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Zhang M, Li Q, Chen L, Yuan X, Yong J. EnConVis: A Unified Framework for Ensemble Contour Visualization. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2023; 29:2067-2079. [PMID: 34982686 DOI: 10.1109/tvcg.2021.3140153] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Ensemble simulation is a crucial method to handle potential uncertainty in modern simulation and has been widely applied in many disciplines. Many ensemble contour visualization methods have been introduced to facilitate ensemble data analysis. On the basis of deep exploration and summarization of existing techniques and domain requirements, we propose a unified framework of ensemble contour visualization, EnConVis (Ensemble Contour Visualization), which systematically combines state-of-the-art methods. We model ensemble contour visualization as a four-step pipeline consisting of four essential procedures: member filtering, point-wise modeling, uncertainty band extraction, and visual mapping. For each of the four essential procedures, we compare different methods they use, analyze their pros and cons, highlight research gaps, and attempt to fill them. Specifically, we add Kernel Density Estimation in the point-wise modeling procedure and multi-layer extraction in the uncertainty band extraction procedure. This step shows the ensemble data's details accurately and provides abstract levels. We also analyze existing methods from a global perspective. We investigate their mechanisms and compare their effects, on the basis of which, we offer selection guidelines for them. From the overall perspective of this framework, we find choices and combinations that have not been tried before, which can be well compensated by our method. Synthetic data and real-world data are leveraged to verify the efficacy of our method. Domain experts' feedback suggests that our approach helps them better understand ensemble data analysis.
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Zhu X, Liu X, Liu S, Shen Y, You L, Wang Y. Robust quasi-uniform surface meshing of neuronal morphology using line skeleton-based progressive convolution approximation. Front Neuroinform 2022; 16:953930. [DOI: 10.3389/fninf.2022.953930] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Accepted: 08/31/2022] [Indexed: 11/13/2022] Open
Abstract
Creating high-quality polygonal meshes which represent the membrane surface of neurons for both visualization and numerical simulation purposes is an important yet nontrivial task, due to their irregular and complicated structures. In this paper, we develop a novel approach of constructing a watertight 3D mesh from the abstract point-and-diameter representation of the given neuronal morphology. The membrane shape of the neuron is reconstructed by progressively deforming an initial sphere with the guidance of the neuronal skeleton, which can be regarded as a digital sculpting process. To efficiently deform the surface, a local mapping is adopted to simulate the animation skinning. As a result, only the vertices within the region of influence (ROI) of the current skeletal position need to be updated. The ROI is determined based on the finite-support convolution kernel, which is convolved along the line skeleton of the neuron to generate a potential field that further smooths the overall surface at both unidirectional and bifurcating regions. Meanwhile, the mesh quality during the entire evolution is always guaranteed by a set of quasi-uniform rules, which split excessively long edges, collapse undersized ones, and adjust vertices within the tangent plane to produce regular triangles. Additionally, the local vertices density on the result mesh is decided by the radius and curvature of neurites to achieve adaptiveness.
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Khan D, Plopski A, Fujimoto Y, Kanbara M, Jabeen G, Zhang YJ, Zhang X, Kato H. Surface Remeshing: A Systematic Literature Review of Methods and Research Directions. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2022; 28:1680-1713. [PMID: 32795969 DOI: 10.1109/tvcg.2020.3016645] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Triangle meshes are used in many important shape-related applications including geometric modeling, animation production, system simulation, and visualization. However, these meshes are typically generated in raw form with several defects and poor-quality elements, obstructing them from practical application. Over the past decades, different surface remeshing techniques have been presented to improve these poor-quality meshes prior to the downstream utilization. A typical surface remeshing algorithm converts an input mesh into a higher quality mesh with consideration of given quality requirements as well as an acceptable approximation to the input mesh. In recent years, surface remeshing has gained significant attention from researchers and engineers, and several remeshing algorithms have been proposed. However, there has been no survey article on remeshing methods in general with a defined search strategy and article selection mechanism covering the recent approaches in surface remeshing domain with a good connection to classical approaches. In this article, we present a survey on surface remeshing techniques, classifying all collected articles in different categories and analyzing specific methods with their advantages, disadvantages, and possible future improvements. Following the systematic literature review methodology, we define step-by-step guidelines throughout the review process, including search strategy, literature inclusion/exclusion criteria, article quality assessment, and data extraction. With the aim of literature collection and classification based on data extraction, we summarized collected articles, considering the key remeshing objectives, the way the mesh quality is defined and improved, and the way their techniques are compared with other previous methods. Remeshing objectives are described by angle range control, feature preservation, error control, valence optimization, and remeshing compatibility. The metrics used in the literature for the evaluation of surface remeshing algorithms are discussed. Meshing techniques are compared with other related methods via a comprehensive table with indices of the method name, the remeshing challenge met and solved, the category the method belongs to, and the year of publication. We expect this survey to be a practical reference for surface remeshing in terms of literature classification, method analysis, and future prospects.
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Visual selection of standard wells for large scale logging data via discrete choice model. Neurocomputing 2021. [DOI: 10.1016/j.neucom.2021.01.105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
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Gui S, Khan D, Wang Q, Yan DM, Lu BZ. Frontiers in biomolecular mesh generation and molecular visualization systems. Vis Comput Ind Biomed Art 2018; 1:7. [PMID: 32240387 PMCID: PMC7099538 DOI: 10.1186/s42492-018-0007-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2018] [Accepted: 07/01/2018] [Indexed: 11/25/2022] Open
Abstract
With the development of biomolecular modeling and simulation, especially implicit solvent modeling, higher requirements are set for the stability, efficiency and mesh quality of molecular mesh generation software. In this review, we summarize the recent works in biomolecular mesh generation and molecular visualization. First, we introduce various definitions of molecular surface and corresponding meshing software. Second, as the mesh quality significantly influences biomolecular simulation, we investigate some remeshing methods in the fields of computer graphics and molecular modeling. Then, we show the application of biomolecular mesh in the boundary element method (BEM) and the finite element method (FEM). Finally, to conveniently visualize the numerical results based on the mesh, we present two types of molecular visualization systems.
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Affiliation(s)
- Sheng Gui
- LSEC, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China.
- University of Chinese Academy of Sciences, Beijing, 100049, China.
| | - Dawar Khan
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Qin Wang
- LSEC, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Dong-Ming Yan
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
| | - Ben-Zhuo Lu
- LSEC, NCMIS, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing, 100190, China
- University of Chinese Academy of Sciences, Beijing, 100049, China
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Zhou Z, Meng L, Tang C, Zhao Y, Guo Z, Hu M, Chen W. Visual Abstraction of Large Scale Geospatial Origin-Destination Movement Data. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2018; 25:43-53. [PMID: 30130199 DOI: 10.1109/tvcg.2018.2864503] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
A variety of human movement datasets are represented in an Origin-Destination(OD) form, such as taxi trips, mobile phone locations, etc. As a commonly-used method to visualize OD data, flow map always fails to discover patterns of human mobility, due to massive intersections and occlusions of lines on a 2D geographical map. A large number of techniques have been proposed to reduce visual clutter of flow maps, such as filtering, clustering and edge bundling, but the correlations of OD flows are often neglected, which makes the simplified OD flow map present little semantic information. In this paper, a characterization of OD flows is established based on an analogy between OD flows and natural language processing (NPL) terms. Then, an iterative multi-objective sampling scheme is designed to select OD flows in a vectorized representation space. To enhance the readability of sampled OD flows, a set of meaningful visual encodings are designed to present the interactions of OD flows. We design and implement a visual exploration system that supports visual inspection and quantitative evaluation from a variety of perspectives. Case studies based on real-world datasets and interviews with domain experts have demonstrated the effectiveness of our system in reducing the visual clutter and enhancing correlations of OD flows.
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Wang Y, Yan DM, Liu X, Tang C, Guo J, Zhang X, Wonka P. Isotropic Surface Remeshing without Large and Small Angles. IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2018; 25:2430-2442. [PMID: 29994531 DOI: 10.1109/tvcg.2018.2837115] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
We introduce a novel algorithm for isotropic surface remeshing which progressively eliminates obtuse triangles and improves small angles. The main novelty of the proposed approach is a simple vertex insertion scheme that facilitates the removal of large angles, and a vertex removal operation that improves the distribution of small angles. In combination with other standard local mesh operators, e.g., connectivity optimization and local tangential smoothing, our algorithm is able to remesh efficiently a low-quality mesh surface. Our approach can be applied directly or used as a post-processing step following other remeshing approaches. Our method has a similar computational efficiency to the fastest approach available, i.e., real-time adaptive remeshing [1]. In comparison with state-of-the-art approaches, our method consistently generates better results based on evaluations using different metrics.
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Khan D, Yan DM, Gui S, Lu B, Zhang X. Molecular Surface Remeshing with Local Region Refinement. Int J Mol Sci 2018; 19:ijms19051383. [PMID: 29734794 PMCID: PMC5983798 DOI: 10.3390/ijms19051383] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2018] [Revised: 04/22/2018] [Accepted: 05/01/2018] [Indexed: 11/23/2022] Open
Abstract
Molecular surface mesh generation is a prerequisite for using the boundary element method (BEM) and finite element method (FEM) in implicit-solvent modeling. Molecular surface meshes typically have small angles, redundant vertices, and low-quality elements. In the implicit-solvent modeling of biomolecular systems it is usually required to improve the mesh quality and eliminate low-quality elements. Existing methods often fail to efficiently remove low-quality elements, especially in complex molecular meshes. In this paper, we propose a mesh refinement method that smooths the meshes, eliminates invalid regions in a cut-and-fill strategy, and improves the minimal angle. We compared our method with four different state-of-the-art methods and found that our method showed a significant improvement over state-of-the-art methods in minimal angle, aspect ratio, and other meshing quality measurements. In addition, our method showed satisfactory results in terms of the ratio of regular vertices and the preservation of area and volume.
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Affiliation(s)
- Dawar Khan
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
- University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Dong-Ming Yan
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
- University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Sheng Gui
- University of Chinese Academy of Sciences, Beijing 100049, China.
- National Center for Mathematics and Interdisciplinary Sciences, State Key Laboratory of Scientific and Engineering Computing, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.
| | - Benzhuo Lu
- University of Chinese Academy of Sciences, Beijing 100049, China.
- National Center for Mathematics and Interdisciplinary Sciences, State Key Laboratory of Scientific and Engineering Computing, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.
| | - Xiaopeng Zhang
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China.
- University of Chinese Academy of Sciences, Beijing 100049, China.
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