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10th International conference on Functional Imaging and Modeling of the Heart, FIMH 2019

ISBN: (纸本)9783030219482

The proceedings contain 46 papers. The special focus in this conference is on Functional Imaging and Modeling of the Heart. The topics include: A Spatial Adaptation of the Time delay Neural Network for Solving ECGI Inverse Problem;on Sampling Spatially-Correlated Random Fields for complex Geometries;Interpolating Low Amplitude ECG Signals Combined with Filtering According to International Standards Improves Inverse reconstruction of Cardiac Electrical Activity;model Assessment Through data Assimilation of Realistic data in Cardiac Electrophysiology;fibrillation Patterns Creep and Jump in a detailed Three-dimensional Model of the Human Atria;tissue drives Lesion: Computational Evidence of Interspecies Variability in Cardiac Radiofrequency Ablation;correcting Undersampled Cardiac Sources in Equivalent double Layer Forward Simulations;left Ventricular Shape and Motion reconstruction Through a Healthy Model for Characterizing Remodeling After Infarction;towards Automated Quantification of Atrial Fibrosis in Images from Catheterized Fiber-Optics Confocal Microscopy Using Convolutional Neural Networks;virtual Catheter Ablation of Target Areas Identified from Image-Based Models of Atrial Fibrillation;High-Resolution Ex Vivo Microstructural MRI After Restoring Ventricular Geometry via 3d Printing;synchrotron X-Ray Phase Contrast Imaging and deep Neural Networks for Cardiac Collagen Quantification in Hypertensive Rat Model;3d High Resolution Imaging of Human Heart for visualization of the Cardiac Structure;Investigating the 3d Local Myocytes Arrangement in the Human LV Mid-Wall with the Transverse Angle;development of a Computational Fluid dynamics (CFd)-Model of the Arterial Epicardial Vasculature;mesh Based Approximation of the Left Ventricle Using a Controlled Shrinkwrap Algorithm;a Computational Approach on Sensitivity of Left Ventricular Wall Strains to Geometry.

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Study on Multi-Scene VR Service Mode of Library in 5G Environment

Study on Multi-Scene VR Service Mode of Library in 5G Enviro...

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International conference on Information Systems and Computer Aided Education (ICISCAE)

作者： Jinping Xu Wenqing Song Library Wuhan University of Technology Wuhan China

ISBN: (数字)9781728183046

ISBN: (纸本)9781728194608

In order to realize the visual reconstruction and feature extraction of library multi-scene VR service mode, the library multi-scene 3d reconstruction and service mode recognition under 5G environment are required. A library multi-scene VR service reconstruction technology based on virtual reality VR visual feature analysis and service mode simulation is proposed. Firstly, RFId radio frequency tag identification technology is used to collect images of service reliability areas of libraries in 5G environment, and VR visual information simulation technology is used to enhance the collected images. Then, VR technology is used to track and render the contour and texture of the image edge to realize the visual feature reconstruction, service pattern recognition and three-dimensional scene reconstruction of VR service patterns in multiple scenes of the library. Finally, an actual library multi-scene VR service reconstruction simulation experiment is carried out. The results show that the method can realize the integrity detection of library multi-scene features in 5G environment, and the output image quality is high. The library multi-scene VR service mode in 5G environment is improved.

关键词： visualization Solid modeling 5G mobile communication Feature extraction Libraries Image reconstruction Radiofrequency identification

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Multi-layer depth and epipolar feature transformers for 3d scene reconstruction 32

Multi-layer depth and epipolar feature transformers for 3D s...

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32nd IEEE/CVF conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2019

作者： Shin, daeyun Ren, Zhile Sudderth, Erik B. Fowlkes, Charless C. University of California Irvine United States Georgia Institute of Technology

ISBN: (纸本)9781728125060

We tackle the problem of automatically reconstructing a complete 3d model of a scene from a single RGB image. This challenging task requires inferring the shape of both visible and occluded surfaces. Our approach utilizes viewer-centered, multi-layer representation of scene geometry adapted from recent methods for single object shape completion. To improve the accuracy of view-centered representations for complex scenes, we introduce a novel "Epipolar Feature Transformer" that transfers convolutional network features from an input view to other virtual camera viewpoints, and thus better covers the 3d scene geometry. Unlike existing approaches that first detect and localize objects in 3d and then infer object shape using category-specific models, our approach is fully convolutional, end-to-end differentiable, and avoids the resolution and memory limitations of voxel representations. We demonstrate the advantages of multi-layer depth representations and epipolar feature transformers on the reconstruction of a large database of indoor scenes. As Figure 3 shows, our method yields roughly 5x improvement in recall and 2x increase in precision, providing estimates which are both more complete and more accurate. © 2019 IEEE Computer Society. All rights reserved.

关键词： Convolution

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Indoor Scene reconstruction: From panorama images to CAd models 18

Indoor Scene Reconstruction: From panorama images to CAD mod...

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International Normal Labour and Birth Research conference

作者： Luo, Chongyang Zou, Bochao Lyu, Xiangwen Xie, Haiyong China Acad Elect & Informat Technol Natl Engn Lab Publ Safety Risk Percept & Control Beijing Peoples R China Xidian Univ Sch Artificial Intelligence Xian Peoples R China Capital Med Univ Adv Innovat Ctr Human Brain Protect Beijing Peoples R China Univ Sci & Technol China Sch Cyber Sci Hefei Peoples R China

ISBN: (纸本)9781728147659

This paper presents a novel method of reconstructing indoor scenes, both structures and objects, by a single panorama photo. The method combines room structure estimation, furniture detection, models selection, as well as 3d positions reasoning. Compare with others, our preliminary results show this method could get almost the same performance with a simpler procedure.

关键词： Indoor scene reconstruction CAd model panorama images Human-centered computing visualization visualization techniques Computing methodologiesp Computer graphicsp Shape modeling

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Planar abstraction and inverse rendering of 3d indoor environment 40

Planar abstraction and inverse rendering of 3D indoor enviro...

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40th Annual conference on European Association for Computer Graphics, EUROGRAPHICS 2019

作者： Kim, Young Min Ryu, Sangwoo Kim, Ig-Jae Korea Republic of Seoul National University Korea Republic of POSTECH Korea Republic of

A large-scale scanned 3d environment suffers from complex occlusions and misalignment errors. The reconstruction contains holes in geometry and ghosting in texture. These are easily noticed and cannot be used in visually compelling VR content without further processing. On the other hand, the well-known Manhattan World priors successfully recreate relatively simple or clean structures. In this paper, we would like to push the limit of planar representation in indoor environments. We use planes not only to represent the environment geometrically but also to solve an inverse rendering problem considering texture and light. The complex process of shape inference and intrinsic imaging is greatly simplified with the help of detected planes and yet produces a realistic 3d indoor environment. The produced content can effectively represent the spatial arrangements for various AR/VR applications and can be readily combined with virtual objects possessing plausible lighting and texture. © 2019 The Eurographics Association.

关键词： Textures

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reconstruction of ceramic dishes from the archaeological site of the East Pomor town Lalsk by using 3d modeling

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AIP conference Proceedings 2022年第1期2503卷

作者： Sergey Grachev Irina Trushkova 1Vjatka State University 36 Moskovskaya str. Kirov city Kirov region Russia 2Vjatka State Agrotechnical University 133 Oktyabrsky av. Kirov city Kirov region Russia

The relevance of the research presented is based on a new collaboration between the exact and the humanities. The humanitarian sciences, which usually reject modeling, and in particular the historical sciences, are now beginning to actively apply it in the applied field. Notable examples include 3d modeling. Content of work. Using the methods of in-teracting sciences, it is possible to approach the solution of some old problems in the content, in particular, in the issues of reconstruction and analysis of the "material world" of bygone eras. On the example of the archaeological collections of the East Pomorye town of Lalsk of the 17th - 19th centuries. demonstrates the possibilities of digital processing of bulk ceramic material from expeditions. The traditional laborious gluing of pottery fragments from the debris found in the ex-cavation site is being replaced by digital modeling techniques. At the same time, museum originals do not suffer. The resulting collections of 3d models logically expand the field of applied use of historical knowledge through the creation of virtual museums. The use of new technologies adapts knowledge for young people, reveals methods for the wider application of IT in liberal arts education. The involvement of people with limited mobility in the public environment has been especially actualized. By mastering 3d modeling, the designated category can more actively get acquainted with the cultural heritage, including educational collections, through virtual museums, and use this information in distance education and education. Conclusions and research prospects. The presented specific studies in the frontier of engineering and the humanities outline the range of modern use of cyber technologies in various non-technical areas and show the directions for further digitalization of life. development and implementation of high-alloy corrosion-resistant steels with an in-creased complex of physical and mechanical properties, including heat resis

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Quality Assessment Framework for 3d Face reconstruction Models

Quality Assessment Framework for 3D Face Reconstruction Mode...

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International conference on virtual Reality and visualization (ICVRV)

作者： Yunrui Zhu Xun Luo Chu Shi Xinyu Wang dehao Kong Tianjin University of Technology

ISBN: (数字)9781728147529

ISBN: (纸本)9781728147536

The 3d face quality assessment framework is used for quality inspection of the 3d face reconstructions models. It mainly detects the holes of the 3d face model, counts the number of holes, and returns the data results to the HTML page. This work can further improve the management of 3d reconstruction models, detect the quality of modeling, improve the utilization of the model, prepare for the subsequent development of 3d reconstruction model, feature point extraction, etc., which is of great significance for industry efficiency optimization.

关键词： conferences virtual reality visualization

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virtual Thin Slice: 3d Conditional GAN-based Super-Resolution for CT Slice Interval 2nd

Virtual Thin Slice: 3D Conditional GAN-based Super-Resolutio...

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2nd International Workshop on Machine Learning for Medical Image reconstruction (MLMIR) as part of the 22nd Medical Image Computing and Computer Assisted Intervention (MICCAI) conference

作者： Kudo, Akira Kitamura, Yoshiro Li, Yuanzhong Iizuka, Satoshi Simo-Serra, Edgar Fujifilm Corp Imaging Technol Ctr Minato Ku Tokyo Japan Univ Tsukuba Ctr Artificial Intelligence Res Tsukuba Ibaraki Japan Waseda Univ Dept Comp Sci & Engn Shinjuku Ku Tokyo Japan

ISBN: (纸本)9783030338435;9783030338428

Many CT slice images are stored with large slice intervals to reduce storage size in clinical practice. This leads to low resolution perpendicular to the slice images (i.e., z-axis), which is insufficient for 3d visualization or image analysis. In this paper, we present a novel architecture based on conditional Generative Adversarial Networks (cGANs) with the goal of generating high resolution images of main body parts including head, chest, abdomen and legs. However, GANs are known to have a difficulty with generating a diversity of patterns due to a phenomena known as mode collapse. To overcome the lack of generated pattern variety, we propose to condition the discriminator on the different body parts. Furthermore, our generator networks are extended to be three dimensional fully convolutional neural networks, allowing for the generation of high resolution images from arbitrary fields of view. In our verification tests, we show that the proposed method obtains the best scores by PSNR/SSIM metrics and Visual Turing Test, allowing for accurate reproduction of the principle anatomy in high resolution. We expect that the proposed method contribute to effective utilization of the existing vast amounts of thick CT images stored in hospitals.

关键词： deep learning Generative Adversarial Network Super resolution Computer vision Computed tomography

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Time Enhanced Architectural Modelling (T.E.A.M.): virtual reality project for the planning and visualization of kinetic architecture and dynamic design

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Journal of Physics: conference Series 2021年第1期2042卷

作者： V. Temporin J. Volpato P.L. Cocco A. d'Angelo M. Tieghi Poplab (Performance Oriented Prototyping Fabrication Laboratory) academic spinoff of Iuav University of Venice - viale Porta Adige 45 45100 ROVIGO Italy.

T.E.A.M. is a research project that was created to facilitate the design of kinetic projects and components created through a computational design process. Time is the ingredient that allows dynamism. The following paper examines the core features of Platform One, an experimental digital application born within the project and aimed at supporting designers during the development, in a VE (virtual Environment) of dynamic architectures and components, as well as the principles that inspired it. The application presents two key features: the first one is that everything modified and developed in the VE retains its geometric characteristics, allowing the user to reach an informed 3d model at the end of the process; the second one is the ease and enjoyment with which the user manipulates complex dynamic geometries in the three-dimensional environment through a natural interface design approach that focuses on direct manipulation of architectural objects and components. The simulator is designed to be used in a 6dOF virtual environment using a commercial VR headset. It has currently been loaded with several archetypal test architectures and soon it will be available to designers who want to test their work with it.

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Attention Based Temporal Convolutional Neural Network for Real-Time 3d Human Pose reconstruction

Attention Based Temporal Convolutional Neural Network for Re...

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作者： Liu, Ruixu University of Dayton

学位级别：Dr.Ph.

Computer vision and artificial intelligence aim to give computers a high-level understanding of images or videos. Through imitating the human brain that perceives and understands multimode information, a neural network can implicitly learn intricate structures of large-scale data. deep learning allows computational models of multiple processing layers to learn and represent data with multiple levels. The main objective of this dissertation research is to develop robust deep learning architectures for human detection, pose estimation, and 3d pose reconstruction. 3d human pose estimation is a classic vision task enabling numerous applications from activity recognition to human-robot interaction and virtual/augmented reality. We present a deep convolutional neural network architecture that encapsulates a multi-scale feature fusion strategy for human detection in a complex background. To detect the human pose on 2d images and to project it to 3d space for 3d pose reconstruction, we need to obtain human keypoints such as face landmark points and joints of hands and body. We present a deep convolutional neural network architecture for human keypoints detection and 2d pose estimation. Our approach for 3d pose prediction from 2d image measurements, is based on two key observations: (1) temporally incoherent and jittery estimates often yield from individual frame prediction; (2) error rate can be remarkably reduced with an enhanced 2d pose input. Therefore, we propose an attention-based temporal convolutional neural network (ATCN) that is capable of guiding the network to adaptively identify important frames. ATCN can also extract a more significant portion of the intermediate output from each processing layer to estimate the 3d pose. A multi-scaled dilated convolution (MdC) method is employed that can model long-range dependencies among frames to achieve large temporal receptive fields. MdC will help to handle partial occlusions, fast motion, and complex background conditio

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