Efficient GPU-accelerated computation of isosurface similarity maps

Imre, Martin; Tao, Jun; Wang, Chaoli

doi:10.1109/pacificvis.2017.8031592

Cited by 5 publications

(1 citation statement)

References 12 publications

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“…The efficiency of voxel2vec is high, as voxel2vec is not a deep neural network model (only one hidden layer) and negative sampling is used to select a small number of negative samples (instead of all samples) for fast training. Note that the learning process takes less time than the GPU-accelerated version of the isosurface-based similarity [67] [43] for MANIX in Fig. 3.…”

Section: Discussionmentioning

confidence: 99%

voxel2vec: A Natural Language Processing Approach to Learning Distributed Representations for Scientific Data

He¹,

Tao²,

Shuoliu³

et al. 2022

Preprint

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Relationships in scientific data, such as the numerical and spatial distribution relations of features in univariate data, the scalar-value combinations' relations in multivariate data, and the association of volumes in time-varying and ensemble data, are intricate and complex. This paper presents voxel2vec, a novel unsupervised representation learning model, which is used to learn distributed representations of scalar values/scalar-value combinations in a low-dimensional vector space. Its basic assumption is that if two scalar values/scalar-value combinations have similar contexts, they usually have high similarity in terms of features. By representing scalar values/scalar-value combinations as symbols, voxel2vec learns the similarity between them in the context of spatial distribution and then allows us to explore the overall association between volumes by transfer prediction. We demonstrate the usefulness and effectiveness of voxel2vec by comparing it with the isosurface similarity map of univariate data and applying the learned distributed representations to feature classification for multivariate data and to association analysis for time-varying and ensemble data.

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Section: Discussionmentioning

confidence: 99%

voxel2vec: A Natural Language Processing Approach to Learning Distributed Representations for Scientific Data

He¹,

Tao²,

Shuoliu³

et al. 2022

Preprint

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Visualization Laboratory at University of Notre Dame

Wang

2020

Visual Informatics

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Exploring Time-Varying Multivariate Volume Data Using Matrix of Isosurface Similarity Maps

Tao

Imre

Wang

et al. 2019

IEEE Trans. Visual. Comput. Graphics

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We present a novel visual representation and interface named the matrix of isosurface similarity maps (MISM) for effective exploration of large time-varying multivariate volumetric data sets. MISM synthesizes three types of similarity maps (i.e., self, temporal, and variable similarity maps) to capture the essential relationships among isosurfaces of different variables and time steps. Additionally, it serves as the main visual mapping and navigation tool for examining the vast number of isosurfaces and exploring the underlying time-varying multivariate data set. We present temporal clustering, variable grouping, and interactive filtering to reduce the huge exploration space of MISM. In conjunction with the isovalue and isosurface views, MISM allows users to identify important isosurfaces or isosurface pairs and compare them over space, time, and value range. More importantly, we introduce path recommendation that suggests, animates, and compares traversal paths for effectively exploring MISM under varied criteria and at different levels-of-detail. A silhouette-based method is applied to render multiple surfaces of interest in a visually succinct manner. We demonstrate the effectiveness of our approach with case studies of several time-varying multivariate data sets and an ensemble data set, and evaluate our work with two domain experts.

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Efficient GPU-accelerated computation of isosurface similarity maps

Cited by 5 publications

References 12 publications

voxel2vec: A Natural Language Processing Approach to Learning Distributed Representations for Scientific Data

voxel2vec: A Natural Language Processing Approach to Learning Distributed Representations for Scientific Data

Visualization Laboratory at University of Notre Dame

Exploring Time-Varying Multivariate Volume Data Using Matrix of Isosurface Similarity Maps

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