检索结果-内蒙古大学图书馆

FluidPlaying: Efficient Adaptive Simulation for Highly Dynamic Fluid

学校读者我要写书评

暂无评论

FluidPlaying: Efficient Adaptive Simulation for Highly Dynam...

Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), IEEE Conference on

作者： Sinuo Liu Xiaojuan Ban Sheng Li Haokai Zeng Xiaokun Wang Yanrui Xu Fei Zhu Guoping Wang Graphics & Interaction Lab National Biomedical Imaging Center School of Computer Science Peking University School of Intelligence Science and Technology University of Science and Technology Beijing Beijing Engineering Technology Research Center of Virtual Simulation and Visualization Peking University Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai)

We present FliudPlaying, a novel dynamic level-based spatially adaptive simulation method that can handle highly dynamic fluid efficiently. To capture the subtle detail of the fluid surface, the high-resolution simulation is performed not only at the free surface but also at those regions with high vorticity levels and velocity difference levels. To minimize the density error, an online optimization scheme is used when increasing the resolution by particle splitting. We also proposed a neighbor-based splash enhancement to compensate for the loss of dynamic details. Compared with the high-resolution simulation baseline, our method can achieve over 3× speedups while consuming only less than 10% computational resources. Furthermore, our method can make up for the loss of high-frequency details caused by the spatial adaptation, and provide more realistic dynamics in particle-based fluid simulation.

关键词： Solid modeling Adaptation models Three-dimensional displays Fluid dynamics Computational modeling Conferences Virtual reality

Comparing bayesian models for organ contouring in headand neck radiotherapy

学校读者我要写书评

暂无评论

arXiv 2021年

作者： Mody, Prerak P. Chaves-De-Plaza, Nicolas Hildebrandt, Klaus van Egmond, René de Ridder, Huib Staring, Marius Department of Radiology Leiden University Medical Center Leiden Netherlands Computer Graphics and Visualization Lab TU Delft Delft Netherlands Industrial Design Engineering TU Delft Delft Netherlands

Deep learning models for organ contouring in radiotherapy are poised for clinical usage, but currently, there exist few tools for automated quality assessment (QA) of the predicted contours. Using Bayesian models and their associated uncertainty, one can potentially automate the process of detecting inaccurate predictions. We investigate two Bayesian models for auto-contouring, DropOut and FlipOut, using a quantitative measure – expected calibration error (ECE) and a qualitative measure – region-based accuracy-vs-uncertainty (R-AvU) graphs. It is well understood that a model should have low ECE to be considered trustworthy. However, in a QA context, a model should also have high uncertainty in inaccurate regions and low uncertainty in accurate regions. Such behaviour could direct visual attention of expert users to potentially inaccurate regions, leading to a speed-up in the QA process. Using R-AvU graphs, we qualitatively compare the behaviour of different models in accurate and inaccurate regions. Experiments are conducted on the MICCAI2015 Head and Neck Segmentation Challenge and on the DeepMindTCIA CT dataset using three models: DropOut-DICE, Dropout-CE (Cross Entropy) and FlipOut-CE. Quantitative results show that DropOut-DICE has the highest ECE, while Dropout-CE and FlipOut-CE have the lowest ECE. To better understand the difference between DropOut-CE and FlipOut-CE, we use the R-AvU graph which shows that FlipOut-CE has better uncertainty coverage in inaccurate regions than DropOut-CE. Such a combination of quantitative and qualitative metrics explores a new approach that helps to select which model can be deployed as a QA tool in clinical settings. © 2021, CC BY.

关键词： Entropy

PREVIS - A Combined Machine Learning and Visual Interpolation Approach for Interactive Reverse Engineering in Assembly Quality Control

学校读者我要写书评

暂无评论

Procedia CIRP 2022年 112卷 483-488页

作者： Patrick Ruediger Felix Claus Viktor Leonhardt Hans Hagen Jan C. Aurich Christoph Garth Institute for Manufacturing Technolgy and Production Systems (FBK) Technische Universität Kaiserslautern P. O. Box 3049 67653 Kaiserslautern Germany Computer Graphics & HCI Lab Technische Universität Kaiserslautern P. O. Box 3049 67653 Kaiserslautern Germany Scientific Visualization Lab Technische Universität Kaiserslautern P. O. Box 3049 67653 Kaiserslautern Germany

We present PREVIS, a visual analytics tool, enhancing machine learning performance analysis in engineering applications. The presented toolchain allows for a direct comparison of regression models. In addition, we provide a methodology to visualize the impact of regression errors on the underlying field of interest in the original domain, the part geometry, via exploiting standard interpolation methods. Further, we allow a real-time preview of user-driven parameter changes in the displacement field via visual interpolation. This allows for fast and accountable online change management. We demonstrate the effectiveness with an ex-ante optimization of an automotive engine hood.

关键词： Explainable Machine Learning Quality Control Reverse Engineering Assembly Process visualization

Learning a perceptual quality metric for correlation in scatterplots

学校读者我要写书评

暂无评论

Learning a perceptual quality metric for correlation in scat...

2019 Conference on Vision, Modeling and visualization, VMV 2019

作者： Wöhler, L. Zou, Y. Mühlhausen, M. Albuquerque, G. Magnor, M. Computer Graphics Lab TU Braunschweig Germany Software for Space Systems and Interactive Visualization DLR Braunschweig Germany

ISBN: (纸本)9783038680987

Visual quality metrics describe the quality and efficiency of multidimensional data visualizations in order to guide data analysts during exploration tasks. Current metrics are usually based on empirical algorithms which do not accurately represent human perception and therefore often differ from the analysts’ expectations. We propose a new perception-based quality metric using deep learning that rates the correlation of data dimensions visualized by scatterplots. First, we created a data set containing over 15, 000 pairs of scatterplots with human annotations on the perceived correlation between the data dimensions. Afterwards, we trained two different Convolutional Neural Networks (CNN), one extracts features from scatterplot images and the other directly from data vectors. We evaluated both CNNs on our test set and compared them to previous visual quality metrics. The experiments show that our new metric is able to represent human perception more accurately than previous methods. © 2019 The Author(s) Eurographics Proceedings © 2019 The Eurographics Association.

关键词： Deep learning

PREVIS-A combined machine learning and visual interpolation approach for interactive reverse engineering in assembly quality control

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Ruediger, Patrick Claus, Felix Leonhardt, Viktor Hagen, Hans Aurich, Jan C. Garth, Christoph Technische Universität Kaiserslautern P. O. Box 3049 Kaiserslautern67653 Germany Computer Graphics & HCI Lab Technische Universität Kaiserslautern P. O. Box 3049 Kaiserslautern67653 Germany Scientific Visualization Lab Technische Universität Kaiserslautern P. O. Box 3049 Kaiserslautern67653 Germany

关键词： Quality control

OMiCroN - Oblique Multipass Hierarchy Creation while Navigating

学校读者我要写书评

暂无评论

arXiv 2020年

作者： da Silva, Vinícius Esperança, Claudio Marroquim, Ricardo Computer Graphics and Visualization Group Van Mourik Broekmanweg 6 Delft Netherlands VISGRAF Lab Estrada Dona Castorina 110 Jardim Botânico Rio de Janeiro - RJCEP: 22460-320 Brazil Cidade Universitária Centro de Tecnologia Block H Rio de Janeiro - RJ21941-972 Brazil

Rendering large point clouds ordinarily requires building a hierarchical data structure for accessing the points that best represent the object for a given viewing frustum and level-of-detail. The building of such data structures frequently represents a large portion of the cost of the rendering pipeline both in terms of time and space complexity, especially when rendering is done for inspection purposes only. This problem has been addressed in the past by incremental construction approaches, but these either result in low quality hierarchies or in longer construction times. In this work we present OMiCroN – Oblique Multipass Hierarchy Creation while Navigating – which is the first algorithm capable of immediately displaying partial renders of the geometry, provided the cloud is made available sorted in Morton order. OMiCroN is fast, being capable of building the entire data structure in memory spending an amount of time that is comparable to that of just reading the cloud from disk. Thus, there is no need for storing an expensive hierarchy, nor for delaying the rendering until the whole hierarchy is read from disk. In fact, a pipeline coupling OMiCroN with an incremental sorting algorithm running in parallel can start rendering as soon as the first sorted prefix is produced, making this setup very convenient for streamed *** Codes [2010]: 68U05 Copyright © 2020, The Authors. All rights reserved.

关键词： Data structures

SHREC'19 track: Extended 2D scene image-based 3D scene retrieval 12

学校读者我要写书评

暂无评论

SHREC'19 track: Extended 2D scene image-based 3D scene retri...

12th Eurographics Workshop on 3D Object Retrieval, 3DOR 2019, in conjunction with the 40th Annual Conference of the European Association for computer graphics, EG 2019

作者： Abdul-Rashid, Hameed Yuan, Juefei Li, Bo Lu, Yijuan Schreck, Tobias Bui, Ngoc-Minh Do, Trong-Le Holenderski, Mike Jarnikov, Dmitri Le, Khiem T. Menkovski, Vlado Nguyen, Khac-Tuan Nguyen, Thanh-An Nguyen, Vinh-Tiep Ninh, Tu V. Rey, Perez Tran, Minh-Triet Wang, Tianyang School of Computer Science and Computer Engineering University of Southern Mississippi United States Department of Computer Science Texas State University United States Institue of Computer Graphics and Knowledge Visualization Graz University of Technology Austria Faculty of Information Technology Vietnam National University Ho Chi Minh City Viet Nam Software Engineering Lab Vietnam National University Ho Chi Minh City Viet Nam Department of Mathematics and Computer Science Eindhoven University of Technology Netherlands University of Information Technology Vietnam National University Ho Chi Minh City Viet Nam Department of Computer Science and Information Technology Austin Peay State University United States

ISBN: (纸本)9783038680772

In the months following our SHREC 2018 - 2D Scene Image-Based 3D Scene Retrieval (SceneIBR2018) track, we have extended the number of the scene categories from the initial 10 classes in the SceneIBR2018 benchmark to 30 classes, resulting in a new benchmark SceneIBR2019 which has 30,000 scene images and 3,000 3D scene models. For that reason, we seek to further evaluate the performance of existing and new 2D scene image-based 3D scene retrieval algorithms using this extended and more comprehensive new benchmark. Three groups from the Netherlands, The United States and Vietnam participated and collectively submitted eight runs. This report documents the evaluation of each method based on seven performance metrics, offers an in-depth discussion as well as analysis on the methods employed and discusses future directions that have the potential to address this task. Again, deep learning techniques have demonstrated notable performance in terms of both accuracy and scalability when applied to this exigent retrieval task. To further enrich the current state of 3D scene understanding and retrieval, our evaluation toolkit, all participating methods' results and the comprehensive 2D/3D benchmark have all been made publicly available. © 2019 The Author(s)

关键词： Three dimensional computer graphics

SHREC'19 track: Extended 2D scene sketch-based 3D scene retrieval 12

学校读者我要写书评

暂无评论

SHREC'19 track: Extended 2D scene sketch-based 3D scene retr...

12th Eurographics Workshop on 3D Object Retrieval, 3DOR 2019, in conjunction with the 40th Annual Conference of the European Association for computer graphics, EG 2019

作者： Yuan, Juefei Abdul-Rashid, Hameed Li, Bo Lu, Yijuan Schreck, Tobias Bui, Ngoc-Minh Do, Trong-Le Nguyen, Khac-Tuan Nguyen, Thanh-An Nguyen, Vinh-Tiep Tran, Minh-Triet Wang, Tianyang School of Computing Sciences and Computer Engineering University of Southern Mississippi United States Department of Computer Science Texas State University United States Institute of Computer Graphics and Knowledge Visualization Graz University of Technology Austria Faculty of Information Technology Vietnam National University Ho Chi Minh City Viet Nam Software Engineering Lab Vietnam National University Ho Chi Minh City Viet Nam University of Information Technology Vietnam National University Ho Chi Minh City Viet Nam Department of Computer Science and Information Technology Austin Peay State University United States

ISBN: (纸本)9783038680772

Sketch-based 3D scene retrieval is to retrieve 3D scene models given a user's hand-drawn 2D scene sketch. It is a brand new but also very challenging research topic in the field of 3D object retrieval due to the semantic gap in their representations: 3D scene models or views differ from non-realistic 2D scene sketches. To boost this interesting research, we organized a 2D Scene Sketch-Based 3D Scene Retrieval track in SHREC'18, resulting a SceneSBR18 benchmark which contains 10 scene classes. In order to make it more comprehensive, we have extended the number of the scene categories from the initial 10 classes in the SceneSBR2018 benchmark to 30 classes, resulting in a new and more challenging benchmark SceneSBR2019 which has 750 2D scene sketches and 3,000 3D scene models. Therefore, the objective of this track is to further evaluate the performance and scalability of different 2D scene sketch-based 3D scene model retrieval algorithms using this extended and more comprehensive new benchmark. In this track, two groups from USA and Vietnam have successfully submitted 4 runs. Based on 7 commonly used retrieval metrics, we evaluate their retrieval performance. We have also conducted a comprehensive analysis and discussion of these methods and proposed several future research directions to deal with this challenging research topic. Deep learning techniques have been proved their great potentials again in dealing with this challenging retrieval task, in terms of both retrieval accuracy and scalability to a larger dataset. We hope this publicly available benchmark, together with its evaluation results and source code, will further enrich and promote 2D scene sketch-based 3D scene retrieval research area and its corresponding applications. © 2019 The Author(s)

关键词： Scalability

IDEAS: Immersive dome experiences for accelerating science

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Faherty, Jacqueline K. SubbaRao, Mark Wyatt, Ryan Ynnerman, Anders deGrasse Tyson, Neil Geller, Aaron Weber, Maria Rosenfield, Philip Stoeckle, Gabriel Weiskopf, Daniel Magnor, Marcus Williams, Peter K.G. Abbott, Brian Marchetti, Lucia Jarrrett, Thomas Fay, Jonathan Peek, Joshua Graur, Or Durrell, Patrick Homeier, Derek Preston, Heather Müller, Thomas Vos, Johanna M. Brown, David Godfrey, Paige Giorla Rice, Emily Gagliuffi, Daniella Bardalez Bock, Alexander American Museum of Natural History Hayden Planetarium Adler Planetarium International Planetarium Society California Academy of Sciences Linköping University Adler Planetarium University of Chicago AAS WorldWide Telescope Instituto de Astronomiá UNAM Natural History Museum Vienna Austria Visualization Research Center University of Stuttgart Computer Graphics Lab TU Braunschweig Center for Astrophysics | Harvard & Smithsonian American Astronomical Society American Museum of Natural History Hayden Planetarium University of Cape Town Iziko Planetarium & Digital Dome Space Telescope Science Institute Johns Hopkins University Youngstown State University Ward Beecher Planetarium Förderkreis Planetarium Göttingen Germany Calusa Nature Center & Planetarium Haus der Astronomie MPIA Microsoft Research Slooh CUNY Macaulay Honors College Scientific Computing and Imaging Institute University of Utah

Astrophysics lies at the crossroads of big datasets (such as the Large Synoptic Survey Telescope and Gaia), open source software to visualize and interpret high dimensional datasets (such as Glue, WorldWide Telescope, and OpenSpace), and uniquely skilled software engineers who bridge data science and research fields. At the same time, more than 4,000 planetariums across the globe immerse millions of visitors in scientific data. We have identified the potential for critical synergy across data, software, hardware, locations, and content that-if prioritized over the next decade-will drive discovery in astronomical research. Planetariums can and should be used for the advancement of scientific research. Current facilities such as the Hayden Planetarium in New York City, Adler Planetarium in Chicago, Morrison Planetarium in San Francisco, the Iziko Planetarium & Digital Dome Research Consortium in Cape Town, and visualization Center C in Norrköping are already developing software which ingests catalogs of astronomical and multi-disciplinary data critical for exploration research primarily for the purpose of creating scientific storylines for the general public. We propose a transformative model whereby scientists become the audience and explorers in planetariums, utilizing software for their own investigative purposes. In this manner, research benefits from the authentic and unique experience of data immersion contained in an environment bathed in context and equipped for collaboration. Consequently, in this white paper we argue that over the next decade the research astronomy community should partner with planetariums to create visualization-based research opportunities for the field. Realizing this vision will require new investments in software and human capital. Copyright © 2019, The Authors. All rights reserved.

关键词： Planetariums