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arXiv 2023年

作者： Mohr, Ludwig Geles, Ismail Fraundorfer, Friedrich Institute of Computer Graphics and Vision Graz University of Technology Inffeldgasse 16 Graz8010 Austria

Rigid registration of point clouds is a fundamental problem in computer vision with many applications from 3D scene reconstruction to geometry capture and robotics. If a suitable initial registration is available, conventional methods like ICP and its many variants can provide adequate solutions. In absence of a suitable initialization and in the presence of a high outlier rate or in the case of small overlap though the task of rigid registration still presents great challenges. The advent of deep learning in computer vision has brought new drive to research on this topic, since it provides the possibility to learn expressive feature-representations and provide one-shot estimates instead of depending on time-consuming iterations of conventional robust methods. Yet, the rotation and permutation invariant nature of point clouds poses its own challenges to deep learning, resulting in loss of performance and low generalization capability due to sensitivity to outliers and characteristics of 3D scans not present during network training. In this work, we present a novel fast and light-weight network architecture using the attention mechanism to augment point descriptors at inference time to optimally suit the registration task of the specific point clouds it is presented with. Employing a fully-connected graph both within and between point clouds lets the network reason about the importance and reliability of points for registration, making our approach robust to outliers, low overlap and unseen data. We test the performance of our registration algorithm on different registration and generalization tasks and provide information on runtime and resource consumption. The code and trained weights are available at https://***/mordecaimalignatius/GAFAR/. Copyright © 2023, The Authors. All rights reserved.

关键词： computer vision

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3D Tracking of Multiple Drones Based on Particle Swarm Optimization 23rd

3D Tracking of Multiple Drones Based on Particle Swarm Opt...

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23rd International Conference on Computational Science, ICCS 2023

作者： Krzeszowski, Tomasz Switonski, Adam Zielinski, Michal Wojciechowski, Konrad Rosner, Jakub Faculty of Electrical and Computer Engineering Rzeszow University of Technology al. Powstancow Warszawy 12 Rzeszow35-959 Poland Department of Computer Graphics Vision and Digital Systems Silesian University of Technology ul. Akademicka 16 Gliwice44-100 Poland Polish-Japanese Academy of Information Technology ul. Koszykowa 86 Warsaw02-008 Poland

ISBN: (纸本)9783031360268

This paper presents a method for the tracking of multiple drones in three-dimensional space based on data from a multi-camera system. It uses the Particle Swarm Optimization (PSO) algorithm and methods for background/foreground detection. In order to evaluate the developed tracking algorithm, the dataset consisting of three simulation sequences and two real ones was prepared. The sequences contain from one to ten drones moving with different flight patterns. The simulation sequences were created using the Unreal Engine and the AirSim plugin, whereas the real sequences were registered in the Human Motion Lab at the Polish-Japanese Academy of Information technology. The lab is equipped with the Vicon motion capture system, which was used to acquire ground truth data. The conducted experiments show the high efficiency and accuracy of the proposed method. For the simulation data, tracking errors from 0.086 m to 0.197 m were obtained, while for real data, the error was 0.101–0.124 m. The system was developed for augmented reality applications, especially games. The dataset is available at http://***/drones/. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Drones

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Identifying and Extracting Pedestrian Behavior in Critical Traffic Situations

arXiv

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arXiv 2024年

作者： Schachner, Martin Schneider, Bernd Weissenbacher, Fabian Kirillova, Nadezda Possegger, Horst Bischof, Horst Klug, Corina Vehicle Safety Institute Graz University of Technology Graz8010 Austria Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria

A better understanding of interactive pedestrian behavior in critical traffic situations is essential for the development of enhanced pedestrian safety systems. Real-world traffic observations play a decisive role in this, since they represent behavior in an unbiased way. In this work, we present an approach of how a subset of very considerable pedestrian-vehicle interactions can be derived from a camera-based observation system. For this purpose, we have examined road user trajectories automatically for establishing temporal and spatial relationships, using 110h hours of video recordings. In order to identify critical interactions, our approach combines the metric post-encroachment time with a newly introduced motion adaption metric. From more than 11,000 reconstructed pedestrian trajectories, 259 potential scenarios remained, using a post-encroachment time threshold of 2s. However, in 95% of cases, no adaptation of the pedestrian behavior was observed due to avoiding criticality. Applying the proposed motion adaption metric, only 21 critical scenarios remained. Manual investigations revealed that critical pedestrian vehicle interactions were present in 7 of those. They were further analyzed and made publicly available for developing pedestrian behavior models3. The results indicate that critical interactions in which the pedestrian perceives and reacts to the vehicle at a relatively late stage can be extracted using the proposed method. © 2024, CC BY-NC-ND.

关键词： Pedestrian safety

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Deep learning as a tool for quantum error reduction in quantum image processing

arXiv

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arXiv 2023年

作者： Werner, Krzysztof Wereszczyński, Kamil Potempa, Rafal Cyran, Krzysztof Dept. of Computer Graphics Vision and Digital Systems Silesian University of Technology Gliwice Poland

Despite the limited availability and quantum volume of quantum computers, quantum image representation is a widely researched area. Currently developed methods use quantum entanglement to encode information about pixel positions. These methods range from using the angle parameter of the rotation gate (e.g., the Flexible Representation of Quantum Images, FRQI), sequences of qubits (e.g., Novel Enhanced Quantum Representation, NEQR), or the angle parameter of the phase shift gates (e.g., Local Phase Image Quantum Encoding, LPIQE) for storing color information. All these methods are significantly affected by decoherence and other forms of quantum noise, which is an inseparable part of quantum computing in the noisy intermediate-scale quantum era. These phenomena can highly influence the measurements and result in extracted images that are visually dissimilar to the originals. Because this process is at its foundation quantum, the computational reversal of this process is possible. There are many methods for error correction, mitigation, and reduction, but all of them use quantum computer time or additional qubits to achieve the desired result. We report the successful use of a generative adversarial network trained for image-to-image translation, in conjunction with Phase Distortion Unraveling error reduction method, for reducing overall error in images encoded using LPIQE. © 2023, CC BY.

关键词： Quantum noise

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Profiling and Visualizing GPU Memory Access and Cache Behavior of Ray Tracers

Profiling and Visualizing GPU Memory Access and Cache Behavi...

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22nd Eurographics Symposium on Parallel graphics and Visualization, EGPGV 2022

作者： von Buelow, Max Riemann, Kai Guthe, Stefan Fellner, Dieter W. Technical University of Darmstadt Germany Germany Fraunhofer IGD Germany Graz University of Technology Institute of Computer Graphics and Knowledge Visualization Austria

ISBN: (纸本)9783038681755

Graphical processing units (GPUs) have gained popularity in recent years due to their efficiency in running massively parallel applications. Recent developments have also adapted ray-tracing algorithms to the GPU, where the bottleneck in the overall performance is usually given by the memory bandwidth. In this paper, we present an interactive, web-based visualization tool for GPU memory traces that provides visual insight into the memory and cache behavior of our reference ray tracer, by mapping internal GPU state back onto 3D objects. In order to visualize cache behavior, we use reuse distances on both GPU cache layers that are calculated on the basis of memory traces extracted from a real GPU using binary instrumentation. An advantage of our system is that it runs independently of the ray-tracing program. We further show visualizations of our GPU ray tracer and compare the visualizations of several ray-tracing approaches. We find our work to act as a convenient toolset to gather insights on which data structures and mesh regions can be cached efficiently, and how ray-tracing acceleration structures behave on various input meshes, bounding volume hierarchies, memory layouts, frame buffer resolutions, and work distribution techniques. © 2022 The Author(s) Eurographics Proceedings © 2022 The Eurographics Association.

关键词： Visualization

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Tool for Game Plot Line Visualization for Designers, Testers and Players 1

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20th International Conference on Cooperative Design, Visualization and Engineering, CDVE 2023

作者： Nowak, Leszek Grabska-Gradzińska, Iwona Palacz, Wojciech Grabska, Ewa Guzik, Maria Department of Information Technologies Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Krakow Poland Department of Games Technology Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Krakow Poland Department of Design and Computer Graphics Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Krakow Poland Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University Krakow Poland

ISBN: (数字)9783031438158

ISBN: (纸本)9783031438141

This paper presents a tool for automatic and interactive visualization of game plots, which can be used to check whether the designers’ work meets the constraints of the world, to help testers control played game story paths. The referential designers’ game plots can be imported from any flowchart diagrams as an XML file and the actual gameplay variant is generated on the fly and applied to the original design based on detected common diagram points. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Visualization

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Challenges of Distance Learning During the Coronavirus (COVID-19) Pandemic A Case Study of the University of Petra

Challenges of Distance Learning During the Coronavirus (COVI...

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Cyber Resilience (ICCR), International Conference on

作者： Khalil Omar Yaser Saleh Ghassan Issa Computer Science Department University of Petra Amman Jordan Department of Computer Graphics and Animation Princes Sumaya University for Technology Amman Jordan School of Information Technology Skyline University College Sharjah UAE

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

The Coronavirus (COVID-19) pandemic has affected all aspects of our daily life and imposed social distancing among people. In higher education, the shift from traditional learning (TL) to the distance learning (DL) model has become inevitable to continue academic life. However, this unexpected shift is complex and poses new challenges and obstacles for the stakeholders of the DL system. In this research, the DL system's stakeholders have been classified into students, instructors, technical administrators, and educational administrators. This research study aims to gather the perceptions of and satisfaction level of these stakeholders with DL and explore the challenges encountered by these stakeholders during the shift to DL during the COVID-19 pandemic. Therefore, four questionnaires were developed and distributed to the identified stakeholders at the University of Petra, which has been adopted as a case study for this research.

关键词： COVID-19 computer aided instruction Pandemics Education Human factors Social factors Stakeholders

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LA-CaRe-CNN: Cascading Refinement CNN for Left Atrial Scar Segmentation 1st

LA-CaRe-CNN: Cascading Refinement CNN for Left Atrial Scar ...

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1st MICCAI Challenge Comprehensive Analysis and Computing of Real-World Medical Images, CARE 2024 Held in Conjunction with 27th International Conference on Medical Image Computing and computer-Assisted Intervention, MICCAI 2024

作者： Thaler, Franz Štern, Darko Plank, Gernot Urschler, Martin Gottfried Schatz Research Center: Medical Physics and Biophysics Medical University of Graz Graz Austria Institute of Computer Graphics and Vision Graz University of Technology Graz Austria AVL List GmbH Graz Austria Institute for Medical Informatics Statistics and Documentation Medical University of Graz Graz Austria

ISBN: (纸本)9783031870088

Atrial fibrillation (AF) represents the most prevalent type of cardiac arrhythmia for which treatment may require patients to undergo ablation therapy. In this surgery cardiac tissues are locally scarred on purpose to prevent electrical signals from causing arrhythmia. Patient-specific cardiac digital twin models show great potential for personalized ablation therapy, however, they demand accurate semantic segmentation of healthy and scarred tissue typically obtained from late gadolinium enhanced (LGE) magnetic resonance (MR) scans. In this work we propose the Left Atrial Cascading Refinement CNN (LA-CaRe-CNN), which aims to accurately segment the left atrium as well as left atrial scar tissue from LGE MR scans. LA-CaRe-CNN is a 2-stage CNN cascade that is trained end-to-end in 3D, where Stage 1 generates a prediction for the left atrium, which is then refined in Stage 2 in conjunction with the original image information to obtain a prediction for the left atrial scar tissue. To account for domain shift towards domains unknown during training, we employ strong intensity and spatial augmentation to increase the diversity of the training dataset. Our proposed method based on a 5-fold ensemble achieves great segmentation results, namely, 89.21% DSC and 1.6969 mm ASSD for the left atrium, as well as 64.59% DSC and 91.80% G-DSC for the more challenging left atrial scar tissue. Thus, segmentations obtained through LA-CaRe-CNN show great potential for the generation of patient-specific cardiac digital twin models and downstream tasks like personalized targeted ablation therapy to treat AF. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Ablation

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Augmentation-Based Domain Generalization and Joint Training from Multiple Source Domains for Whole Heart Segmentation 1st

Augmentation-Based Domain Generalization and Joint Training...

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ISBN: (纸本)9783031870088

As the leading cause of death worldwide, cardiovascular diseases motivate the development of more sophisticated methods to analyze the heart and its substructures from medical images like Computed Tomography (CT) and Magnetic Resonance (MR). Semantic segmentations of important cardiac structures that represent the whole heart are useful to assess patient-specific cardiac morphology and pathology. Furthermore, accurate semantic segmentations can be used to generate cardiac digital twin models which allows e.g. electrophysiological simulation and personalized therapy planning. Even though deep learning-based methods for medical image segmentation achieved great advancements over the last decade, retaining good performance under domain shift - i.e. when training and test data are sampled from different data distributions - remains challenging. In order to perform well on domains known at training-time, we employ a (1) balanced joint training approach that utilizes CT and MR data in equal amounts from different source domains. Further, aiming to alleviate domain shift towards domains only encountered at test-time, we rely on (2) strong intensity and spatial augmentation techniques to greatly diversify the available training data. Our proposed whole heart segmentation method, a 5-fold ensemble with our contributions, achieves the best performance for MR data overall and a performance similar to the best performance for CT data when compared to a model trained solely on CT. With 93.33% DSC and 0.8388 mm ASSD for CT and 89.30% DSC and 1.2411 mm ASSD for MR data, our method demonstrates great potential to efficiently obtain accurate semantic segmentations from which patient-specific cardiac twin models can be generated. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Semantic Segmentation

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An Evaluation of Affordance Templates for Human-Robot Interaction

An Evaluation of Affordance Templates for Human-Robot Intera...

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IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Laurent Frering Peter Mohr-Ziak Clemens Koenczoel Jochen A. Mosbacher Matthias Eder Dietrich Albert Bettina Kubicek Gerald Steinbauer-Wagner Institute of Software Technology Graz University of Technology Graz Austria Institute of Computer Graphics Graz University of Technology Graz Austria Department of Psychology University of Graz Graz Austria

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

There is an interest in and need for the use of semiautonomous robots in various fields, such as disaster response. Over the years, different techniques for semiautonomous control were developed, ranging from teleoperation guidance to different user interface designs for setting task constraints and goals interactively. Among those, affordance templates emerged as a recent method for users to efficiently provide robots with contextual information about object shapes, properties, and affordances. In many fields where direct teleoperation is common, affordance templates seem to be a promising candidate for improving performance and usability. However, despite the reports on the potential benefits of this technique in comparison to direct teleoperation, they are often qualitative or focus on tasks where teleoperation is particularly challenging. This can be a problem because task difficulty can influence different performance metrics and human factors, so results from studies that show large differences in task difficulty between interaction modes cannot be directly generalized to tasks with smaller or non-existent differences in difficulty. In this study, we aim to evaluate the effectiveness of affordance templates in the context of debris removal for disaster response, a task where direct teleoperation is a viable technique. We compared the two methods in a simulated setting through a user study involving 41 participants by measuring a) usability through questionnaires and b) performance on secondary tasks, an established measure of spare information processing capacity. The study results show that despite similar difficulty, users performed better on secondary tasks when using affordance template-based semiautonomy in this setting.

关键词： Atmospheric measurements Shape Affordances Disasters Human-robot interaction Information processing Human factors Particle measurements Usability Robots

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