Relevant bibliographies by topics / Three-dimensional learning

Academic literature on the topic 'Three-dimensional learning'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Three-dimensional learning"

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Roth, Dan, Ming-Hsuan Yang, and Narendra Ahuja. "Learning to Recognize Three-Dimensional Objects." Neural Computation 14, no. 5 (May 1, 2002): 1071–103. http://dx.doi.org/10.1162/089976602753633394.

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A learning account for the problem of object recognition is developed within the probably approximately correct (PAC) model of learnability. The key assumption underlying this work is that objects can be recognized (or discriminated) using simple representations in terms of syntactically simple relations over the raw image. Although the potential number of these simple relations could be huge, only a few of them are actually present in each observed image, and a fairly small number of those observed are relevant to discriminating an object. We show that these properties can be exploited to yield an efficient learning approach in terms of sample and computational complexity within the PAC model. No assumptions are needed on the distribution of the observed objects, and the learning performance is quantified relative to its experience. Most important, the success of learning an object representation is naturally tied to the ability to represent it as a function of some intermediate representations extracted from the image. We evaluate this approach in a large-scale experimental study in which the SNoW learning architecture is used to learn representations for the 100 objects in the Columbia Object Image Library. Experimental results exhibit good generalization and robustness properties of the SNoW-based method relative to other approaches. SNoW's recognition rate degrades more gracefully when the training data contains fewer views, and it shows similar behavior in some preliminary experiments with partially occluded objects.
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Flores-Abreu, I. Nuri, T. Andrew Hurly, and Susan D. Healy. "Three-dimensional spatial learning in hummingbirds." Animal Behaviour 85, no. 3 (March 2013): 579–84. http://dx.doi.org/10.1016/j.anbehav.2012.12.019.

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Zelger, P., K. Kaser, B. Rossboth, L. Velas, G. J. Schütz, and A. Jesacher. "Three-dimensional localization microscopy using deep learning." Optics Express 26, no. 25 (December 5, 2018): 33166. http://dx.doi.org/10.1364/oe.26.033166.

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Grobéty, Marie-Claude, and Françoise Schenk. "Spatial learning in a three-dimensional maze." Animal Behaviour 43, no. 6 (June 1992): 1011–20. http://dx.doi.org/10.1016/s0003-3472(06)80014-x.

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Bryant, Rita. "Three-Dimensional Learning at Camp Mind's Eye." Gifted Education International 5, no. 1 (September 1987): 29–32. http://dx.doi.org/10.1177/026142948700500106.

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The article outlines the philosophy which guides the planning of creative, residential summer camps in Texas, U.S.A. The aim of the camps is to provide a protected environment in which young people and their teachers are able to take risks and reach high levels of innovative productivity beyond the attainment of a single individual.
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Gothwal, Pushpa, and Sandesh Singh Shekhawa. "Three Dimensional Cube." International Journal of Engineering & Technology 7, no. 3.30 (August 24, 2018): 90. http://dx.doi.org/10.14419/ijet.v7i3.30.18207.

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Three dimensional cube is the 3D display device implemented using RGB LED. The objective of this paper to design a 3D CUBE using shift registers controlled by the microcontroller. It consists of using a 8-bit microcontrollers (Arduino Mega 2560, Arduino Uno and AT89s52) with on chip ADC, PWM and UART, RGB LEDs, pulse sensor (SEN-11574), shift registers (74HC595N) as core element of the system. It has six keys used for selecting the defined patterns like square, cube, heart traingle etc.. The application of this project is used for entertainment, display the measured physiological parameter (Heart beat), learning and teaching purpose. It can also help to medical professionals for teaching purpose, defence personals in RADAR and architects.
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Garrett, Michael, and Mark McMahon. "Computer-Generated Three-Dimensional Training Environments." International Journal of Gaming and Computer-Mediated Simulations 2, no. 3 (July 2010): 43–60. http://dx.doi.org/10.4018/jgcms.2010070103.

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Problem-based learning is an instructional strategy that emphasises the accumulation and development of knowledge via an active and experiential based approach to solving problems. This pedagogical framework can be instantiated using gaming technology to provide learners with the ability to control their learning experience within a dynamic, responsive, and visually rich three-dimensional virtual environment. In this regard, a conceptual framework referred to as the Simulation, User, and Problem-based Learning (SUPL) approach has been developed in order to inform the design of 3D simulation environments based on gaming technology within a problem-based learning pedagogy. The SUPL approach identifies a series of design factors relative to the user, the problem-solving task, and the 3D simulation environment that guide the learning process and facilitate the transfer of knowledge. This paper will present a simulation environment design according to this conceptual framework for a problem-solving task within the context of an underground mine emergency evacuation. The problem-solving task will be designed to satisfy learning objectives that relate to the development of knowledge and skills for emergency evacuation of the Dominion Mining’s Challenger mining operation located in South Australia.
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Yuan, Xin Lei. "Technical Analysis on Three-Dimensional Virtual Learning Community." Advanced Materials Research 557-559 (July 2012): 2029–32. http://dx.doi.org/10.4028/www.scientific.net/amr.557-559.2029.

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Cultivating a sense of community in education has become increasingly popular. The purpose of this study was to analysis the three-dimensional virtual learning community. States of students in virtual community discourse were discussed from the technical view, than the paper sought to explore how media affected relationship between students.
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Umetani, Nobuyuki, and Bernd Bickel. "Learning three-dimensional flow for interactive aerodynamic design." ACM Transactions on Graphics 37, no. 4 (August 10, 2018): 1–10. http://dx.doi.org/10.1145/3197517.3201325.

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Sinha, Pawan, and Tomaso Poggio. "Role of learning in three-dimensional form perception." Nature 384, no. 6608 (December 1996): 460–63. http://dx.doi.org/10.1038/384460a0.

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Dissertations / Theses on the topic "Three-dimensional learning"

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Benveniste, David 1977. "Cognitive conflict in learning three-dimensional space station structures." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/26750.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2004.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
"September 2004."
Includes bibliographical references (p. 87-88).
(cont.) reached very high values early in the experiment and was significantly but slightly lower in FC than in GC. The target position relative to the subject's body did not affect performance, but subjects responded significantly faster when they were visually upright than when they were upside-down. Although alternative explanations cannot be ruled out, data collected and subjects' comments suggest that unlearning the GC cognitive map posed a significant challenge, and that subjects' knowledge of modules in GC, acquired earlier in the experiment, impeded their learning in FC, at least for the complex FC we used. Results of a Perspective Taking Ability test correlated weakly but significantly with TTR performance in GC, but not in FC. Other tests of spatial skills showed no significant correlation with performance. The effects of motion sickness susceptibility and of gender are also discussed. Supported by NASA Cooperative Agreement NCC 9-58 with the National Space Biomedical Research Institute.
Astronauts train on the ground in normal gravity, in replicas of the space station. Physical constraints force the configuration of these modules on the ground to be different from the configuration of the space station in flight. Based on descriptions of mishaps in human wayfinding (Jonsson 2002), it was hypothesized that the cognitive map of the space station formed from the replicas on the ground could be hard to unlearn. Could the resulting conflict with the actual configuration in flight explain why astronauts lack survey knowledge and often lose track of their orientation? Can they be trained using virtual reality to learn the correct configuration? What makes a configuration hard to learn or unlearn? We studied the ability to learn two realistic and polarized cubic modules in immersive virtual reality. Subjects (n=19) learned these modules first separately, then attached in two different configurations: first a "ground configuration" (GC), then a "flight configuration" (FC). The intrinsic visual verticals of both modules matched in GC, but not in FC, and walls at the interface between the modules were different in the two configurations. Subjects received guided tours of the modules and, through repeated trials, had to predict the location and orientation of one wall (the target), using the wall they were facing. The environment was pseudo-randomly rotated between trials. In the two module environments, subjects were set in the first module and had to place and orient the target wall in the second. The total time to respond to each trial (TTR) and the percent of correct responses (%-correct) were measured. The TTR decreased continuously with time within each virtual environment, but was significantly larger in FC than in GC. %-Correct
by David Benveniste.
S.M.
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Huang, Yawen. "Cross-modality feature learning for three-dimensional brain image synthesis." Thesis, University of Sheffield, 2018. http://etheses.whiterose.ac.uk/21226/.

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Multi-modality medical imaging is increasingly used for comprehensive assessment of complex diseases in either diagnostic examinations or as part of medical research trials. Different imaging modalities provide complementary information about living tissues. However, multi-modal examinations are not always possible due to adversary factors such as patient discomfort, increased cost, prolonged scanning time and scanner unavailability. In addition, in large imaging studies, incomplete records are not uncommon owing to image artifacts, data corruption or data loss, which compromise the potential of multi-modal acquisitions. Moreover, independently of how well an imaging system is, the performance of the imaging equipment usually comes to a certain limit through different physical devices. Additional interferences arise (particularly for medical imaging systems), for example, limited acquisition times, sophisticated and costly equipment and patients with severe medical conditions, which also cause image degradation. The acquisitions can be considered as the degraded version of the original high-quality images. In this dissertation, we explore the problems of image super-resolution and cross-modality synthesis for one Magnetic Resonance Imaging (MRI) modality from an image of another MRI modality of the same subject using an image synthesis framework for reconstructing the missing/complex modality data. We develop models and techniques that allow us to connect the domain of source modality data and the domain of target modality data, enabling transformation between elements of the two domains. In particular, we first introduce the models that project both source modality data and target modality data into a common multi-modality feature space in a supervised setting. This common space then allows us to connect cross-modality features that depict a relationship between each other, and we can impose the learned association function that synthesizes any target modality image. Moreover, we develop a weakly-supervised method that takes a few registered multi-modality image pairs as training data and generates the desired modality data without being constrained a large number of multi-modality images collection of well-processed (\textit{e.g.}, skull-stripped and strictly registered) brain data. Finally, we propose an approach that provides a generic way of learning a dual mapping between source and target domains while considering both visually high-fidelity synthesis and task-practicability. We demonstrate that this model can be used to take any arbitrary modality and efficiently synthesize the desirable modality data in an unsupervised manner. We show that these proposed models advance the state-of-the-art on image super-resolution and cross-modality synthesis tasks that need jointly processing of multi-modality images and that we can design the algorithms in ways to generate the practically beneficial data to medical image analysis.
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Richards, Jason T. (Jason Todd) 1975. "Three-dimensional spatial learning in a virtual space station node." Thesis, Massachusetts Institute of Technology, 2000. http://hdl.handle.net/1721.1/69233.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2000.
This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Includes bibliographical references (p. 77-78).
Astronauts find it difficult to recognize their orientation while facing any of the viewing directions in 6-ported space station node modules. Our previous experiments tested the spatial memory of human subjects in 1-G in an analogous cubic virtual environment and showed that humans are able to learn to orient when instructed to imagine different body orientations while facing in two different directions. Can subjects do the task when facing in all 6 directions? Does training help? Does spatial memory depend on the direction of remembered targets relative to the body? Does performance depend on the subject's ability to rotate himself mentally and use imagery? How long is ability retained after training? 3D spatial learning was studied in two virtual cubic chambers, in which a picture of an animal was drawn on each wall. Through trial-by-trial exposures to a virtual chamber, subjects (n=24) had to memorize the spatial relationships among the 6 pictures around them and learn to predict the direction to a specific picture if they were facing any wall in any roll orientation. After learning in one chamber, the procedure was repeated in a second. Before being tested, subjects received computer-based instructions and practice. Half of subjects were taught to remember logical picture groupings (strategy), while the remaining (control) subjects were free to do the task as they saw fit. Subjects' retention of configurational knowledge (both chambers) and spatial ability (second chamber only, without feedback) were re-tested 1, 7, and 30 days after initial training. Response time (RT) and percent correct (% correct) learning curves were measured on all four days, while configurational knowledge was tested on the last three. All subjects ultimately learned to do the task within 36 trials in either test environment, but performed faster in the second environment than in the first (especially the strategy-trained group). The strategy group showed superior % correct and RT for above/behind targets and generally better configurational knowledge. Retention of configurational knowledge and spatial ability for both groups was good over 30 days. The subjects who reported using mental imagery (n=8) had higher scores on figure rotation tests and % correct for left/right targets. Performances by the control group on the experimental tasks were significantly correlated with those on conventional tests of field independence and 2/3D figure rotation ability. Strategy training helped those who had poorer mental rotation skills, and those who could not use mental imagery. Supported by NASA Cooperative Agreement NCC9-58 with the National Space Biomedical Research Institute, USA.
by Jason T. Richards.
S.M.
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Nichols, Scott A. "Improvement of the camera calibration through the use of machine learning techniques." [Gainesville, Fla.] : University of Florida, 2001. http://etd.fcla.edu/etd/uf/2001/anp1587/nichols%5Fthesis.pdf.

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Thesis (M.S.)--University of Florida, 2001.
Title from first page of PDF file. Document formatted into pages; contains vii, 45 p.; also contains graphics. Vita. Includes bibliographical references (p. 43-44).
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Fichtl, Severin Andreas Thomas-Morus. "Developmental learning of preconditions for means-end actions from 3D vision." Thesis, University of Aberdeen, 2015. http://digitool.abdn.ac.uk:80/webclient/DeliveryManager?pid=227931.

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Specifically equipped and programmed robots are highly successful in controlled industrial environments such as automated production lines. For the transition of robots from such controlled uniform environments to unconstrained household environments with a large range of conditions and variations, a new paradigm is needed to prepare the robots for deployment. Robots need to be able to quickly adapt to their changing environments and learn on their own how to solve their tasks in novel situations. This dissertation focusses on the aspect of learning to predict the success of two-object means-end actions in a developmental way. E.g. the action of bringing one object into reach by pulling another, where the one object is on top of the other. Here it is the “on top” relation that affects the success of the action. Learning the preconditions for complex means-end actions via supervised learning can take several thousand training samples, which is impractical to generate, hence more rapid learning capabilities are necessary. Three contributions of this dissertation are used to solve the learning problem. 1. Inspired by infant psychology this dissertation investigates an approach to intrinsic motivation that is based on active learning, guiding the robot's exploration to create experience useful for improving classification performance. 2. This dissertation introduces histogram based 3D vision features that encode the relative spatial relations between surface points of object pairs, allowing a robot to reliably recognise the important spatial categories that affect means-end action outcomes. 3. Intrinsically encoded experience is extracted into symbolic category knowledge, encoding higher level abstract categories. These symbolic categories are used for knowledge transfer by using them to extend the state space of action precondition learning classifiers. Depending on the actions and their preconditions, the contributions of this dissertation enable a robot to achieve success prediction accuracies above 85% with ten training samples instead of approximately 1000 training samples that would otherwise be required. These results can be achieved when (a) the action preconditions can be easily identified from the used vision features or (b) the action preconditions to be learnt rest upon already existing knowledge, then it is possible to achieve these results by reusing the existing knowledge. This dissertation demonstrates, in simulation, an alternative to handcoding the knowledge required for a robot to interact with and manipulate objects in the environment. It shows that rapid learning, grounded in autonomous exploration, can be feasible if the necessary vision features are constructed and if existing knowledge is consistently reused.
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Kaplan-Rakowski, Regina. "The Effect of Stereoscopic Three-Dimensional Images on Recall of Second Language Vocabulary." OpenSIUC, 2016. https://opensiuc.lib.siu.edu/dissertations/1240.

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The purpose of this experimental study was to investigate the effect of stereoscopic three-dimensional (S3D) images on productive and receptive recall of foreign language vocabulary. S3D images are highly-realistic and differ from non-stereoscopic three-dimensional (NS3D) images in that they provide the impression of the added third dimension of depth. This within-subject study exposed the participants (N = 82) in a controlled setting to a series of carefully designed and randomly distributed NS3D and S3D images. The subjects were then given immediate productive and receptive tests of foreign language vocabulary items that were represented by NS3D and S3D images. Quantitative data consisted of the scores from the vocabulary tests. Qualitative data, gathered through background questionnaires and follow-up surveys, included a mixture of open-ended and Likert questions. The statistical analyses of the data using a series of paired t-tests showed NS3D and S3D images to be equally effective for vocabulary recall. In addition, significantly more subjects found S3D images to be engaging and/or more useful, while subjects also indicated that they perceived the main benefits of learning with S3D images to come from enhanced focus, realism, engagement, and association. At the same time, some learners reported being distracted and experiencing discomfort while viewing S3D images. Post hoc tests revealed that lower performance on S3D images was driven only by those subgroups that exhibited discomfort and / or lack of experience with S3D technology.
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Järrendahl, Hannes. "Automatic Detection of Anatomical Landmarks in Three-Dimensional MRI." Thesis, Linköpings universitet, Datorseende, 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-130944.

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Detection and positioning of anatomical landmarks, also called points of interest(POI), is often a concept of interest in medical image processing. Different measures or automatic image analyzes are often directly based upon positions of such points, e.g. in organ segmentation or tissue quantification. Manual positioning of these landmarks is a time consuming and resource demanding process. In this thesis, a general method for positioning of anatomical landmarks is outlined, implemented and evaluated. The evaluation of the method is limited to three different POI; left femur head, right femur head and vertebra T9. These POI are used to define the range of the abdomen in order to measure the amount of abdominal fat in 3D data acquired with quantitative magnetic resonance imaging (MRI). By getting more detailed information about the abdominal body fat composition, medical diagnoses can be issued with higher confidence. Examples of applications could be identifying patients with high risk of developing metabolic or catabolic disease and characterizing the effects of different interventions, i.e. training, bariatric surgery and medications. The proposed method is shown to be highly robust and accurate for positioning of left and right femur head. Due to insufficient performance regarding T9 detection, a modified method is proposed for T9 positioning. The modified method shows promises of accurate and repeatable results but has to be evaluated more extensively in order to draw further conclusions.
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McManigle, John E. "Three-dimensional geometric image analysis for interventional electrophysiology." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:2f36fa8e-9c64-4807-97c0-25e63398da7e.

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Improving imaging hardware, computational power, and algorithmic design are driving advances in interventional medical imaging. We lay the groundwork here for more effective use of machine learning and image registration in clinical electrophysiology. To achieve identification of atrial fibrosis using image data, we registered the electroanatomic map (EAM) data of atrial fibrillation (AF) patients undergoing pulmonary vein isolation (PVI) with MR (n = 16) or CT (n = 18) images. The relationship between image features and bipolar voltage was evaluated using single-parameter regression and random forest models. Random forest performed significantly better than regression, identifying fibrosis with area under the receiver operating characteristic curve (AUC) 0.746 (MR) and 0.977 (CT). This is the first evaluation of voltage prediction using image data. Next, we compared the character of native atrial fibrosis with ablation scar in MR images. Fourteen AF patients undergoing repeat PVI were recruited. EAM data from their first PVI was registered to the MR images acquired before the first PVI (‘pre-operative’) and before the second PVI ('post-operative' with respect to the first PVI). Non-ablation map points had similar characteristics in the two images, while ablation points exhibited higher intensity and more heterogeneity in post-operative images. Ablation scar is more strongly enhancing and more heterogeneous than native fibrosis. Finally, we addressed myocardial measurement in 3-D echocardiograms. The circular Hough transform was modified with a feature asymmetry filter, epicardial edges, and a search constraint. Manual and Hough measurements were compared in 5641 slices from 3-D images. The enhanced Hough algorithm was more accurate than the unmodified version (Dice coefficient 0.77 vs. 0.58). This method promises utility in segmentation-assisted cross-modality registration. By improving the information that can be extracted from medical images and the ease with which that information can be accessed, this progress will contribute to the advancing integration of imaging in electrophysiology.
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Sweet, Monica Ann. "Representational flexibility in the three-year-old : evidence from dimensional change tasks /." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC IP addresses, 2003. http://wwwlib.umi.com/cr/ucsd/fullcit?p3112192.

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Al-Ashqar, Rami. "Relationship descriptors for interactive motion adaptation." Thesis, University of Edinburgh, 2017. http://hdl.handle.net/1842/29008.

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In this thesis we present an interactive motion adaptation scheme for close interactions between skeletal characters and mesh structures, such as navigating restricted environments and manipulating tools. We propose a new spatial-relationship based representation to encode character-object interactions describing the kinematics of the body parts by the weighted sum of vectors relative to descriptor points selectively sampled over the scene. In contrast to previous discrete representations that either only handle static spatial relationships, or require offline, costly optimization processes, our continuous framework smoothly adapts the motion of a character to deformations in the objects and character morphologies in real-time whilst preserving the original context and style of the scene. We demonstrate the strength of working in our relationship-descriptor space in tackling the issue of motion editing under large environment deformations by integrating procedural animation techniques such as repositioning contacts in an interaction whilst preserving the context and style of the original animation. Furthermore we propose a method that can be used to adapt animations from template objects to novel ones by solving for mappings between the two in our relationship-descriptor space effectively transferring an entire motion from one object to a new one of different geometry whilst ensuring continuity across all frames of the animation, as opposed to mapping static poses only as is traditionally achieved. The experimental results show that our method can be used for a wide range of applications, including motion retargeting for dynamically changing scenes, multi-character interactions, and interactive character control and deformation transfer for scenes that involve close interactions. We further demonstrate a key use case in retargeting locomotion to uneven terrains and curving paths convincingly for bipeds and quadrupeds. Our framework is useful for artists who need to design animated scenes interactively, and modern computer games that allow users to design their own virtual characters, objects and environments, such that they can recycle existing motion data for a large variety of different configurations without the need to manually reconfigure motion from scratch or store expensive combinations of animation in memory. Most importantly it’s achieved in real-time.
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Books on the topic "Three-dimensional learning"

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(Firm), Alias/Wavefront, ed. Learning Maya 5. [Toronto]: Alias/Wavefront, 2003.

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Bell-Smith, Clifford John. Three dimensional awareness, childhood drawing, and the learning disabled student. Ottawa: National Library of Canada = Bibliothèque nationale du Canada, 1992.

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(Firm), Alias, ed. Learning Maya 7. [Toronto, Ont.]: Alias, 2005.

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Gérard, Medioni, ed. Tensor voting: A perceptual organization approach to computer vision and machine learning. [San Rafael, Calif.]: Morgan & Claypool Publishers, 2007.

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Marc-André, Guindon, ed. Learning Autodesk Maya 2008: The special effects handbook. San Rafael, Calif: Autodesk, Inc., 2007.

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Learning Autodesk 3ds Max Design 2010: Essentials. San Rafael, CA: Autodesk/Focal Press, 2009.

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Hess, D. Roland. Blender foundations: The essential guide to learning Blender 2.5. Burlington, MA: Elsevier, 2010.

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Blender foundations: The essential guide to learning Blender 2.6. Burlington, MA: Elsevier, 2010.

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Learning Autodesk Maya 2009: The special effects handbook. San Rafael, CA: Autodesk, 2008.

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Dann, Wanda. Learning to program with Alice. Upper Saddle River, NJ: Pearson/Prentice Hall, 2007.

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Book chapters on the topic "Three-dimensional learning"

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Baker, Tim. "Three-dimensional Learning." In Attracting and Retaining Talent, 226–40. London: Palgrave Macmillan UK, 2014. http://dx.doi.org/10.1057/9781137411754_12.

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Ren, Qiang, Yinpeng Wang, Yongzhong Li, and Shutong Qi. "Three-Dimensional Electromagnetic Scattering Solver." In Sophisticated Electromagnetic Forward Scattering Solver via Deep Learning, 99–122. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-6261-4_5.

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Stieff, Mike, Robert C. Bateman, and David H. Uttal. "Teaching and Learning with Three-dimensional Representations." In Visualization in Science Education, 93–120. Dordrecht: Springer Netherlands, 2005. http://dx.doi.org/10.1007/1-4020-3613-2_7.

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Gao, Ying, Hongshuai Han, Fei Ge, and Shuxia Guo. "Visualization of Multi-dimensional Information of Electromagnetic Environment Based on Three Dimensional Spheres." In E-Learning and Games, 163–72. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40259-8_14.

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Teshima, Yoshinori, Yasunari Watanabe, Yohsuke Hosoya, Kazuma Sakai, Tsukasa Nakano, Akiko Tanaka, Toshiaki Aomatsu, et al. "Three-Dimensional Models of Earth for Tactile Learning." In Lecture Notes in Computer Science, 116–19. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-41267-2_16.

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Zhu, Yao-Lin, Xiao-Yu Wang, Tao-Ruan Wan, and Yu-Qiao Yang. "The Analysis and Creation of Mogao Caves’ Three-Dimensional Model." In E-Learning and Games, 191–98. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-65849-0_20.

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Peng, Gao. "Application of Computer Three-Dimensional Learning in Hybrid Teaching." In Lecture Notes in Electrical Engineering, 2225–30. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-16-0115-6_271.

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Richards, Robert A., and Sheri D. Sheppard. "A learning classifier system for three-dimensional shape optimization." In Parallel Problem Solving from Nature — PPSN IV, 1032–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/3-540-61723-x_1066.

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Boda, István Károly, and Erzsébet Tóth. "Text-Based Second Language Learning in the Three-Dimensional Space." In Accentuated Innovations in Cognitive Info-Communication, 125–48. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-10956-0_6.

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Chen, Juanjuan. "Using a Three-Dimensional Cognitive Mapping Approach to Support Inquiry Learning." In Cognitive Mapping for Problem-Based and Inquiry Learning, 131–55. London: Routledge, 2022. http://dx.doi.org/10.4324/9781003305439-15.

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Conference papers on the topic "Three-dimensional learning"

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Xiang, Mingjun, Lingxiao Wang, Yu Sha, Hui Yuan, Kai Zhou, and Hartmut G. Roskos. "Phase Retrieval for Terahertz Holography with Physics-Informed Deep Learning." In Digital Holography and Three-Dimensional Imaging. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/dh.2022.tu4a.4.

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Two novel phase retrieval methods for THz holography based on physics-informed deep learning are presented. They employ unsupervised learning and supervised learning based on the MNIST dataset, respectively.
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Xiong, Chencen, Zhenbo Ren, Jianglei Di, and Jianlin Zhao. "Phase imaging for digital holography with deep learning." In Digital Holography and Three-Dimensional Imaging. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/dh.2022.w5a.52.

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Deep learning has been widely employed for digital holographic reconstruction. In this paper, we present a learning-based method for phase imaging from digital holograms without complicated and notorious operations such as phase unwrapping.
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Miao, Xin, Xin Yuan, and Paul Wilford. "Deep Learning for Compressive Spectral Imaging." In Digital Holography and Three-Dimensional Imaging. Washington, D.C.: OSA, 2019. http://dx.doi.org/10.1364/dh.2019.m3b.3.

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Rahmani, Babak, Damien Loterie, Eirini Kakkava, Navid Borhani, Ugur Tegin, Demetri Psaltis, and Christophe Moser. "Multimode fiber projection with machine learning." In Digital Holography and Three-Dimensional Imaging. Washington, D.C.: OSA, 2020. http://dx.doi.org/10.1364/dh.2020.htu5b.3.

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Shui, Xinghua, and Huadong Zheng. "Multi-depth Hologram Generation with Unsupervised-learning Based Computer-generated Holography." In Digital Holography and Three-Dimensional Imaging. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/dh.2022.w5a.12.

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Unsupervised-learning based computer-generated holography provides an approach for 2D hologram generation. We propose an unsupervised learning network for multi-depth hologram generation with fully utilizing the different representations of multi-depth object.
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Chia, Yu-Hsin, Sunil Vyas, Yi-You Huang, and Yuan Luo. "Deep learning based HiLo optical sectioning imaging." In Digital Holography and Three-Dimensional Imaging. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/dh.2022.w5a.19.

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We present a deep learning based HiLo optical sectioning imaging method, which utilizes a U-net model to perform image to image transformation for HiLo imaging. Our method can reduce the image acquisition time by half.
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Wang, Hao, Meng Lyu, Ni Chen, and Guohai Situ. "In-line hologram reconstruction with deep learning." In Digital Holography and Three-Dimensional Imaging. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/dh.2018.dw2f.2.

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Nguyen, Thanh, Vy Bui, and George Nehmetallah. "3D Optical Diffraction Tomography Using Deep Learning." In Digital Holography and Three-Dimensional Imaging. Washington, D.C.: OSA, 2018. http://dx.doi.org/10.1364/dh.2018.dw2f.4.

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Choo, Hyon-Gon, Yeon-Gyeong Ju, Kwan-Jung Oh, Yongjun Lim, and Jae-Hyeung Park. "Hologram Reconstruction using cascaded deep learning networks." In Digital Holography and Three-Dimensional Imaging. Washington, D.C.: OSA, 2021. http://dx.doi.org/10.1364/dh.2021.df4c.3.

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Moon, Junbeom, Kihong Choi, Keehoon Hong, Joongki Park, and Soon Ki Jung. "Learning-based noise reduction method for incoherent digital holography." In Digital Holography and Three-Dimensional Imaging. Washington, D.C.: Optica Publishing Group, 2022. http://dx.doi.org/10.1364/dh.2022.th4a.1.

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The reconstructed image acquired from the incoherent holographic system contains a high degree of the grainy noise component. We propose a learning-based noise reduction scheme to mitigate such noise in the complex-valued domain.
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Reports on the topic "Three-dimensional learning"

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Osadchyi, Viacheslav V., Hanna Y. Chemerys, Kateryna P. Osadcha, Vladyslav S. Kruhlyk, Serhii L. Koniukhov, and Arnold E. Kiv. Conceptual model of learning based on the combined capabilities of augmented and virtual reality technologies with adaptive learning systems. [б. в.], November 2020. http://dx.doi.org/10.31812/123456789/4417.

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The article is devoted to actual problem of using modern ICT tools to increase the level of efficiency of the educational process. The current state and relevance of the use of augmented reality (AR) and virtual reality (VR) technologies as an appropriate means of improving the educational process are considered. In particular, attention is paid to the potential of the combined capabilities of AR and VR technologies with adaptive learning systems. Insufficient elaboration of cross-use opportunities for achieving of efficiency of the educational process in state-of-the-art research has been identified. Based on analysis of latest publications and experience of using of augmented and virtual reality technologies, as well as the concept of adaptive learning, conceptual model of learning based on the combined capabilities of AR and VR technologies with adaptive learning systems has been designed. The use of VR and AR technologies as a special information environment is justified, which is applied in accordance with the identified dominant type of students' thinking. The prospects of using the proposed model in training process at educational institutions for the implementation and support of new teaching and learning strategies, as well as improving learning outcomes are determined by the example of such courses as “Algorithms and data structures”, “Computer graphics and three-dimensional modeling”, “Circuit Engineering”, “Computer Architecture”.
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Kompaniets, Alla, Hanna Chemerys, and Iryna Krasheninnik. Using 3D modelling in design training simulator with augmented reality. [б. в.], February 2020. http://dx.doi.org/10.31812/123456789/3740.

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The article is devoted to the theoretical consideration of the problem and the use of innovative technologies in the educational process in the educational establishment of secondary education in the process of studying the school course of computer science. The main advantages of using educational simulators in the educational process are considered, based on the new state standard of basic and complete general secondary education. Based on the analysis of scientific and methodological literature and network sources, the features of the development of simulators for educational purposes are described. Innovative tools for simulator development have been investigated, as augmented reality with the use of three-dimensional simulation. The peculiarities of using a simulator with augmented reality when studying the topic of algorithmization in the course of studying a school computer science are considered. The article also describes the implementation of augmented reality simulator for the formation of algorithmic thinking skills by students, presents the results of development and describes the functionality of the software product. In the further prospects of the study, it is planned to conduct an experimental study to determine the effectiveness of the use of software development in the learning process.
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