Augmented Reality: A Balancing Act Between High Quality and Real-Time Constraints

Mixed Reality ◽  
1999 ◽  
pp. 325-346 ◽  
Author(s):  
Gudrun Klinker ◽  
Didier Strieker ◽  
Dirk Reiners
2014 ◽  
Vol 5 (3) ◽  
pp. 1
Author(s):  
Márcio Cerqueira de Farias Macedo ◽  
Antônio Lopes Apolinário Júnior ◽  
Antonio Carlos dos Santos Souza ◽  
Gilson Antônio Giraldi

To provide on-patient medical data visualization, a medical augmented reality environment must support volume rendering, accurate tracking, real-time performance and high visual quality in the final rendering. Another interesting feature is markerless registration, to solve the intrusiveness introduced by the use of fiducial markers for tracking. In this paper we address the problem of on-patient medical data visualization in a real-time high-quality markerless augmented reality environment. The medical data consists of a volume reconstructed from 3D computed tomography image data. Markerless registration is done by generating a 3D reference model of the region of interest in the patient and tracking it from the depth stream of an RGB-D sensor. From the estimated camera pose, the volumetric medical data and the reference model are combined allowing a visualization of the patient as well as part of his anatomy. To improve the visual perception of the scene, focus+context visualization is used in the augmented reality scene to dynamically define which parts of the medical volume will be visualized in the context of the patient’s image. Moreover, context-preserving volume rendering is employed to dynamically control which parts of the volume will be rendered. The results obtained show that the markerless environment runs in real-time and the techniques applied greatly improve the visual quality of the final rendering.


2020 ◽  
Vol 2020 (14) ◽  
pp. 378-1-378-7
Author(s):  
Tyler Nuanes ◽  
Matt Elsey ◽  
Radek Grzeszczuk ◽  
John Paul Shen

We present a high-quality sky segmentation model for depth refinement and investigate residual architecture performance to inform optimally shrinking the network. We describe a model that runs in near real-time on mobile device, present a new, highquality dataset, and detail a unique weighing to trade off false positives and false negatives in binary classifiers. We show how the optimizations improve bokeh rendering by correcting stereo depth misprediction in sky regions. We detail techniques used to preserve edges, reject false positives, and ensure generalization to the diversity of sky scenes. Finally, we present a compact model and compare performance of four popular residual architectures (ShuffleNet, MobileNetV2, Resnet-101, and Resnet-34-like) at constant computational cost.


2015 ◽  
Vol 6 (2) ◽  
Author(s):  
Rujianto Eko Saputro ◽  
Dhanar Intan Surya Saputra
Keyword(s):  

Media pembelajaran ternyata selalu mengikuti perkembangan teknologi yangada, mulai dari teknologi cetak, audio visual, komputer sampai teknologi gabunganantara teknologi cetak dengan komputer. Saat ini media pembelajaran hasil gabunganteknologi cetak dan komputer dapat diwujudkan dengan media teknologi AugmentedReality (AR). Augmented Reality (AR) adalah teknologi yang digunakan untukmerealisasikan dunia virtual ke dalam dunia nyata secara real-time. Organ pencernaanmanusia terdiri atas Mulut, Kerongkongan atau esofagus, Lambung, Usus halus, danUsus besar. Media pembelajaran mengenal organ pencernaan manusia pada saat inisangat monoton, yaitu melalui gambar, buku atau bahkan alat proyeksi lainnya.Menggunakan Augmented Reality yang mampu merealisasikan dunia virtual ke dunianyata, dapat mengubah objek-objek tersebut menjadi objek 3D, sehingga metodepembelajaran tidaklah monoton dan anak-anak jadi terpacu untuk mengetahuinya lebihlanjut, seperti mengetahui nama organ dan keterangan dari masing-masing organtersebut.


2018 ◽  
Author(s):  
Kyle Plunkett

This manuscript provides two demonstrations of how Augmented Reality (AR), which is the projection of virtual information onto a real-world object, can be applied in the classroom and in the laboratory. Using only a smart phone and the free HP Reveal app, content rich AR notecards were prepared. The physical notecards are based on Organic Chemistry I reactions and show only a reagent and substrate. Upon interacting with the HP Reveal app, an AR video projection shows the product of the reaction as well as a real-time, hand-drawn curved-arrow mechanism of how the product is formed. Thirty AR notecards based on common Organic Chemistry I reactions and mechanisms are provided in the Supporting Information and are available for widespread use. In addition, the HP Reveal app was used to create AR video projections onto laboratory instrumentation so that a virtual expert can guide the user during the equipment setup and operation.


Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 13
Author(s):  
Balaji M ◽  
Chandrasekaran M ◽  
Vaithiyanathan Dhandapani

A Novel Rail-Network Hardware with simulation facilities is presented in this paper. The hardware is designed to facilitate the learning of application-oriented, logical, real-time programming in an embedded system environment. The platform enables the creation of multiple unique programming scenarios with variability in complexity without any hardware changes. Prior experimental hardware comes with static programming facilities that focus the students’ learning on hardware features and programming basics, leaving them ill-equipped to take up practical applications with more real-time constraints. This hardware complements and completes their learning to help them program real-world embedded systems. The hardware uses LEDs to simulate the movement of trains in a network. The network has train stations, intersections and parking slots where the train movements can be controlled by using a 16-bit Renesas RL78/G13 microcontroller. Additionally, simulating facilities are provided to enable the students to navigate the trains by manual controls using switches and indicators. This helps them get an easy understanding of train navigation functions before taking up programming. The students start with simple tasks and gradually progress to more complicated ones with real-time constraints, on their own. During training, students’ learning outcomes are evaluated by obtaining their feedback and conducting a test at the end to measure their knowledge acquisition during the training. Students’ Knowledge Enhancement Index is originated to measure the knowledge acquired by the students. It is observed that 87% of students have successfully enhanced their knowledge undergoing training with this rail-network simulator.


2021 ◽  
Vol 11 (11) ◽  
pp. 4758
Author(s):  
Ana Malta ◽  
Mateus Mendes ◽  
Torres Farinha

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.


2021 ◽  
Vol 2021 (2) ◽  
Author(s):  
Qiang Chen ◽  
Jianyuan Xiao ◽  
Peifeng Fan

Abstract A class of high-order canonical symplectic structure-preserving geometric algorithms are developed for high-quality simulations of the quantized Dirac-Maxwell theory based strong-field quantum electrodynamics (SFQED) and relativistic quantum plasmas (RQP) phenomena. With minimal coupling, the Lagrangian density of an interacting bispinor-gauge fields theory is constructed in a conjugate real fields form. The canonical symplectic form and canonical equations of this field theory are obtained by the general Hamilton’s principle on cotangent bundle. Based on discrete exterior calculus, the gauge field components are discreted to form a cochain complex, and the bispinor components are naturally discreted on a staggered dual lattice as combinations of differential forms. With pull-back and push-forward gauge covariant derivatives, the discrete action is gauge invariant. A well-defined discrete canonical Poisson bracket generates a semi-discrete lattice canonical field theory (LCFT), which admits the canonical symplectic form, unitary property, gauge symmetry and discrete Poincaré subgroup, which are good approximations of the original continuous geometric structures. The Hamiltonian splitting method, Cayley transformation and symmetric composition technique are introduced to construct a class of high-order numerical schemes for the semi-discrete LCFT. These schemes involve two degenerate fermion flavors and are locally unconditional stable, which also preserve the geometric structures. Admitting Nielsen-Ninomiya theorem, the continuous chiral symmetry is partially broken on the lattice. As an extension, a pair of discrete chiral operators are introduced to reconstruct the lattice chirality. Equipped with statistically quantization-equivalent ensemble models of the Dirac vacuum and non-trivial plasma backgrounds, the schemes are expected to have excellent performance in secular simulations of relativistic quantum effects, where the numerical errors of conserved quantities are well bounded by very small values without coherent accumulation. The algorithms are verified in detail by numerical energy spectra. Real-time LCFT simulations are successfully implemented for the nonlinear Schwinger mechanism induced e-e+ pairs creation and vacuum Kerr effect, where the nonlinear and non-perturbative features captured by the solutions provide a complete strong-field physical picture in a very wide range, which open a new door toward high-quality simulations in SFQED and RQP fields.


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