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IEEE Transactions on Visualization and Computer Graphics

Ran Luo, Tianjia Shao, Huamin Wang, Weiwei Xu, Xiang Chen, Kun Zhou, Yin Yang
NNWarp is a highly re-usable and efficient neural network (NN) based nonlinear deformable simulation framework. Unlike other machine learning applications such as image recognition, where different inputs have a uniform and consistent format (e.g. an array of all the pixels in an image), the input for deformable simulation is quite variable, high-dimensional, and parametrization-unfriendly. Consequently, even though the neural network is known for its rich expressivity of nonlinear functions, directly using an NN to reconstruct the force-displacement relation for general deformable simulation is nearly impossible...
November 15, 2018: IEEE Transactions on Visualization and Computer Graphics
Mingming Liu, Kexin Zhang, Jie Zhu, Jun Wang, Jie Guo, Yanwen Guo
We propose a new method for modeling the indoor scene from a single image. With our system, the user only needs to drag a few bounding boxes surrounding the objects of interest. Our system then automatically finds the most similar models from the ShapeNet repository and aligns them with the corresponding objects of interest. To achieve this, each 3D model is represented as a group of view-dependent representations generated from a set of synthesized views. We iteratively conduct object segmentation and 3D model retrieval, based on the observation that good segmentation of the objects of interest can significantly improve the accuracy of model retrieval and make it robust to cluttered background and occlusion, and in turn, the retrieved models can be used to assist with segmentation...
November 12, 2018: IEEE Transactions on Visualization and Computer Graphics
Jun Han, Jun Tao, Chaoli Wang
For effective flow visualization, identifying representative flow lines or surfaces is an important problem which has been studied. However, no work can solve the problem for both lines and surfaces. In this paper, we present FlowNet, a single deep learning framework for clustering and selection of streamlines and stream surfaces. Given a collection of streamlines or stream surfaces generated from a flow field data set, our approach converts them into binary volumes and then employs an autoencoder to learn their respective latent feature descriptors...
November 12, 2018: IEEE Transactions on Visualization and Computer Graphics
Martin Sik, Jaroslav Krivanek
Two decades have passed since the introduction of Markov chain Monte Carlo (MCMC) into light transport simulation by Veach and Guibas, and numerous follow-up works have been published since then. However, up until now no survey has attempted to cover majority of these methods. The aim of this paper is therefore to offer a first comprehensive survey of MCMC algorithms for light transport simulation. The methods presented in this paper are categorized by their objectives and properties, while we point out their strengths and weaknesses...
November 9, 2018: IEEE Transactions on Visualization and Computer Graphics
Wenbin He, Hanqi Guo, Han-Wei Shen, Tom Peterka
We propose surface density estimate (SDE) to model the spatial distribution of surface features-isosurfaces, ridge surfaces, and streamsurfaces-in 3D ensemble simulation data. The inputs of SDE computation are surfaces represented as polygon meshes, and no field datasets are required. The SDE is defined as the kernel density estimate of the infinite set of points on the input surfaces and is approximated by accumulating the surface densities of triangular patches. We also propose an algorithm to guide the selection of a proper kernel bandwidth for SDE computation...
November 6, 2018: IEEE Transactions on Visualization and Computer Graphics
Mohammad Raji, Alok Hota, Tanner Hobson, Jian Huang
In this paper, we propose using a decoupled architecture to create a microservice that can deliver scientific visualization remotely with efficiency, scalability, and superior availability, affordability and accessibility. Through our effort, we have created an open source platform, Tapestry, which can be deployed on Amazon AWS as a production use microservice. The applications we use to demonstrate the efficacy of the Tapestry microservice in this work are: (1) embedding interactive visualizations into lightweight web pages, (2) creating scientific visualization movies that are fully controllable by the viewers, (3) serving as a rendering engine for high-end displays such as power-walls, and (4) embedding data-intensive visualizations into augmented reality devices efficiently...
November 5, 2018: IEEE Transactions on Visualization and Computer Graphics
Guanyu Xing, Yanli Liu, Haibin Ling, Xavier Granier, Yanci Zhang
We propose an automatic framework to recover the illumination of indoor scenes based on a single RGB-D image. Unlike previous works, our method can recover spatially varying illumination without using any lighting capturing devices or HDR information. The recovered illumination can produce realistic rendering results. To model the geometry of the visible and invisible parts of scenes corresponding to the input RGB-D image, we assume that all objects shown in the image are located in a box with six faces and build a geometry model based on the depth map...
October 26, 2018: IEEE Transactions on Visualization and Computer Graphics
Haiyong Jiang, Dong-Ming Yan, Xiaopeng Zhang, Peter Wonka
We introduce a new approach for procedural modeling. Our main idea is to select shapes using selection-expressions instead of simple string matching used in current state-of-the-art grammars like CGA shape and CGA++. A selection-expression specifies how to select a potentially complex subset of shapes from a shape hierarchy, e.g. "select all tall windows in the second floor of the main building facade". This new way of modeling enables us to express modeling ideas in their global context rather than traditional rules that operate only locally...
October 23, 2018: IEEE Transactions on Visualization and Computer Graphics
Akhilesh Camisetty, Chaitanya Chandurkar, Maoyuan Sun, David Koop
Visual analytics systems continue to integrate new technologies and leverage modern environments for exploration and collaboration, making tools and techniques available to a wide audience through web browsers. Many of these systems have been developed with rich interactions, offering users the opportunity to examine details and explore hypotheses that have not been directly encoded by a designer. Understanding is enhanced when users can replay and revisit the steps in the sensemaking process, and in collaborative settings, it is especially important to be able to review not only the current state but also what decisions were made along the way...
October 19, 2018: IEEE Transactions on Visualization and Computer Graphics
Petar Pjanic, Simon Willi, Daisuke Iwai, Anselm Grundhofer
This paper introduces a novel photometric compensation technique for inter-projector luminance and chrominance variations. Although it sounds as a classical technical issue, to the best of our knowledge there is no existing solution to alleviate the spatial non-uniformity among strongly heterogeneous projectors at perceptually acceptable quality. Primary goal of our method is increasing the perceived seamlessness of the projection system by automatically generating an improved and consistent visual quality...
October 18, 2018: IEEE Transactions on Visualization and Computer Graphics
Donguk Kim, Mokwon Lee, Youngsong Cho, Deok-Soo Kim
The beta-complex is a construct derived from the Voronoi diagram of spherical balls of arbitrary radii and has proven a powerful capability for proximity reasoning among spherical balls in three-dimensional space. Important applications related to molecular shapes in structural/computational molecular biology have been correctly, efficiently, and conveniently solved in the unified framework of the beta-complex and the Voronoi diagram. The beta-complex is a generalization of the alpha-complex. However, there are similarities and dissimilarities between the two complexes and it is necessary to correctly understand these similarities and dissimilarities to choose the right complex to solve application problems at hand...
October 18, 2018: IEEE Transactions on Visualization and Computer Graphics
Aritra Dasgupta, Jorge Poco, Bernice Rogowitz, Kyungsik Han, Enrico Bertini, Claudio T Silva
Geographical maps encoded with rainbow color scales are widely used for spatial data analysis in climate science, despite evidence from the visualization literature that they are not perceptually optimal. We present a controlled user study that compares the effect of color scales on performance accuracy for climate-modeling tasks using pairs of continuous geographical maps generated using climatological metrics. For each pair of maps, 39 scientist-observers judged: i) the magnitude of their difference, ii) their degree of spatial similarity, and iii) the region of greatest dissimilarity between them...
October 17, 2018: IEEE Transactions on Visualization and Computer Graphics
Hendrik Strobelt, Sebastian Gehrmann, Michael Behrisch, Adam Perer, Hanspeter Pfister, Alexander M Rush
Neural sequence-to-sequence models have proven to be accurate and robust for many sequence prediction tasks, and have become the standard approach for automatic translation of text. The models work with a five-stage blackbox pipeline that begins with encoding a source sequence to a vector space and then decoding out to a new target sequence. This process is now standard, but like many deep learning methods remains quite difficult to understand or debug. In this work, we present a visual analysis tool that allows interaction and "what if"-style exploration of trained sequence-to-sequence models through each stage of the translation process...
October 17, 2018: IEEE Transactions on Visualization and Computer Graphics
Norbert Lindow, Daniel Baum, Morgan Leborgne, Hans-Christian Hege
The analysis and visualization of nucleic acids (RNA and DNA) is playing an increasingly important role due to their fundamental importance for all forms of life and the growing number of known 3D structures of such molecules. The great complexity of these structures, in particular, those of RNA, demands interactive visualization to get deeper insights into the relationship between the 2D secondary structure motifs and their 3D tertiary structures. Over the last decades, a lot of research in molecular visualization has focused on the visual exploration of protein structures while nucleic acids have only been marginally addressed...
October 17, 2018: IEEE Transactions on Visualization and Computer Graphics
Feng Wang, Ingo Wald, Qi Wu, Will Usher, Chris R Johnson
Adaptive mesh refinement (AMR) is a key technology for large-scale simulations that allows for adaptively changing the simulation mesh resolution, resulting in significant computational and storage savings. However, visualizing such AMR data poses a significant challenge due to the difficulties introduced by the hierarchical representation when reconstructing continuous field values. In this paper, we detail a comprehensive solution for interactive isosurface rendering of block-structured AMR data. We contribute a novel reconstruction strategy-the octant method-which is continuous, adaptive and simple to implement...
October 16, 2018: IEEE Transactions on Visualization and Computer Graphics
Yuxin Ma, Anthony K H Tung, Wei Wang, Xiang Gao, Zhigeng Pan, Wei Chen
Similarity measuring methods are widely adopted in a broad range of visualization applications. In this work, we address the challenge of representing human perception in the visual analysis of scatterplots by introducing a novel deep-learning-based approach, ScatterNet, captures perception-driven similarities of such plots. The approach exploits deep neural networks to extract semantic features of scatterplot images for similarity calculation. We create a large labeled dataset consisting of similar and dissimilar images of scatterplots to train the deep neural network...
October 12, 2018: IEEE Transactions on Visualization and Computer Graphics
Wenjia Huang, Demetri Terzopoulos
We introduce a framework for simulating a variety of nontrivial, socially motivated behaviors that underlie the orderly passage of pedestrians through doorways, especially the common courtesy of opening and holding doors open for others, an important etiquette that has been overlooked in the literature on autonomous multi-human animation. Emulating such social activity requires serious attention to the interplay of visual perception, navigation in constrained doorway environments, manipulation of a variety of door types, and high-level decision making based on social considerations...
October 4, 2018: IEEE Transactions on Visualization and Computer Graphics
Hakim Si-Mohammed, Jimmy Petit, Camille Jeunet, Ferran Argelaguet, Fabien Spindler, Andeol Evain, Nicolas Roussel, Gery Casiez, Anatole Lecuyer
Brain-Computer Interfaces (BCIs) enable users to interact with computers without any dedicated movement, bringing new hands-free interaction paradigms. In this paper we study the combination of BCI and Augmented Reality (AR). We first tested the feasibility of using BCI in AR settings based on Optical See-Through Head-Mounted Displays (OST-HMDs). Experimental results showed that a BCI and an OST-HMD equipment (EEG headset and Hololens in our case) are well compatible and that small movements of the head can be tolerated when using the BCI...
October 4, 2018: IEEE Transactions on Visualization and Computer Graphics
Xiaoting Hong, Stephen Brooks
Given the expanding use of 3D Objects in a variety of fields such as animation, gaming, virtual worlds, commerce, augmented reality and 3D printing, we present a novel system for object browsing and searching. Specifically, the system packs objects into an interactive 3D cloud for browsing and searching on mobile devices. It was designed with the aim of increasing search efficiency in a variety of active environments, while providing a visually engaging layout, and we evaluated this by conducting a comparative user study...
October 4, 2018: IEEE Transactions on Visualization and Computer Graphics
Qianwen Chao, Zhigang Deng, Yangxi Xiao, Dunbang He, Qiguang Miao, Xiaogang Jin
Aiming at objectively measuring the realism of virtual traffic flows and evaluating the effectiveness of different traffic simulation techniques, this paper introduces a general, dictionary-based learning method to evaluate the fidelity of any traffic trajectory data. First, a traffic pattern dictionary that characterizes common patterns of real-world traffic behavior is built offline from pre-collected ground truth traffic data. The corresponding learning error is set as the benchmark of the dictionary-based traffic representation...
October 4, 2018: IEEE Transactions on Visualization and Computer Graphics
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