reconstruction of living soybean plants and quantitative detection for phenotypic traits.
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Fang, Wei, Hui Feng, Wanneng Yang, Lingfeng Duan, Guoxing Chen, Lizhong Xiong, and Qian Liu. "High-throughput volumetric reconstruction for 3D wheat plant architecture studies." Journal of Innovative Optical Health Sciences 09, no. 05 (July 18, 2016): 1650037. http://dx.doi.org/10.1142/s1793545816500371.

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For many tiller crops, the plant architecture (PA), including the plant fresh weight, plant height, number of tillers, tiller angle and stem diameter, significantly affects the grain yield. In this study, we propose a method based on volumetric reconstruction for high-throughput three-dimensional (3D) wheat PA studies. The proposed methodology involves plant volumetric reconstruction from multiple images, plant model processing and phenotypic parameter estimation and analysis. This study was performed on 80 Triticum aestivum plants, and the results were analyzed. Comparing the automated measurements with manual measurements, the mean absolute percentage error (MAPE) in the plant height and the plant fresh weight was 2.71% (1.08[Formula: see text]cm with an average plant height of 40.07[Formula: see text]cm) and 10.06% (1.41[Formula: see text]g with an average plant fresh weight of 14.06[Formula: see text]g), respectively. The root mean square error (RMSE) was 1.37[Formula: see text]cm and 1.79[Formula: see text]g for the plant height and plant fresh weight, respectively. The correlation coefficients were 0.95 and 0.96 for the plant height and plant fresh weight, respectively. Additionally, the proposed methodology, including plant reconstruction, model processing and trait extraction, required only approximately 20[Formula: see text]s on average per plant using parallel computing on a graphics processing unit (GPU), demonstrating that the methodology would be valuable for a high-throughput phenotyping platform.
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Zaman, Sharifa, and B. Fatima. "THREE-DIMENSIONAL RECONSTRUCTION AND VISUALIZATION OF PLANT CELLS." Journal of Mountain Area Research 5 (December 29, 2020): 28. http://dx.doi.org/10.53874/jmar.v5i0.80.

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The mechanical properties (like sensory texture etc.) of plants/fruits directly depend on their microstructures. Therefore, it is very important to well understand the geometry and topology of cells in order to control the microstructure for better mechanical response. In this research, techniques of digital image processing and segmentation in conjunction with mathematical morphology models are used to visualize and analyze the 3D cells of potato. ImageJ and MATLAB are used throughout in this study. The labeled image stacks are essential for studying quantitative characterization of 3D cells, MATLAB is used to label each image stacks. By using MATLAB 12420 cells were segmented within a short period of time and labeled each cell uniquely.
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Dissertations / Theses on the topic "Plant 3D reconstruction"

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Preuksakarn, Chakkrit. "Reconstructing plant architecture from 3D laser scanner data." Thesis, Montpellier 2, 2012. http://www.theses.fr/2012MON20116/document.

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Les modèles virtuels de plantes sont visuellement de plus en plus réalistes dans les applications infographiques. Cependant, dans le contexte de la biologie et l'agronomie, l'acquisition de modèles précis de plantes réelles reste un problème majeur pour la construction de modèles quantitatifs du développement des plantes.Récemment, des scanners laser 3D permettent d'acquérir des images 3D avec pour chaque pixel une profondeur correspondant à la distance entre le scanner et la surface de l'objet visé. Cependant, une plante est généralement un ensemble important de petites surfaces sur lesquelles les méthodes classiques de reconstruction échouent. Dans cette thèse, nous présentons une méthode pour reconstruire des modèles virtuels de plantes à partir de scans laser. Mesurer des plantes avec un scanner laser produit des données avec différents niveaux de précision. Les scans sont généralement denses sur la surface des branches principales mais recouvrent avec peu de points les branches fines. Le cœur de notre méthode est de créer itérativement un squelette de la structure de la plante en fonction de la densité locale de points. Pour cela, une méthode localement adaptative a été développée qui combine une phase de contraction et un algorithme de suivi de points.Nous présentons également une procédure d'évaluation quantitative pour comparer nos reconstructions avec des structures reconstruites par des experts de plantes réelles. Pour cela, nous explorons d'abord l'utilisation d'une distance d'édition entre arborescence. Finalement, nous formalisons la comparaison sous forme d'un problème d'assignation pour trouver le meilleur appariement entre deux structures et quantifier leurs différences
In the last decade, very realistic rendering of plant architectures have been produced in computer graphics applications. However, in the context of biology and agronomy, acquisition of accurate models of real plants is still a tedious task and a major bottleneck for the construction of quantitative models of plant development. Recently, 3D laser scanners made it possible to acquire 3D images on which each pixel has an associate depth corresponding to the distance between the scanner and the pinpointed surface of the object. Standard geometrical reconstructions fail on plants structures as they usually contain a complex set of discontinuous or branching surfaces distributed in space with varying orientations. In this thesis, we present a method for reconstructing virtual models of plants from laser scanning of real-world vegetation. Measuring plants with laser scanners produces data with different levels of precision. Points set are usually dense on the surface of the main branches, but only sparsely cover thin branches. The core of our method is to iteratively create the skeletal structure of the plant according to local density of point set. This is achieved thanks to a method that locally adapts to the levels of precision of the data by combining a contraction phase and a local point tracking algorithm. In addition, we present a quantitative evaluation procedure to compare our reconstructions against expertised structures of real plants. For this, we first explore the use of an edit distance between tree graphs. Alternatively, we formalize the comparison as an assignment problem to find the best matching between the two structures and quantify their differences
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Schöler, Florian [Verfasser]. "3D Reconstruction of Plant Architecture by Grammar-based Modeling and Markov Chain Sampling / Florian Schöler." Bonn : Universitäts- und Landesbibliothek Bonn, 2014. http://d-nb.info/1060787245/34.

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Bartoli, Adrien. "Reconstruction et alignement en vision 3D : points, droites, plans et caméras." Phd thesis, Grenoble INPG, 2003. http://tel.archives-ouvertes.fr/tel-00004360.

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Cette thèse concerne la reconstruction de modèles 3D de scènes à partir d'images prises par des caméras. Il est courant de reconstruire à partir de sous-ensembles d'images puis de fusionner les modèles partiels ainsi obtenus par une phase d'alignement 3D. Les algorithmes de reconstruction et d'alignement s'appuient sur des correspondances de points ou de droites entre les images. La localisation de ces points ou droites dans les images est affectée par un bruit de mesure, influençant la qualité des modèles 3D reconstruits. Cette thèse est centrée sur l'obtention de résultats optimaux et sur les problèmes de représentation qui en découlent. La première partie de cette thèse aborde le problème de la reconstruction de modèles 3D. Les cas des caméras calibrées et non calibrées sont traités. Nous développons des méthodes de reconstruction de points, de droites et de caméras. L'incorporation de contraintes géométriques de coplanarité permet la reconstruction conjointe de plans. Nos contributions principales sont le développement et la comparaison de méthodes permettant la reconstruction 3D optimale de points, droites, plans et caméras. La deuxième partie de cette thèse aborde le problème de l'alignement de modèles 3D, qui consiste à estimer la transformation géométrique liant deux modèles 3D. Les méthodes existantes sont basées sur des correspondances de points. Nous étudions le cas des correspondances de droites. Les cas des caméras calibrées et non calibrées sont traités. Nos contributions majeures dans ce domaine sont, d'un point de vue théorique, une étude des transformations géométriques de droites 3D. Plus précisément, nous étendons la représentation matricielle standard, adaptée aux points, en une représentation adaptée aux droites. D'un point de vue pratique, nous développons et comparons plusieurs méthodes d'alignement linéaires et non-linéaires. Nous proposons finalement des méthodes de reconstruction de modèles 3D lorsque la scène observée n'est pas rigide. Par ailleurs, nous développons un méthode de détection automatique de surfaces planes dans une modèle 3D.
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Karlsson, Anette. "In-Plane Motion Correction in Reconstruction of non-Cartesian 3D-functional MRI." Thesis, Linköpings universitet, Datorseende, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-72056.

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When patients move during an MRI examination, severe artifacts arise in the reconstructed image and motion correction is therefore often desired. An in-plane motion correction algorithm suitable for PRESTO-CAN, a new 3D functional MRI method where sampling of k-space is radial in kx-direction and kz-direction and Cartesian in ky-direction, was implemented in this thesis work. Rotation and translation movements can be estimated and corrected for sepa- rately since the magnitude of the data is only affected by the rotation. The data were sampled in a radial pattern and the rotation was estimated by finding the translation in angular direction using circular correlation. Correlation was also used when finding the translation in x-direction and z-direction. The motion correction algorithm was evaluated on computer simulated data, the motion was detected and corrected for, and this resulted in images with greatly reduced artifacts due to patient movements.
När patienter rör sig under en MRI-undersökning uppstår artefakter i den rekonstruerande bilden och därför är det önskvärt med rörelsekorrigering. En 2D- rörelsekorrigeringsalgoritm som är anpassad för PRESTO-CAN har tagits fram. PRESTO-CAN är en ny fMRI-metod för 3D där samplingen av k-rummet är radiell i (kx,kz)-planet och kartesisk i ky-riktningen. Rotations- och translationsrörelser kan estimeras separat då magnituden av signalen bara påverkas av rotationsrörelser. Eftersom data är samplat radiellt kan rotationen estimeras genom att hitta translationen i vinkelled med hjälp av cirkulär korrelation. Korrelation används även för att hitta translationen i i x- och z-riktningen. Test på simulerat data visar att rörelsekorrigeringsalgoritmen både detekterar och korrigerar för rörelser vilket leder till bilder med mycket mindre rörelseartefakter.
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Dosch, Philippe. "Un environnement pour la reconstruction 3D d'édifices à partir de plans d'architecte." Nancy 1, 2000. http://docnum.univ-lorraine.fr/public/SCD_T_2000_0066_DOSCH.pdf.

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Cette thèse s'inscrit dans le domaine de l'analyse de documents, et porte plus précisément sur la reconnaissance de graphiques. Notre objectif est d'obtenir un modèle 3D d'un édifice à partir de ses plans d'architecte. Pour cela, nous avons intégré des modules d'analyse et une interface homme-machine (IHM) permettant à l'operateur de contrôler les traitements à effectuer de manière optimale. La majeure partie de la these détaille les différents traitements mis en œuvre, du bas-niveau (sur des images de plans numérisés) jusqu'au haut-niveau (sur des données vectorisées). Nous décrivons les choix d'architecture logicielle qui nous ont menés à définir un système composé de trois couches hiérarchiques : une bibliothèque de composants logiciels, une couche applicative regroupant les différents outils de traitement d'images et de graphiques et l'IHM. Celle-ci permet d'interagir directement sur les données, de contrôler le déroulement de l'analyse et gérer le dialogue homme-machine. Dans ce travail d'équipe, nos contributions principales portent sur l'organisation spatiale des traitements (tuilage), l'extraction d'indices de niveau intermédiaire (lignes tiretées, symboles tels que les cages d'escalier), la mise en correspondance des étages pour la reconstruction 3D du bâtiment correspondant, l'intégration logicielle et la mise au point de tout l'aspect IHM
This thesis is in line with the field of document analysis, and more precisely deals with graphics recognition. Our purpose is the construction of a 3D model of a building from the architectural drawings of its f1oors. For that, we have a set of analysis modules and a graphical user interface (GUI) allowing a human operator to control the processings to be pelformed in an optimal way. The major part of this thesis describes the various processings implemented, from the low-level (bitmap images processings) to the high-Ievel (vectorized data processings). We describe the choices which have led us to define a threelayered software architecture, hierarchally organized: A library of software components, an applicative layer grouping the various processings together and the GUI. The latter allows to directly interact on data to control the the analysis, and manages the man-machine cooperation. All the members of our research teams have been involved in this work, but our main contributions concem the design of the GUI, the spatial organization of processings (tiling), the extraction of middle-level features (dashed and dotted lines, symbols such as stairwell, etc. ) and matching algorithms to construct the 3D structure of a building, as weil as the software integration and the design of the GUI
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Jonsson, Mikael. "Make it Flat : Detection and Correction of Planar Regions in Triangle Meshes." Thesis, Linköpings universitet, Datorseende, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-126589.

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The art of reconstructing a real-world scene digitally has been on the mind of researchers for decades. Recently, it has attracted more and more attention from companies seeing a chance to bring this kind of technology to the market. Digital reconstruction of buildings in particular is a niche that has both potential and room for improvement. With this background, this thesis will present the design and evaluation of a pipeline made to find and correct approximately flat surfaces in architectural scenes. The scenes are 3D-reconstructed triangle meshes based on RGB images. The thesis will also comprise an evaluation of a few different components available for doing this, leading to a choice of best components. The goal is to improve the visual quality of the reconstruction. The final pipeline is designed with two blocks - one to detect initial plane seeds and one to refine the detected planes. The first block makes use of a multi-label energy formulation on the graph that describes the reconstructed surface. Penalties are assigned to each vertex and each edge of the graph based on the vertex labels, effectively describing a Markov Random Field. The energy is minimized with the help of the alpha-expansion algorithm. The second block uses heuristics for growing the detected plane seeds, merging similar planes together and extracting deviating details. Results on several scenes are presented, showing that the visual quality has been improved while maintaining accuracy compared with ground truth data.
Konsten att digitalt rekonstruera en verklig miljö har länge varit intressant för forskare. Nyligen har området även tilldragit sig mer och mer uppmärksamhet från företag som ser en möjlighet att föra den här typen av teknik till produkter på marknaden. I synnerhet är digital rekonstruktion av byggnader en nisch som har både stor potential och möjlighet till förbättring. Med denna bakgrund så presenterar detta examensarbete designen för och utvärderingen av en pipeline som skapats för att detektera och rätta till approximativt platta regioner i arkitektoniska miljöer. Miljöerna är 3D-rekonstruerade triangelmeshar skapade från RGB-bilder. Examensarbetet omfattar även utvärdering av olika komponenter för att uppnå detta, som avslutas med att de mest lämpliga komponenterna presenteras. Målet i korthet är att förbättra den visuella kvaliteten av en rekonstruerad modell. Den slutgiltiga pipelinen består av två övergripande block - ett för att detektera initiala plan och ett för att förbättra de funna planen. Det första blocket använder en multi-label energiformulering på grafen som beskriver den rekonstruerade ytan. Straffvärden tilldelas varje vertex och varje båge i grafen baserade på varje vertex label. På så sätt beskriver grafen ett Markov Random Field. Energin är sedan minimerad med alpha-expansion-algoritmen. Det andra blocket använder heuristiker för att låta planen växa, slå ihop närliggande plan och för att extrahera avvikande detaljer. Resultat på flera miljöer presenteras också för att påvisa att den visuella kvaliteten har förbättrats utan att rekonstruktionens noggrannhet har försämrats jämfört med ground truth-data.
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Da, Costa Luis Eduardo. "Reconstruction de modèles 3D à partir d'information 2D partielle : application au cas d'une plante." Mémoire, École de technologie supérieure, 2007. http://espace.etsmtl.ca/573/1/DA_COSTA_Luis_Eduardo.pdf.

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Une méthode pour générer des modèles informatiques fidèles de plantes naturelles est proposée dans ce travail; cette méthode prend comme entrée des photographies 2D prises du champ. Le formalisme choisi comme base pour la représentation de plantes s'appelle «Systèmes de Lindenmayer» (LSystems), qui sont des systèmes grammaticaux contrôlés par une condition initale et une (ou plusieurs) règle(s) de réécriture Générer un modèle informatique d'une plante est l'équivalent à résoudre le problème inverse pour un sous-type de ce formalisme, appelé «LSystems à crochets»; ce travail utilise un algorithme évolutif pour résoudre ce problème inverse. Une description détaillée de l'algorithme, ainsi que la justification du design choisi, sont présentées; un ensemble d'expériences démontre que l'algorithme explore de manière satisfaisante l'espace de solutions candidates, et que les approximations qu'il propose sont adéquates dans la majorité des cas. Ses limitations et faiblesses sont aussi rapportées et ensuite discutées.
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Horna, Sébastien. "Reconstruction géométrique et topologique de complexes architecturaux 3D à partir de plans numériques 2D." Poitiers, 2008. http://theses.edel.univ-poitiers.fr/theses/2008/Horna-Sebastien/2008-Horna-Sebastien-These.pdf.

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L’intérieur des bâtiments est souvent modélisé en 3D pour diverses applications de modélisation ou de simulation. Par exemple, plusieurs méthodes permettent d’étudier l’éclairage, les transferts de chaleur, la propagation d’ondes. Ces applications nécessitent dans la plupart des cas une représentation volumique de l’environnement avec des relations d’adjacence et d’incidence entre les éléments. Malheureusement, les données correspondant au bâtiment sont en général seulement disponibles en 2D et les besoins des applications 3D varient d’une utilisation à l’autre. Pour résoudre ce problème, nous proposons une description formelle d’un ensemble de contraintes de cohérence dédiées à la modélisation d’intérieur de bâtiments. Dans cette thèse nous montrons comment cette représentation est utilisée pour : (i) reconstruire un modèle 3D à partir de plans d’architecte numériques 2D ; (ii) détecter automatiquement les incohérences géométriques, topologiques et sémantiques ; (iii) développer des opérations automatiques et semi-automatiques pour corriger les plans 2D. Toutes les contraintes de cohérence sont définies en 2D et 3D et reposent sur le modèle topologique des cartes généralisées. Ces opérations sont utilisées pour éditer les scènes 2D et 3D afin d’affiner ou de modifier les modèles. Enfin, nous expliquons comment ce modèle est utilisé pour une application de visualisation par lancé de rayons
Virtual architectural (indoor) scenes are often modelled in 3D for various types of simulation systems. For instance, some authors propose methods dedicated to lighting, heat transfer, acoustic or radio wave propagation simulations. These methods rely in most cases on a volumetric representation of the environment, with adjacency and incidence relationships. Unfortunately, many buildings data are only given by 2D plans and the 3D needs varies from one application to another. To solve these problems, we propose a formal representation of consistency constraints dedicated to building interiors and associated with a topological model. We show that such a representation can be used for : (i) reconstructing a 3D model from 2D architectural plans ; (ii) detecting automatically geometrical, topological and semantical inconsistencies ; (iii) designing automatic and semi-automatic operations to correct and enrich a 2D plan. All our constraints are homogeneously defined in 2D and 3D, implemented with generalized maps and used in modeling operations. We explain how this model can be successfully used with various ray-tracing methods
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Da, Costa Luis Eduardo. "Reconstruction de modèles 3D à partir d'information 2D partielle : application au cas d'une plante /." Thèse, Montréal : École de technologie supérieure, 2007. http://proquest.umi.com/pqdweb?did=1397920901&sid=3&Fmt=2&clientId=46962&RQT=309&VName=PQD.

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Thèse (Ph.D.) -- École de technologie supérieure, Montréal, 2007.
"Thèse présentée à l'École de technologie supérieure comme exigence partielle à l'obtention du doctorat en génie". CaQQUQ Bibliogr. : f. [173]-179. Également disponible en version électronique. CaQMUQET
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Bui, Cao Vu. "Modélisation d'environnements intérieurs par reconstruction 3D en temps réel et extraction de plans architecturaux 2D." Thesis, Troyes, 2018. http://www.theses.fr/2018TROY0032.

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Nous explorons le problème de la reconstruction complète d’un environnement intérieur en utilisant des données mixtes issues de la caméra RGB-D (couleur plus profondeur) de faible coût et de la centrale inertielle. Le processus de numérisation est réalisé en temps réel, en déplacement avec 6 degrés de liberté du système de numérisation. Nous nous concentrons sur les systèmes mobiles avec des contraintes informatiques, tels que les smartphones ou les tablettes. Les problèmes problématiques présentent un ensemble de défis fondamentaux : l'estimation du positionnement et de la trajectoire du périphérique lorsqu'il se déplace pendant l'acquisition de l'environnement et l'utilisation de structures de données légères pour stocker la représentation de la scène reconstruite. Le système doit être optimisé et efficace pour la mémoire, de sorte qu'il puisse fonctionner en temps réel, à bord de l'équipement mobile. Nous proposons également une nouvelle méthode de reconstruction de la surface, nommé Dodécaèdre Mapping, une solution de triangulation discrète pour la surface complète de l'environnement intérieur. L’algorithme tente d'approximer le maillage surfacique en déformant et en collant la surface affinée du dodécaèdre sur le nuage de points numérisé. Le dernier module de cette mission de recherche consiste à développer des outils de l'extraction automatique / semi-automatique de plans architecturaux 2D à partir de la reconstruction 3D de la scène scannée. Ce processus d'extraction est possible à partir du nuage de points 3D ou du maillage en définissant un plan de coupe
Scene reconstruction is the process of building an accurate geometric model of one's environment from We explore the problem of complete scene reconstruction in indoor environments using mixed - data from the low-cost RGB-D camera and the inertial unit. The scanning process is realized in real-time, on the move with 6DoF of the numerizing system. We focus on computationally-constrained mobile systems, such as smartphone or tablet devices. Problematic issues present a set of fundamental challenges - estimating the state and trajectory of the device as it moves while scanning environment and utilizing lightweight data structures to hold the representation of the reconstructed scene. The system needs to be computationally and memory-efficient, so that it can run in real time, on-board the mobile device. The point-cloud resulted in the above module, which is non-structured and noisy cause of the quality of the low-cost sensor, needed a new method for the surface reconstruction. Our Dodecahedron Mapping is presented like a triangulation solution for the completed indoor environment scanning. After filtering and smoothing the point cloud, the algorithm tries to approximating the surface mesh by deforming and pasting the dodecahedron surface to the scanned point cloud. And the last stage of this research mission is to developing tools for the automatic extraction of 2D architectural plans from the 3D scanned building scene. This extracting process is also possible from the 3D point cloud or mesh by defining a section plane
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Book chapters on the topic "Plant 3D reconstruction"

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Schöler, Florian, and Volker Steinhage. "Towards an Automated 3D Reconstruction of Plant Architecture." In Applications of Graph Transformations with Industrial Relevance, 51–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34176-2_6.

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Lou, Lu, Yonghuai Liu, Jiwan Han, and John H. Doonan. "Accurate Multi-View Stereo 3D Reconstruction for Cost-Effective Plant Phenotyping." In Lecture Notes in Computer Science, 349–56. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-11755-3_39.

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Guan, Xin, Jinpeng Wang, Yang Zhou, Kemo Jin, and Nianyu Zou. "Study on 3D Reconstruction of Plant Root Phenotype Based on X-CT Technique." In Green Energy and Networking, 182–92. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-62483-5_20.

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Livingston III, David P., and Tan Tuong. "3D Reconstruction of Frozen Plant Tissue: A Unique Histological Analysis to Image Postfreeze Responses." In Plant and Microbe Adaptations to Cold in a Changing World, 107–17. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8253-6_9.

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Holi, Pavitra, Seong Sill Park, Ashok Kumar Patil, G. Ajay Kumar, and Young Ho Chai. "Intelligent Reconstruction and Assembling of Pipeline from Point Cloud Data in Smart Plant 3D." In Lecture Notes in Computer Science, 360–70. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24078-7_36.

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Kanatani, Kenichi, Yasuyuki Sugaya, and Yasushi Kanazawa. "3D Reconstruction of a Plane." In Guide to 3D Vision Computation, 107–15. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-48493-8_8.

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Elibol, Armagan, Stefan Posch, Andreas Maurer, Klaus Pillen, and Birgit Möller. "Vision-Based 3D-Reconstruction of Barley Plants." In Pattern Recognition and Image Analysis, 406–15. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-38628-2_48.

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Pan, Zhigeng, Weixi Hu, Xinyu Guo, and Chunjiang Zhao. "An Efficient Image-Based 3D Reconstruction Algorithm for Plants." In Computational Science and Its Applications – ICCSA 2004, 751–60. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24709-8_79.

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Holzmann, Thomas, Michael Maurer, Friedrich Fraundorfer, and Horst Bischof. "Semantically Aware Urban 3D Reconstruction with Plane-Based Regularization." In Computer Vision – ECCV 2018, 487–503. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-01264-9_29.

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Toyooka, Kiminori, and Byung-Ho Kang. "Reconstructing Plant Cells in 3D by Serial Section Electron Tomography." In Methods in Molecular Biology, 159–70. Totowa, NJ: Humana Press, 2013. http://dx.doi.org/10.1007/978-1-62703-643-6_13.

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Conference papers on the topic "Plant 3D reconstruction"

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Lin, Chenhui, Hong Wang, Chengliang Liu, and Liang Gong. "3D reconstruction based plant-monitoring and plant-phenotyping platform." In 2020 3rd World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM). IEEE, 2020. http://dx.doi.org/10.1109/wcmeim52463.2020.00115.

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Gu, Xiaomeng, Lihong Xu, Dawei Li, and Peng Zhang. "3D reconstruction and visualization of plant leaves." In Sixth International Conference on Graphic and Image Processing (ICGIP 2014), edited by Yulin Wang, Xudong Jiang, and David Zhang. SPIE, 2015. http://dx.doi.org/10.1117/12.2179142.

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Ying Zheng, Steve Gu, Herbert Edelsbrunner, Carlo Tomasi, and Philip Benfey. "Detailed reconstruction of 3D plant root shape." In 2011 IEEE International Conference on Computer Vision (ICCV). IEEE, 2011. http://dx.doi.org/10.1109/iccv.2011.6126475.

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"3D RECONSTRUCTION OF PLANT ROOTS FROM MRI IMAGES." In International Conference on Computer Vision Theory and Applications. SciTePress - Science and and Technology Publications, 2012. http://dx.doi.org/10.5220/0003869800240033.

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"3D Reconstruction of Small Plant From Multiple Views." In 2014 ASABE Annual International Meeting. American Society of Agricultural and Biological Engineers, 2014. http://dx.doi.org/10.13031/aim.20141893190.

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Roussel, Johanna, Andreas Fischbach, Siegfried Jahnke, and Hanno Scharr. "3D Surface Reconstruction of Plant Seeds by Volume Carving." In Proceedings of the Computer Vision Problems in Plant Phenotyping Workshop 2015. British Machine Vision Association, 2015. http://dx.doi.org/10.5244/c.29.cvppp.7.

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Kumar, Pankaj, Jason Connor, and Stan Mikiavcic. "High-throughput 3D reconstruction of plant shoots for phenotyping." In 2014 13th International Conference on Control Automation Robotics & Vision (ICARCV). IEEE, 2014. http://dx.doi.org/10.1109/icarcv.2014.7064306.

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Zhu, Feiyu, Suresh Thapa, Tiao Gao, Yufeng Ge, Harkamal Walia, and Hongfeng Yu. "3D Reconstruction of Plant Leaves for High-Throughput Phenotyping." In 2018 IEEE International Conference on Big Data (Big Data). IEEE, 2018. http://dx.doi.org/10.1109/bigdata.2018.8622428.

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"Processing of serial microscopic images for 3D reconstruction of plant tissues." In Bioinformatics of Genome Regulation and Structure/Systems Biology (BGRS/SB-2022) :. Institute of Cytology and Genetics, the Siberian Branch of the Russian Academy of Sciences, 2022. http://dx.doi.org/10.18699/sbb-2022-375.

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Cerutti, Guillaume, Sophie Ribes, Christophe Godin, Carlos Galvan-Ampudia, and Teva Vernoux. "3-d Tessellation of Plant Tissue - A Dual Optimization Approach to Cell-Level Meristem Reconstruction from Microscopy Images." In 2015 International Conference on 3D Vision (3DV). IEEE, 2015. http://dx.doi.org/10.1109/3dv.2015.57.

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