Academic literature on the topic '2D/3D object discovery'
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Journal articles on the topic "2D/3D object discovery"
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Mahmud, Bahar Uddin, Guan Yue Hong, Abdullah Al Mamun, Em Poh Ping, and Qingliu Wu. "Deep Learning-Based Segmentation of 3D Volumetric Image and Microstructural Analysis." Sensors 23, no. 5 (February 27, 2023): 2640. http://dx.doi.org/10.3390/s23052640.
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As a fundamental but difficult topic in computer vision, 3D object segmentation has various applications in medical image analysis, autonomous vehicles, robotics, virtual reality, lithium battery image analysis, etc. In the past, 3D segmentation was performed using hand-made features and design techniques, but these techniques could not generalize to vast amounts of data or reach acceptable accuracy. Deep learning techniques have lately emerged as the preferred method for 3D segmentation jobs as a result of their extraordinary performance in 2D computer vision. Our proposed method used a CNN-based architecture called 3D UNET, which is inspired by the famous 2D UNET that has been used to segment volumetric image data. To see the internal changes of composite materials, for instance, in a lithium battery image, it is necessary to see the flow of different materials and follow the directions analyzing the inside properties. In this paper, a combination of 3D UNET and VGG19 has been used to conduct a multiclass segmentation of publicly available sandstone datasets to analyze their microstructures using image data based on four different objects in the samples of volumetric data. In our image sample, there are a total of 448 2D images, which are then aggregated as one 3D volume to examine the 3D volumetric data. The solution involves the segmentation of each object in the volume data and further analysis of each object to find its average size, area percentage, total area, etc. The open-source image processing package IMAGEJ is used for further analysis of individual particles. In this study, it was demonstrated that convolutional neural networks can be trained to recognize sandstone microstructure traits with an accuracy of 96.78% and an IOU of 91.12%. According to our knowledge, many prior works have applied 3D UNET for segmentation, but very few papers extend it further to show the details of particles in the sample. The proposed solution offers a computational insight for real-time implementation and is discovered to be superior to the current state-of-the-art methods. The result has importance for the creation of an approximately similar model for the microstructural analysis of volumetric data.
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Atick, Joseph J., Paul A. Griffin, and A. Norman Redlich. "Statistical Approach to Shape from Shading: Reconstruction of Three-Dimensional Face Surfaces from Single Two-Dimensional Images." Neural Computation 8, no. 6 (August 1996): 1321–40. http://dx.doi.org/10.1162/neco.1996.8.6.1321.
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The human visual system is proficient in perceiving three-dimensional shape from the shading patterns in a two-dimensional image. How it does this is not well understood and continues to be a question of fundamental and practical interest. In this paper we present a new quantitative approach to shape-from-shading that may provide some answers. We suggest that the brain, through evolution or prior experience, has discovered that objects can be classified into lower-dimensional object-classes as to their shape. Extraction of shape from shading is then equivalent to the much simpler problem of parameter estimation in a low-dimensional space. We carry out this proposal for an important class of three-dimensional (3D) objects: human heads. From an ensemble of several hundred laser-scanned 3D heads, we use principal component analysis to derive a low-dimensional parameterization of head shape space. An algorithm for solving shape-from-shading using this representation is presented. It works well even on real images where it is able to recover the 3D surface for a given person, maintaining facial detail and identity, from a single 2D image of his face. This algorithm has applications in face recognition and animation.
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Mahima, K. T. Yasas, Asanka Perera, Sreenatha Anavatti, and Matt Garratt. "Exploring Adversarial Robustness of LiDAR Semantic Segmentation in Autonomous Driving." Sensors 23, no. 23 (December 2, 2023): 9579. http://dx.doi.org/10.3390/s23239579.
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Deep learning networks have demonstrated outstanding performance in 2D and 3D vision tasks. However, recent research demonstrated that these networks result in failures when imperceptible perturbations are added to the input known as adversarial attacks. This phenomenon has recently received increased interest in the field of autonomous vehicles and has been extensively researched on 2D image-based perception tasks and 3D object detection. However, the adversarial robustness of 3D LiDAR semantic segmentation in autonomous vehicles is a relatively unexplored topic. This study expands the adversarial examples to LiDAR-based 3D semantic segmentation. We developed and analyzed three LiDAR point-based adversarial attack methods on different networks developed on the SemanticKITTI dataset. The findings illustrate that the Cylinder3D network has the highest adversarial susceptibility to the analyzed attacks. We investigated how the class-wise point distribution influences the adversarial robustness of each class in the SemanticKITTI dataset and discovered that ground-level points are extremely vulnerable to point perturbation attacks. Further, the transferability of each attack strategy was assessed, and we found that networks relying on point data representation demonstrate a notable level of resistance. Our findings will enable future research in developing more complex and specific adversarial attacks against LiDAR segmentation and countermeasures against adversarial attacks.
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Kello, Martin, Michal Goga, Klaudia Kotorova, Dominika Sebova, Richard Frenak, Ludmila Tkacikova, and Jan Mojzis. "Screening Evaluation of Antiproliferative, Antimicrobial and Antioxidant Activity of Lichen Extracts and Secondary Metabolites In Vitro." Plants 12, no. 3 (January 30, 2023): 611. http://dx.doi.org/10.3390/plants12030611.
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Lichen metabolites represent a wide range of substances with a variety of biological effects. The present study was designed to analyze the potential antiproliferative, antimicrobial and antioxidative effects of several extracts from lichens (Pseudevernia furfuracea, Lobaria pulmonaria, Cetraria islandica, Evernia prunastri, Stereocaulon tomentosum, Xanthoria elegans and Umbilicaria hirsuta) and their secondary metabolites (atranorin, physodic acid, evernic acid and gyrophoric acid). The crude extract, as well as the isolated metabolites, showed potent antiproliferative, cytotoxic activity on a broad range of cancer cell lines in 2D (monolayer) and 3D (spheroid) models. Furthermore, antioxidant (2,2-diphenyl-1-picryl-hydrazylhydrate (DPPH) and in vitro antimicrobial activities were assessed. Data showed that the lichen extracts, as well as the compounds present, possessed biological potential in the studied assays. It was also observed that the extracts were more efficient and their major compounds showed strong effects as antiproliferative, antioxidant and antibacterial agents. Moreover, we demonstrated the 2D and 3D models’ importance to drug discovery for further in vivo studies. Despite the fact that lichen compounds have been neglected by the scientific community for long periods, nowadays they are objects of investigation based on their promising effects.
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Mazur, O. A., L. M. Hrubyak, O. V. Kupchynskyi, and N. V. Bankovska. "Case Study: Using 3D Speckle Tracking Echocardiography for Left Ventricular Aneurysm Diagnosis." Ukrainian journal of cardiovascular surgery, no. 4 (41) (December 16, 2020): 90–95. http://dx.doi.org/10.30702/ujcvs/20.4112/061090-095/073.7.
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Nowadays magnetic resonance imaging (MRI) is a gold standard for diagnosing abnormalities of left ventricular geometry and function, however, it is not universally accessible. Furthermore, MRI is not compatible with pacemakers and similar devices. 3D speckle tracking echocardiography (3D STE) is a cutting-edge echocardiography imaging technique for myocardial deformation assessment. As such, 3D STE looks very promising for diagnosing structural complications of myocardial infarction (MI) and choosing the optimal surgical techniques.
In this case study, we used 3D STE to assess left ventricular function in a patient with left ventricular aneurysm.
The patient was admitted to National Amosov Institute of Cardiovascular Surgery three weeks after having a second MI (the first MI was reported 4 years ago). His coronary angiography showed diffuse coronary artery disease. 2D echocardiography (performed on Toshiba Artida) results: end-diastolic volume (EDV) 206 ml, end-systolic volume (ESV) 141 ml, ejection fraction (EF) (Simpson’s method) 31%. An object sized 2.2*1.6 cm was discovered in the apical region (left ventricular thrombus). 3D STE results: EDV 209 ml, ESV 182 ml, EF 13%. Global area strain (GAS) was considerably decreased (–13.7 %) showing the pattern of ischemic cardiomyopathy with multivessel disease. Due to several reasons, it was impossible to obtain an MRI scan, so a CT coronary angiography was performed (Toshiba Aquilion One). The results of multi-slice computed tomography (MSCT) were consistent with those of echocardiography.
According to the results, the initial plan to resect the apical akinesia region was ruled out. The patient underwent coronary artery bypass grafting (CABG) (4 shunts), the removal of thrombi from the left ventricle (additional fresh thrombi were discovered during the surgical intervention), and left ventricular aneurysm repair under cardiopulmonary bypass. Post-treatment 3D STE results: EDV dropped to 135 ml, EF rose from 13% to 32%. GAS increased up to –20.4 %, while the strains of all segments increased to subnormal levels. The overall dynamics was positive, and the patient was discharged to undergo postoperative rehabilitation.
The case shows that 3D STE data is consistent with CT data in patients with abnormal ventricular remodeling. 3D STE is a good method for differentiation between akinetic scar tissue and a dyskinetic left ventricular aneurysm.
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Seggie, R. J., R. B. Ainsworth, D.A.Johnson, J. P. M. Koninx, B. Spaargaren, and P. M. Stephenson. "AWAKENING OF A SLEEPING GIANT: SUNRISE- TROUBADOUR GAS-CONDENSATE FIELD." APPEA Journal 40, no. 1 (2000): 417. http://dx.doi.org/10.1071/aj99024.
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The Sunrise and Troubadour fields form a complex of giant gas-condensate accumulations located in the Timor Sea some 450 km northwest of Darwin. Left unappraised for almost a quarter of a century since discovery, recently renewed attention has brought these stranded hydrocarbon accumulations to the point of comm-ercialisation.A focussed appraisal program during 1997–1999 driven by expectations of growth in LNG and domestic gas markets, involved the acquisition and processing of an extensive grid of modern 2D seismic and the drilling, coring and testing of three wells. The aim of this program was to quantify better both in-place hydrocarbon volumes (reservoir properties and their distribution) and hydrocarbon recovery efficiency (gas quality and deliverability). Maximum value has been extracted from these data via a combination of deterministic and probabilistic methods, and the integration of analyses across all disciplines.This paper provides an overview of these efforts, describes the fields and details major subsurface uncertainties. Key aspects are:3D, object-based geological modelling of the reservoir, covering the spectrum of plausible sedimentological interpretations.Convolution of rock properties, derived from seismic (AVO) inversion, with 3D geological model realisations to define reservoir properties in inter-well areas.Incorporation of faults (both seismically mapped and probabilistically modelled sub-seismic faults) into both the static 3D reservoir models and the dynamic reservoir simulations.Interpretation of a tilted gas-water contact apparently arising from flow of water in the Plover aquifer away from active tectonism to the north.Extensive gas and condensate fluid analysis and modelling.Scenario-based approach to dynamic modelling.In summary, acquisition of an extensive suite of quality data during the past two-three years coupled with novel, integrated, state-of-the-art analysis of the subsurface has led to a major increase in estimates of potentially recoverable gas and condensate. Improved volumetric confidence in conjunction with both traditional and innovative engineering design (e.g. Floating Liquefied Natural Gas technology) has made viable a range of possible commercial developments from 2005 onwards.
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Bastin, J. C., T. Boycott-Brown, A. Sims, and R. Woodhouse. "The South Morecambe Gas Field, Blocks 110/2a, 110/3a, 110/7a and 110/8a, East Irish Sea." Geological Society, London, Memoirs 20, no. 1 (2003): 107–18. http://dx.doi.org/10.1144/gsl.mem.2003.020.01.09.
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AbstractSouth Morecambe Gas Field is situated in the East Irish Sea and produces gas from the Triassic Sherwood Sandstone Group. Exploration of the basin commenced in 1966 and the discovery well, 110/2-1, was drilled in 1974. Appraisal was complete by 1983 and development was carried out in two phases with the object of providing deliverability to help to satisfy the winter peak in demand. First gas was produced in January 1985 and production during the winter can be sustained at 50MMCMD (1750mmscfd). The stratigraphic succession of the East Irish Sea Basin (EISB) consists of Carboniferous (Dinantian to Westphalian) strata unconformably overlain by 15000 to 20000 feet of continental Permo-Triassic strata. The Triassic Sherwood Sandstone Group contains reservoir rocks and the overlying Mercia Mudstone Group evaporites provide a seal. Seismic cover of the area includes 2D and 3D data, the latter providing good images that form the basis of the current structural interpretation. The structural development of the basin commenced with extension in the Permo-Triassic followed by inversions in the late Jurassic and early Tertiary. The reservoir has been zoned using a scheme that recognizes primary depositional facies as the main criterion for correlation. The petrophysical evaluation has introduced new methods of calculating porosity, Sw and net pay. The latest reservoir pressure data has been used in a material balance study and a two tank simulation model, both give GIIP estimates which are in line with earlier estimates. The new petrophysically derived reservoir parameters were also used to make a volumetric estimate of GIIP. Remaining recoverable reserves are at least 3 Tcf.
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Sawada, Tadamasa. "Influence of 3D Centro-Symmetry on a 2D Retinal Image." Symmetry 12, no. 11 (November 12, 2020): 1863. http://dx.doi.org/10.3390/sym12111863.
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An object is 3D centro-symmetrical if the object can be segmented into two halves and the relationship between them can be represented by a combination of reflection about a plane and a rotation through 180° about an axis that is normal to the plane. A 2D orthographic image of the 3D centro-symmetrical object is always 2D rotation-symmetrical. Note that the human visual system is known to be sensitive to 2D rotational symmetry. This human sensitivity to 2D rotational symmetry might also be used to detect 3D centro-symmetry. If it is, can this detection of 3D centro-symmetry be helpful for the perception of 3D? In this study, the geometrical properties of 3D centro-symmetry and its 2D orthographic and perspective projections were examined to find out whether 3D centro-symmetry plays any role in the perception of 3D. I found that, from a theoretical point-of-view, it is unlikely that 3D centro-symmetry can be used by the human visual system to organize a 2D image of an object in a way that makes it possible to recover the 3D shape of an object from its 2D image.
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Passarella, Rossi, and Osvari Arsalan. "Object Reconstruction from 2D Drawing sketch to 3D Object." Computer Engineering and Applications Journal 5, no. 3 (October 26, 2016): 119–26. http://dx.doi.org/10.18495/comengapp.v5i3.183.
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Design engineer in the early phase of building up another product is  typically using a freehand sketching to communicate or illustrate the idea in the form of orthographic projection. This orthographic projection is based on viewpoint. A translation from 2D drawing view point to 3D models is needed to help engineer to imagine the product preview in 3D. This procedure includes a tedious, so that automation is needed. The way to deal with this reproduction issue begin straightforwardly from 2D freehand portraying, by using the camera, the 2D drawing is captured and then transferred to a Personal Computer. Inside the computer, the image is processed with filtering to find the view point zones. The view point zone than separate to 3 zones, each zone consists of the pixel coordinate. This coordinates are used to generated and processing of 3D voxel Image according to the form of geometries. A case study is presented in order to emphasize and discuss the proposed method
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Fujiyoshi, Hironobu, and Manabu Hashimoto. "2D and 3D Feature for Object Recognition." Journal of the Robotics Society of Japan 35, no. 1 (2017): 22–27. http://dx.doi.org/10.7210/jrsj.35.22.
Full textDissertations / Theses on the topic "2D/3D object discovery"
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Kara, Sandra. "Unsupervised object discovery in images and video data." Electronic Thesis or Diss., université Paris-Saclay, 2025. http://www.theses.fr/2025UPASG019.
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Cette thèse explore les méthodes d'apprentissage auto-supervisé pour la localisation d'objets, communément appelées « Object Discovery ». La localisation d'objets dans les images et les vidéos est un élément essentiel des tâches de vision par ordinateur telles que la détection, la ré-identification, le suivi, etc. Les algorithmes supervisés actuels peuvent localiser (et classifier) les objets avec précision, mais ils sont coûteux en raison de la nécessité de données annotées. Le processus d'étiquetage est généralement répété pour chaque nouvelle donnée ou catégorie d'intérêt, limitant ainsi leur évolutivité. De plus, les approches sémantiquement spécialisées nécessitent une connaissance préalable des classes cibles, restreignant leur utilisation aux objets connus. La découverte d'objets vise à pallier ces limitations en étant plus générique. La première contribution de la thèse s'est concentrée sur la modalité image, en étudiant comment les caractéristiques des modèles transformers de vision auto-supervisés peuvent servir d'indices pour la découverte d'objets multiples. Afin de localiser les objets dans leur définition la plus large, nous avons étendu notre étude aux données vidéo, en exploitant les indices de mouvement et en ciblant la localisation d'objets capables de se déplacer. Nous avons introduit la modélisation de l'arrière-plan et la distillation de connaissances dans la découverte d'objets pour résoudre le problème de la sur-segmentation de l'arrière-plan dans les méthodes existantes, et pour réintégrer les objets statiques, améliorant ainsi de manière significative le rapport signal/bruit dans les prédictions. Reconnaissant les limites des données à modalité unique, nous avons incorporé des données 3D à travers un apprentissage par distillation de connaissances cross-modale. L'échange de connaissances entre les domaines 2D et 3D a permis d'améliorer l'alignement des régions d'objets entre les deux modalités, rendant possible l'utilisation de la cohérence multi-modale comme critère de confianceThis thesis explores self-supervised learning methods for object localization, commonly known as Object Discovery. Object localization in images and videos is an essential component of computer vision tasks such as detection, re-identification, tracking etc. Current supervised algorithms can localize (and classify) objects accurately but are costly due to the need for annotated data. The process of labeling is typically repeated for each new data or category of interest, limiting their scalability. Additionally, the semantically specialized approaches require prior knowledge of the target classes, restricting their use to known objects. Object Discovery aims to address these limitations by being more generic. The first contribution of this thesis focused on the image modality, investigating how features from self-supervised vision transformers can serve as cues for multi-object discovery. To localize objects in their broadest definition, we extended our focus to video data, leveraging motion cues and targeting the localization of objects that can move. We introduced background modeling and knowledge distillation in object discovery to tackle the background over-segmentation issue in existing object discovery methods and to reintegrate static objects, significantly improving the signal-to-noise ratio in predictions. Recognizing the limitations of single-modality data, we incorporated 3D data through a cross-modal distillation framework. The knowledge exchange between 2D and 3D domains improved alignment on object regions between the two modalities, enabling the use of multi-modal consistency as a confidence criterion
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Shao, Zhimin. "3D/2D object recognition from surface patterns." Thesis, University of Surrey, 1997. http://epubs.surrey.ac.uk/844055/.
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Attributed Relational Graph (ARG) is a powerful representation for model based object recognition due to its inherent robustness in handling noisy and incomplete data. In the past few years, the availability of efficient ARG matching algorithms and their theoretical underpinnings have greatly contributed to many successful applications of ARG representation in tackling high level vision problems. During my past three year investigation into object recognition using ARG representation, we have developed a number of novel theories and techniques in the subject area. Some are image processing techniques which help to segment and generate primitive features for building ARG representation (Chapter 2 and 4). Some are about projective invariance in ARG representations (Chapter 3 and 5). Some are about new ARG matching algorithms (Chapter 6). This thesis serves as a summary document of these theories and techniques. The most important contributions of our work to the domain of computer vision, in my opinion, are in two areas: Firstly, in the area of projective invariant ARG representation for object recognition. Here, we demonstrated for the first time, a way to systematically derive ARG representation for objects under complex projective transform by exploiting the knowledge of invariance. The methodology developed by us is a sound strategy that generates ARG representations with a number of desirable and provable properties, amongst which, the most important one is the ability to capture global transformation constraint using binary relations only. The approach significantly reduces the heuristic nature of designing relational measurements and paves the way for wider application of ARG representation in 2D and 3D object recognition. Secondly, in the area of ARG matching. A new mathematical framework for deterministic relaxation algorithms was developed to overcome a number of problems appeared in the existing theories and practises of efficient ARG labelling. A novel labelling algorithm was proposed based on the new theoretical framework. The algorithm has a number of desirable properties compared to existing algorithms. In particular, the resulting algorithm delivers more consistent, faithful-to-observation results in the presence of ambiguities and multiple interpretations compared to other algorithms.
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Sirtkaya, Salim. "Moving Object Detction In 2d And 3d Scenes." Master's thesis, METU, 2004. http://etd.lib.metu.edu.tr/upload/2/12605310/index.pdf.
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This thesis describes the theoretical bases, development and testing of an integrated moving object detection framework in 2D and 3D scenes. The detection problem is analyzed in stationary and non-stationary camera sequences and different algorithms are developed for each case. Two methods are proposed in stationary camera sequences: background extraction followed by differencing and thresholding, and motion detection using optical flow field calculated by &ldquoKanade-Lucas Feature Tracker&rdquo
. For non-stationary camera sequences, different algorithms are developed based on the scene structure and camera motion characteristics. In planar scenes where the scene is flat or distant from the camera and/or when camera makes rotations only, a method is proposed that uses 2D parametric registration based on affine parameters of the dominant plane for independently moving object detection. A modified version of the 2D parametric registration approach is used when the scene is not planar but consists of a few number of planes at different depths, and camera makes translational motion. Optical flow field segmentation and sequential registration are the key points for this case. For 3D scenes, where the depth variation within the scene is high, a parallax rigidity based approach is developed for moving object detection. All these algorithms are integrated to form a unified independently moving object detector that works in stationary and non-stationary camera sequences and with different scene and camera motion structures. Optical flow field estimation and segmentation is used for this purpose.
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Toth, Levente. "3D object recognition based on constrained 2D views." Thesis, University of Plymouth, 1998. http://hdl.handle.net/10026.1/1808.
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The aim of the present work was to build a novel 3D object recognition system capable of classifying man-made and natural objects based on single 2D views. The approach to this problem has been one motivated by recent theories on biological vision and multiresolution analysis. The project's objectives were the implementation of a system that is able to deal with simple 3D scenes and constitutes an engineering solution to the problem of 3D object recognition, allowing the proposed recognition system to operate in a practically acceptable time frame. The developed system takes further the work on automatic classification of marine phytoplanktons, carried out at the Centre for Intelligent Systems, University of Plymouth. The thesis discusses the main theoretical issues that prompted the fundamental system design options. The principles and the implementation of the coarse data channels used in the system are described. A new multiresolution representation of 2D views is presented, which provides the classifier module of the system with coarse-coded descriptions of the scale-space distribution of potentially interesting features. A multiresolution analysis-based mechanism is proposed, which directs the system's attention towards potentially salient features. Unsupervised similarity-based feature grouping is introduced, which is used in coarse data channels to yield feature signatures that are not spatially coherent and provide the classifier module with salient descriptions of object views. A simple texture descriptor is described, which is based on properties of a special wavelet transform. The system has been tested on computer-generated and natural image data sets, in conditions where the inter-object similarity was monitored and quantitatively assessed by human subjects, or the analysed objects were very similar and their discrimination constituted a difficult task even for human experts. The validity of the above described approaches has been proven. The studies conducted with various statistical and artificial neural network-based classifiers have shown that the system is able to perform well in all of the above mentioned situations. These investigations also made possible to take further and generalise a number of important conclusions drawn during previous work carried out in the field of 2D shape (plankton) recognition, regarding the behaviour of multiple coarse data channels-based pattern recognition systems and various classifier architectures. The system possesses the ability of dealing with difficult field-collected images of objects and the techniques employed by its component modules make possible its extension to the domain of complex multiple-object 3D scene recognition. The system is expected to find immediate applicability in the field of marine biota classification.
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Govender, Natasha. "Active object recognition for 2D and 3D applications." Doctoral thesis, University of Cape Town, 2015. http://hdl.handle.net/11427/16520.
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Includes bibliographical referencesActive object recognition provides a mechanism for selecting informative viewpoints to complete recognition tasks as quickly and accurately as possible. One can manipulate the position of the camera or the object of interest to obtain more useful information. This approach can improve the computational efficiency of the recognition task by only processing viewpoints selected based on the amount of relevant information they contain. Active object recognition methods are based around how to select the next best viewpoint and the integration of the extracted information. Most active recognition methods do not use local interest points which have been shown to work well in other recognition tasks and are tested on images containing a single object with no occlusions or clutter. In this thesis we investigate using local interest points (SIFT) in probabilistic and non-probabilistic settings for active single and multiple object and viewpoint/pose recognition. Test images used contain objects that are occluded and occur in significant clutter. Visually similar objects are also included in our dataset. Initially we introduce a non-probabilistic 3D active object recognition system which consists of a mechanism for selecting the next best viewpoint and an integration strategy to provide feedback to the system. A novel approach to weighting the uniqueness of features extracted is presented, using a vocabulary tree data structure. This process is then used to determine the next best viewpoint by selecting the one with the highest number of unique features. A Bayesian framework uses the modified statistics from the vocabulary structure to update the system's confidence in the identity of the object. New test images are only captured when the belief hypothesis is below a predefined threshold. This vocabulary tree method is tested against randomly selecting the next viewpoint and a state-of-the-art active object recognition method by Kootstra et al.. Our approach outperforms both methods by correctly recognizing more objects with less computational expense. This vocabulary tree method is extended for use in a probabilistic setting to improve the object recognition accuracy. We introduce Bayesian approaches for object recognition and object and pose recognition. Three likelihood models are introduced which incorporate various parameters and levels of complexity. The occlusion model, which includes geometric information and variables that cater for the background distribution and occlusion, correctly recognizes all objects on our challenging database. This probabilistic approach is further extended for recognizing multiple objects and poses in a test images. We show through experiments that this model can recognize multiple objects which occur in close proximity to distractor objects. Our viewpoint selection strategy is also extended to the multiple object application and performs well when compared to randomly selecting the next viewpoint, the activation model and mutual information. We also study the impact of using active vision for shape recognition. Fourier descriptors are used as input to our shape recognition system with mutual information as the active vision component. We build multinomial and Gaussian distributions using this information, which correctly recognizes a sequence of objects. We demonstrate the effectiveness of active vision in object recognition systems. We show that even in different recognition applications using different low level inputs, incorporating active vision improves the overall accuracy and decreases the computational expense of object recognition systems.
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Noé, Estelle. "3D layered articulated object from a single 2D drawing." Thesis, KTH, Medieteknik och interaktionsdesign, MID, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-216943.
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Modeling articulated objects made of rigid layered parts used to populate 3D scenes in video games or movie production is a complex and time-consuming task for digital artists. This work proposes a sketch-based approach to efficiently model 3D layered articulated objects, such as animals with rigid shells and armors, in annotating a single 2D photo manually, and eventually fabricate it from automatically computed 2D patterns. In considering symmetrical objects seen under a 3/4 view, and an- notating salient features such as extremities of the rigid articulated parts as a mix of circular and Bézier curve, this approach is able to retrieve depth information, hidden parts, and rotation-articulated structure. The resulting shape consists of a set of quadrangulated polygons that may be flattened in 2D. Details such as ears, tails, and legs were further models using dedicated annotations. The accuracy of the reconstruction has been validated on synthetic cylindrical examples, and its ro- bustness in reconstructing a 3D model of armor, armadillo, and shrimp. The latter was finally fabricated using paper.Att modellera artikulerade objekt gjorda av styva delar lagda i lager som används till att fylla 3D-scener i datorspel och filmskapande är en komplex och tidsödande uppgift för digitala konstnärer. Den här undersökningen föreslår ett skiss-baserat tillvägagångssätt att effektivt modellera artikulerade 3D-objekt lagda i lager, såsom djur med styva skal och rustning, i att annotera ett 2D-foto manuellt, och eventuellt skapa det från automatiskt beräknade 2D-mönster. Hänsyn är tagen till symmetriska objekt sedda under en 3/4 vy, och annotera framträdande egenskapersåsom extremiteter av de styva artikulerade delarna som en blandning avcirkulära och Bézier-kurvor, kan det här tillvägagångssättet hämta information om djup, gömda delar och rotations-artikulerade strukturer. Den slutliga formen består av ett set av fyrsidiga polygoner som kan bli tillplattade i 2D. Detaljer såsom öron, svansar och ben där framtida modeller använder dedikerade annotationer. Noggrannheten av rekonstruktionen har blivit validerad på syntetiska cylindriska exempeloch dess robusthet i att rekonstruera en 3D-modell av en rustning, ett bältdjur och en räka. Den senare skapades slutligen med hjälp av papper.
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Zhu, Yonggen. "Feature extraction and 2D/3D object recognition using geometric invariants." Thesis, King's College London (University of London), 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.362731.
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Gamal, Eldin Ahmed. "Point process and graph cut applied to 2D and 3D object extraction." Nice, 2011. http://www.theses.fr/2011NICE4107.
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L’objectif de cette thèse est de développer une nouvelle approche de détection d’objets 3D à partir d’une image 2D, prenant en compte les occultations et les phénomènes de perspective. Cette approche est fondée sur la théorie des processus ponctuels marqués, qui a fait ses preuves dans la solution de plusieurs problèmes en imagerie haute résolution. Le travail de la thèse est structuré en deux parties. Dans la première partie, nous proposons une nouvelle méthode probabiliste pour gérer les occultations et les effets de perspective. Le modèle proposé est fondé sur la simulation d’une scène 3D utilisant OpenGL sur une carte graphique (GPU). C’est une méthode orientée objet, intégrée dans le cadre d’un processus ponctuel marqué. Nous l’appliquons pour l’estimation de la taille d’une colonie de manchots, là où nous modélisons certaines configurations candidat composé d’objet 3D s’appuyant sur le plan réel. Une densité de Gibbs est définie sur l’espace des configurations, qui prend en compte des informations a priori et sur les données. Pour une configuration proposée, la scène est projetée sur le plan image, et les configurations sont modifiées jusqu’à convergence. Pour évaluer une configuration proposée, nous mesurons la similarité entre l’image projetée de la configuration projetée et l’image réelle, définissant ainsi le terme d’attache aux données et l’a priori pénalisant les recouvrements entre objets. Nous avons introduit des modifications dans l’algorithme d’optimisation pour prendre en compte les nouvelles dépendances qui existent dans notre modèle 3D. Nous proposons une nouvelle méthode d’optimisation appelée « Naissances et Coupe multiples » (Multiple Births and Cut » (MBC) en anglais). Cette méthode combine à la fois la nouvelle méthode d’optimisation « Naissance et mort multiples » (MBD) et les « Graph-Cut ». Les méthodes MBC et MBD sont utilisées pour l’optimisation d’un processus ponctuel marqué. Nous avons comparé les algorithmes MBC et MBD montrant que les principaux avantages de notre algorithme nouvellement proposé sont la réduction du nombre de paramètres, la vitesse de convergence et de la qualité des résultats obtenus. Nous avons validé notre algorithme sur le problème de dénombrement des flamants roses dans une colonieThe topic of this thesis is to develop a novel approach for 3D object detection from a 2D image. This approach takes into consideration the occlusions and the perspective effects. This work has been embedded in a marked point process framework, proved to be efficient for solving many challenging problems dealing with high resolution images. The accomplished work during the thesis can be presented in two parts : In the first part, we propose a novel probabilistic approach to handle occlusions and perspective effects. The proposed method is based on 3D scene simulation on the GPU using OpenGL. It is an object based method embedded in a marked point process framework. We apply it for the size estimation of a penguin colony, where we model a penguin colony as an unknown number of 3D objects. The main idea of the proposed approach is to sample some candidate configurations consisting of 3D objects lying on the real plane. A Gibbs energy is define on the configuration space, which takes into account both prior and data information. The proposed configurations are projected onto the image plane, and the configurations are modified until convergence. To evaluate a proposed configuration, we measure the similarity between the projected image of the proposed configuration and the real image, by defining a data term and a prior term which penalize objects overlapping. We introduced modifications to the optimization algorithm to take into account new dependencies that exists in our 3D model. In the second part, we propose a new optimization method which we call “Multiple Births and Cut” (MBC). It combines the recently developed optimization algorithm Multiple Births and Deaths (MBD) and the Graph-Cut. MBD and MBC optimization methods are applied for the optimization of a marked point process. We compared the MBC to the MBD algorithms showing that the main advantage of our newly proposed algorithm is the reduction of the number of parameters, the speed of convergence and the quality of the obtained results. We validated our algorithm on the counting problem of flamingos in a colony
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Gomez-Donoso, Francisco. "Contributions to 3D object recognition and 3D hand pose estimation using deep learning techniques." Doctoral thesis, Universidad de Alicante, 2020. http://hdl.handle.net/10045/110658.
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In this thesis, a study of two blooming fields in the artificial intelligence topic is carried out. The first part of the present document is about 3D object recognition methods. Object recognition in general is about providing the ability to understand what objects appears in the input data of an intelligent system. Any robot, from industrial robots to social robots, could benefit of such capability to improve its performance and carry out high level tasks. In fact, this topic has been largely studied and some object recognition methods present in the state of the art outperform humans in terms of accuracy. Nonetheless, these methods are image-based, namely, they focus in recognizing visual features. This could be a problem in some contexts as there exist objects that look alike some other, different objects. For instance, a social robot that recognizes a face in a picture, or an intelligent car that recognizes a pedestrian in a billboard. A potential solution for this issue would be involving tridimensional data so that the systems would not focus on visual features but topological features. Thus, in this thesis, a study of 3D object recognition methods is carried out. The approaches proposed in this document, which take advantage of deep learning methods, take as an input point clouds and are able to provide the correct category. We evaluated the proposals with a range of public challenges, datasets and real life data with high success. The second part of the thesis is about hand pose estimation. This is also an interesting topic that focuses in providing the hand's kinematics. A range of systems, from human computer interaction and virtual reality to social robots could benefit of such capability. For instance to interface a computer and control it with seamless hand gestures or to interact with a social robot that is able to understand human non-verbal communication methods. Thus, in the present document, hand pose estimation approaches are proposed. It is worth noting that the proposals take as an input color images and are able to provide 2D and 3D hand pose in the image plane and euclidean coordinate frames. Specifically, the hand poses are encoded in a collection of points that represents the joints in a hand, so that they can be easily reconstructed in the full hand pose. The methods are evaluated on custom and public datasets, and integrated with a robotic hand teleoperation application with great success.
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Sambra-Petre, Raluca-Diana. "2D/3D knowledge inference for intelligent access to enriched visual content." Phd thesis, Institut National des Télécommunications, 2013. http://tel.archives-ouvertes.fr/tel-00917972.
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This Ph.D. thesis tackles the issue of sill and video object categorization. The objective is to associate semantic labels to 2D objects present in natural images/videos. The principle of the proposed approach consists of exploiting categorized 3D model repositories in order to identify unknown 2D objects based on 2D/3D matching techniques. We propose here an object recognition framework, designed to work for real time applications. The similarity between classified 3D models and unknown 2D content is evaluated with the help of the 2D/3D description. A voting procedure is further employed in order to determine the most probable categories of the 2D object. A representative viewing angle selection strategy and a new contour based descriptor (so-called AH), are proposed. The experimental evaluation proved that, by employing the intelligent selection of views, the number of projections can be decreased significantly (up to 5 times) while obtaining similar performance. The results have also shown the superiority of AH with respect to other state of the art descriptors. An objective evaluation of the intra and inter class variability of the 3D model repositories involved in this work is also proposed, together with a comparative study of the retained indexing approaches . An interactive, scribble-based segmentation approach is also introduced. The proposed method is specifically designed to overcome compression artefacts such as those introduced by JPEG compression. We finally present an indexing/retrieval/classification Web platform, so-called Diana, which integrates the various methodologies employed in this thesis
Books on the topic "2D/3D object discovery"
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Bernard, Frischer, and Dakouri-Hild Anastasia, eds. Beyond illustration: 2d and 3d digital technologies as tools for discovery in archaeology. Oxford: Archaeopress, 2008.
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Arcand, Kimberly, and Megan Watzke. Stars in Your Hand. The MIT Press, 2022. http://dx.doi.org/10.7551/mitpress/13800.001.0001.
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An illustrated guide to exploring the Universe in three dimensions. Astronomers have made remarkable discoveries about our Universe, despite their reliance on the flat projection, or 2D view, the sky has offered them. But now, drawing on the vast stores of data available from telescopes and observatories on the ground and in space, astronomers can now use visualization tools to explore the cosmos in 3D. In Stars in Your Hand, Kimberly Arcand and Megan Watzke offer an illustrated guide to exploring the Universe in three dimensions, with easy-to-follow instructions for creating models of stars and constellations using a 3D printer and 3D computer imaging. Stars in Your Hand and 3D technology make learning about space an adventure. Intrigued by the stunning images from high-powered telescopes? Using this book, you can fly virtually through a 3D spacescape and hold models of cosmic objects in your hand. Arcand and Watzke outline advances in 3D technology, describe some amazing recent discoveries in astronomy, reacquaint us with the night sky, and provide brief biographies of the telescopes, probes, and rovers that are bringing us so much data. They then offer images and instructions for printing and visualizing stars, nebulae, supernovae, galaxies, and even black holes in 3D. The 3D Universe is a marvel, and Stars in Your Hand serves as a unique and thrilling portal to discovery.
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Wang, Jason T. L., Bruce A. Shapiro, and Dennis Shasha, eds. Pattern Discovery in Biomolecular Data. Oxford University Press, 1999. http://dx.doi.org/10.1093/oso/9780195119404.001.0001.
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Finding patterns in biomolecular data, particularly in DNA and RNA, is at the center of modern biological research. These data are complex and growing rapidly, so the search for patterns requires increasingly sophisticated computer methods. Pattern Discovery in Biomolecular Data provides a clear, up-to-date summary of the principal techniques. Each chapter is self-contained, and the techniques are drawn from many fields, including graph theory, information theory, statistics, genetic algorithms, computer visualization, and vision. Since pattern searches often benefit from multiple approaches, the book presents methods in their purest form so that readers can best choose the method or combination that fits their needs. The chapters focus on finding patterns in DNA, RNA, and protein sequences, finding patterns in 2D and 3D structures, and choosing system components. This volume will be invaluable for all workers in genomics and genetic analysis, and others whose research requires biocomputing.
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Mohd Mortar, Nurul Aida, Ahmad Fauzan Aziz, Anis Nadhirah Ismail, Faizul Che Pa, and Mohammad Tamizi Selimin. INTRODUCTION TO ENGINEERING DRAWING II. 2024th ed. PENERBIT UNIVERSITI MALAYSIA PERLIS, 2024. http://dx.doi.org/10.58915/bk2023.014.
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This book teaches aspiring students the fundamentals of technical drawing with rigorous attention to detail with conventional and modern techniques. The first chapter introduces the notion of auxiliary views, which are obtained when the lines of sight are not parallel to the major projection planes (frontal, horizontal or profile). The following chapter is pictorial drawing, which provides a 3D depiction to aid in comprehending the contour of an object or deciphering a drawing. The working drawing system, which is used throughout the construction or manufacture of a product, is thoroughly detailed in Chapter 3. The final three chapters covered modern engineering drawing using the AutoCAD application. AutoCAD is the use of computer technologies to facilitate the design, modification and optimisation of 2D, and 3D design. Students can also test their understanding by tackling the exercises provided in this book.
Book chapters on the topic "2D/3D object discovery"
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Stavrou, Pavlos, Pavlos Mavridis, Georgios Papaioannou, Georgios Passalis, and Theoharis Theoharis. "3D Object Repair Using 2D Algorithms." In Computational Science – ICCS 2006, 271–78. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11758525_36.
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Liu, Yong-Jin, Qiu-Fang Fu, Ye Liu, and Xiao-Lan Fu. "2D-Line-Drawing-Based 3D Object Recognition." In Computational Visual Media, 146–53. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34263-9_19.
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Shen, Tianchang, Jun Gao, Amlan Kar, and Sanja Fidler. "Interactive Annotation of 3D Object Geometry Using 2D Scribbles." In Computer Vision – ECCV 2020, 751–67. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-58520-4_44.
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Jansen, Leland, Nathan Liebrecht, Sara Soltaninejad, and Anup Basu. "3D Object Classification Using 2D Perspectives of Point Clouds." In Lecture Notes in Computer Science, 453–62. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54407-2_38.
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Koh, Sungshik, and Phil Jung Kim. "Uncertainty Analysis Using Geometrical Property Between 2D-to-3D Under Affine Projection." In Fuzzy Systems and Knowledge Discovery, 898–907. Berlin, Heidelberg: Springer Berlin Heidelberg, 2006. http://dx.doi.org/10.1007/11881599_112.
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Wu, Qiangqiang, Yan Xia, Jia Wan, and Antoni B. Chan. "Boosting 3D Single Object Tracking with 2D Matching Distillation and 3D Pre-training." In Lecture Notes in Computer Science, 270–88. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-73254-6_16.
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Ghobadi, Seyed Eghbal, Omar Edmond Loepprich, Oliver Lottner, Klaus Hartmann, Wolfgang Weihs, and Otmar Loffeld. "2D/3D Image Data Analysis for Object Tracking and Classification." In Lecture Notes in Electrical Engineering, 1–13. Dordrecht: Springer Netherlands, 2009. http://dx.doi.org/10.1007/978-90-481-3177-8_1.
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Zhang, Ruiyang, Hu Zhang, Hang Yu, and Zhedong Zheng. "Approaching Outside: Scaling Unsupervised 3D Object Detection from 2D Scene." In Lecture Notes in Computer Science, 249–66. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-73247-8_15.
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Zrira, Nabila, Mohamed Hannat, El Houssine Bouyakhf, and Haris Ahmad Khan. "2D/3D Object Recognition and Categorization Approaches for Robotic Grasping." In Advances in Soft Computing and Machine Learning in Image Processing, 567–93. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-63754-9_26.
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Shin, Jiwon, Rudolph Triebel, and Roland Siegwart. "Unsupervised 3D Object Discovery and Categorization for Mobile Robots." In Springer Tracts in Advanced Robotics, 61–76. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-29363-9_4.
Full textConference papers on the topic "2D/3D object discovery"
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Lee, Hanyeol, Jae Hyung Jung, and Chan Gook Park. "2D-3D Object Shape Alignment for Camera-Object Pose Compensation in Object-Visual SLAM." In 2024 IEEE International Conference on Robotics and Automation (ICRA), 15936–42. IEEE, 2024. http://dx.doi.org/10.1109/icra57147.2024.10610659.
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Yang, Zetong, Zhiding Yu, Chris Choy, Renhao Wang, Anima Anandkumar, and Jose M. Alvarez. "Improving Distant 3D Object Detection Using 2D Box Supervision." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 14853–63. IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.01407.
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Wang, Guoqing, Teng Ran, Wendong Xiao, Liang Yuan, and Hong Jiang. "FRS-Voxel: A 3D-2D Hybrid Feature Extraction Network for 3D Object Detection." In 2024 IEEE 7th Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 1170–75. IEEE, 2024. http://dx.doi.org/10.1109/itnec60942.2024.10733002.
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Ji, Haoxuanye, Pengpeng Liang, and Erkang Cheng. "Enhancing 3D Object Detection with 2D Detection-Guided Query Anchors." In 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 21178–87. IEEE, 2024. http://dx.doi.org/10.1109/cvpr52733.2024.02001.
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Wu, Zhennan, and Hiroyuki Sato. "Build-It-Here: Utilizing 2D Inpainting Models for On-Site 3D Object Generation." In 2024 IEEE 9th International Conference on Computational Intelligence and Applications (ICCIA), 104–8. IEEE, 2024. http://dx.doi.org/10.1109/iccia62557.2024.10719286.
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Park, Yechan, Gyuhyeon Pak, and Euntai Kim. "Leveraging 2D Semantic Information for Dynamic Object Removal in Static 3D Point Cloud Map Construction." In 2024 24th International Conference on Control, Automation and Systems (ICCAS), 98–99. IEEE, 2024. https://doi.org/10.23919/iccas63016.2024.10773371.
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Ahmed, Tanver, Adiba Mahjabin Nitu, and Masud Ibn Afjal. "Effective Object Detection in Hyperspectral Images using Segmentation and Multibranch 2D-3D CNN Feature Fusion." In 2024 IEEE International Conference on Computing, Applications and Systems (COMPAS), 1–6. IEEE, 2024. https://doi.org/10.1109/compas60761.2024.10796671.
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Staszak, Rafal, and Dominik Belter. "3D Object Localization With 2D Object Detector and 2D Localization." In 2022 17th International Conference on Control, Automation, Robotics and Vision (ICARCV). IEEE, 2022. http://dx.doi.org/10.1109/icarcv57592.2022.10004312.
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Wang, Patrick S. P. "3D object understanding from 2D images." In International Symposium on Multispectral Image Processing, edited by Ji Zhou, Anil K. Jain, Tianxu Zhang, Yaoting Zhu, Mingyue Ding, and Jianguo Liu. SPIE, 1998. http://dx.doi.org/10.1117/12.323587.
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Srivastava, Siddharth, Gaurav Sharma, and Brejesh Lall. "Large Scale Novel Object Discovery in 3D." In 2018 IEEE Winter Conference on Applications of Computer Vision (WACV). IEEE, 2018. http://dx.doi.org/10.1109/wacv.2018.00026.
Full textReports on the topic "2D/3D object discovery"
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Li, Hang, Hosam Hegazy, Xiaorui Xue, Jiansong Zhang, and Yunfeng Chen. BIM Standards for Roads and Related Transportation Assets. Purdue University, 2023. http://dx.doi.org/10.5703/1288284317641.
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With the industry foundation classes (IFC) building information modeling (BIM) standard (ISO 16739) being adopted by AASHTO as the national standard for modeling bridge and road infrastructure projects, there comes a great opportunity to upgrade the INDOT model development standard of roads and related assets to 2D+3D BIM. This upgrade complies with the national standard and creates a solid foundation for preserving accurate asset information for lifecycle data needs. This study reviewed the current modeling standards for drainage and pavement at different state DOTs and investigated the interoperability between state-of-the-art design modeling software and IFC. It was found that while the latest modeling software is capable of supporting interoperability with IFC, there remain gaps that must be addressed to achieve smooth interoperability for supporting life cycle asset data management. Specifically, the prevalent use of IfcBuildingElementProxy and IfcCourse led to a lack of differentiation in the use of IFC entities for the representations of different components, such as inlets, outfalls, conduits, and different concrete pavement layers. This, in turn, caused challenges in the quality assurance (QA) of IFC models and rendered the conventional model view definition (MVD)-based model checking insufficient. To address these gaps and push forward BIM for infrastructure at INDOT, efforts were made in this project to initially create model development instruction manuals that can serve as the foundation for further development and the eventual establish a consistent and comprehensive IFC-based modeling standards and protocols. In addition, automated object classification leveraging invariant signatures of architecture, engineering, and construction (AEC) objects was investigated. Correspondingly, a QA method and tool was developed to check and identify the different components in an IFC model. The developed tool achieved 91% accuracy on drainage and 100% accuracy in concrete pavement in its tested performance. These solutions aim to support the lifecycle management of INDOT transportation infrastructure projects using BIM and IFC.