We present a method that transforms an unstructured vector design into a logical hierarchy of groups of objects. Each group is a meaningful collection, formed by proximity in visual characteristics (like size, shape, color, etc.) and spatial location of objects and models the grouping principles designers use. We first simplify the input design by partially or completely flattening it and isolate duplicate geometries in the design (for example, repeating patterns due to copy and paste operations). Next we build the object containment hierarchy by assigning objects that are wholly enclosed inside the geometry of other objects as children of the enclosing parent. In the final clustering phase, we use agglomerative clustering to obtain a bottom-up hierarchical grouping of all objects by comparing and ranking all pairs of objects according to visual and spatial characteristics. Spatial proximity segregates far apart objects, but when they are identical (or near identical) designers generally prefer to keep (and edit) them together. To accommodate this, we detect near identical objects and group them together during clustering despite their spatial separation. We further restrict group formation so that z-order disturbances in the design keep the visual appearance unaffected for tightly-overlapping geometry. The generated organization is equivalent to the original design and the organization results are used to facilitate abstract navigation (hierarchical, lateral or near similar) and selections in the design. Our technique works well with a variety of input designs with commonly identifiable objects and structural patterns. CCS Concepts • Applied computing → Document analysis; • Information systems → Clustering;
{"title":"Automatic Hierarchical Arrangement of Vector Designs","authors":"Matthew Fisher, V. Agarwal, T. Beri","doi":"10.2312/EGS.20211016","DOIUrl":"https://doi.org/10.2312/EGS.20211016","url":null,"abstract":"We present a method that transforms an unstructured vector design into a logical hierarchy of groups of objects. Each group is a meaningful collection, formed by proximity in visual characteristics (like size, shape, color, etc.) and spatial location of objects and models the grouping principles designers use. We first simplify the input design by partially or completely flattening it and isolate duplicate geometries in the design (for example, repeating patterns due to copy and paste operations). Next we build the object containment hierarchy by assigning objects that are wholly enclosed inside the geometry of other objects as children of the enclosing parent. In the final clustering phase, we use agglomerative clustering to obtain a bottom-up hierarchical grouping of all objects by comparing and ranking all pairs of objects according to visual and spatial characteristics. Spatial proximity segregates far apart objects, but when they are identical (or near identical) designers generally prefer to keep (and edit) them together. To accommodate this, we detect near identical objects and group them together during clustering despite their spatial separation. We further restrict group formation so that z-order disturbances in the design keep the visual appearance unaffected for tightly-overlapping geometry. The generated organization is equivalent to the original design and the organization results are used to facilitate abstract navigation (hierarchical, lateral or near similar) and selections in the design. Our technique works well with a variety of input designs with commonly identifiable objects and structural patterns. CCS Concepts • Applied computing → Document analysis; • Information systems → Clustering;","PeriodicalId":72958,"journal":{"name":"Eurographics ... Workshop on 3D Object Retrieval : EG 3DOR. Eurographics Workshop on 3D Object Retrieval","volume":"195 1","pages":"29-32"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78995578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Florent Langenfeld, Tunde Aderinwale, Charles W Christoffer, Woong-Hee Shin, Genki Terashi, Xiao Wang, D. Kihara, H. Benhabiles, K. Hammoudi, A. Cabani, Féryal Windal, Mahmoud Melkemi, Ekpo Otu, R. Zwiggelaar, David Hunter, Yonghuai Liu, Léa Sirugue, Huu-Nghia H. Nguyen, Tuan-Duy H. Nguyen, Vinh-Thuyen Nguyen-Truong, D. Le, Hai-Dang Nguyen, M. Tran, M. Montès
Proteins are essential to nearly all cellular mechanism, and often interact through their surface with other cell molecules, such as proteins and ligands. The evolution generates plenty of different proteins, with unique abilities, but also proteins with related functions hence surface, which is therefore of primary importance for their activity. In the present work, we assess the ability of five methods to retrieve similar protein surfaces, using either their shape only (3D meshes), or their shape and the electrostatic potential at their surface, an important surface property. Five different groups participated in this challenge using the shape only, and one group extended its pre-existing algorithm to handle the electrostatic potential. The results reveal both the ability of the methods to detect related proteins and their difficulties to distinguish between topologically related proteins. CCS Concepts • Applied computing → Computational biology; • General and reference → Evaluation;
{"title":"SHREC 2021: Surface-based Protein Domains Retrieval","authors":"Florent Langenfeld, Tunde Aderinwale, Charles W Christoffer, Woong-Hee Shin, Genki Terashi, Xiao Wang, D. Kihara, H. Benhabiles, K. Hammoudi, A. Cabani, Féryal Windal, Mahmoud Melkemi, Ekpo Otu, R. Zwiggelaar, David Hunter, Yonghuai Liu, Léa Sirugue, Huu-Nghia H. Nguyen, Tuan-Duy H. Nguyen, Vinh-Thuyen Nguyen-Truong, D. Le, Hai-Dang Nguyen, M. Tran, M. Montès","doi":"10.2312/3DOR.20211308","DOIUrl":"https://doi.org/10.2312/3DOR.20211308","url":null,"abstract":"Proteins are essential to nearly all cellular mechanism, and often interact through their surface with other cell molecules, such as proteins and ligands. The evolution generates plenty of different proteins, with unique abilities, but also proteins with related functions hence surface, which is therefore of primary importance for their activity. In the present work, we assess the ability of five methods to retrieve similar protein surfaces, using either their shape only (3D meshes), or their shape and the electrostatic potential at their surface, an important surface property. Five different groups participated in this challenge using the shape only, and one group extended its pre-existing algorithm to handle the electrostatic potential. The results reveal both the ability of the methods to detect related proteins and their difficulties to distinguish between topologically related proteins. CCS Concepts • Applied computing → Computational biology; • General and reference → Evaluation;","PeriodicalId":72958,"journal":{"name":"Eurographics ... Workshop on 3D Object Retrieval : EG 3DOR. Eurographics Workshop on 3D Object Retrieval","volume":"19 1","pages":"19-26"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87468730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zhilin Cai, Yang Zhang, Marco Manzi, A. C. Öztireli, M. Gross, T. Aydin
We present a new method for designing high quality denoisers that are robust to varying noise characteristics of input images. Instead of taking a conventional blind denoising approach or relying on explicit noise parameter estimation networks as well as invertible camera imaging pipeline models, we propose a two-stage model that first processes an input image with a small set of specialized denoisers, and then passes the resulting intermediate denoised images to a kernel predicting network that estimates per-pixel denoising kernels. We demonstrate that our approach achieves robustness to noise parameters at a level that exceeds comparable blind denoisers, while also coming close to state-of-the-art denoising quality for camera sensor noise. CCS Concepts • Computing methodologies → Image processing;
{"title":"Robust Image Denoising using Kernel Predicting Networks","authors":"Zhilin Cai, Yang Zhang, Marco Manzi, A. C. Öztireli, M. Gross, T. Aydin","doi":"10.2312/EGS.20211018","DOIUrl":"https://doi.org/10.2312/EGS.20211018","url":null,"abstract":"We present a new method for designing high quality denoisers that are robust to varying noise characteristics of input images. Instead of taking a conventional blind denoising approach or relying on explicit noise parameter estimation networks as well as invertible camera imaging pipeline models, we propose a two-stage model that first processes an input image with a small set of specialized denoisers, and then passes the resulting intermediate denoised images to a kernel predicting network that estimates per-pixel denoising kernels. We demonstrate that our approach achieves robustness to noise parameters at a level that exceeds comparable blind denoisers, while also coming close to state-of-the-art denoising quality for camera sensor noise. CCS Concepts • Computing methodologies → Image processing;","PeriodicalId":72958,"journal":{"name":"Eurographics ... Workshop on 3D Object Retrieval : EG 3DOR. Eurographics Workshop on 3D Object Retrieval","volume":"40 1","pages":"37-40"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83258805","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I. Evangelou, N. Vitsas, Georgios Papaioannou, Manolis Georgioudakis, A. Chatzisymeon
The Building Information Modelling (BIM) procedure introduces specifications and data exchange formats widely used by the construction industry to describe functional and geometric elements of building structures in the design, planning, cost estimation and construction phases of large civil engineering projects. In this paper we explain how to apply a modern, low-parameter, neural-network-based classification solution to the automatic geometric BIM element labeling, which is becoming an increasingly important task in software solutions for the construction industry. The network is designed so that it extracts features regarding general shape, scale and aspect ratio of each BIM element and be extremely fast during training and prediction. We evaluate our network architecture on a real BIM dataset and showcase accuracy that is difficult to achieve with a generic 3D shape classification network. CCS Concepts • Computing methodologies → Neural networks; Shape analysis;
{"title":"Shape Classification of Building Information Models using Neural Networks","authors":"I. Evangelou, N. Vitsas, Georgios Papaioannou, Manolis Georgioudakis, A. Chatzisymeon","doi":"10.2312/3DOR.20211306","DOIUrl":"https://doi.org/10.2312/3DOR.20211306","url":null,"abstract":"The Building Information Modelling (BIM) procedure introduces specifications and data exchange formats widely used by the construction industry to describe functional and geometric elements of building structures in the design, planning, cost estimation and construction phases of large civil engineering projects. In this paper we explain how to apply a modern, low-parameter, neural-network-based classification solution to the automatic geometric BIM element labeling, which is becoming an increasingly important task in software solutions for the construction industry. The network is designed so that it extracts features regarding general shape, scale and aspect ratio of each BIM element and be extremely fast during training and prediction. We evaluate our network architecture on a real BIM dataset and showcase accuracy that is difficult to achieve with a generic 3D shape classification network. CCS Concepts • Computing methodologies → Neural networks; Shape analysis;","PeriodicalId":72958,"journal":{"name":"Eurographics ... Workshop on 3D Object Retrieval : EG 3DOR. Eurographics Workshop on 3D Object Retrieval","volume":"3 1","pages":"1-4"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74681782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
F. Ganglberger, J. Kaczanowska, W. Haubensak, K. Bühler
Advances in high-throughput imaging techniques enable the creation of networks depicting spatio-temporal biological and neurophysiological processes with unprecedented size and magnitude. These networks involve thousands of nodes, which can not be compared over time by traditional methods due to complexity and clutter. When investigating networks over multiple time steps, a crucial question for the visualisation research community becomes apparent: How to visually trace changes of the connectivity over several transitions? Therefore, we developed an easy-to-use method that maps multiple networks to a common embedding space. Visualising the distribution of node-clusters of interest (e.g. brain regions) enables their tracing over time. We demonstrate this approach by visualizing spatial co-evolution networks of different evolutionary timepoints as small multiples to investigate how the human brain genetically and functionally evolved over the mammalian lineage.
{"title":"Visualising the Transition of Large Networks via Dimensionality Reduction to Illustrate the Evolution of the Human Brain","authors":"F. Ganglberger, J. Kaczanowska, W. Haubensak, K. Bühler","doi":"10.2312/EGS.20211014","DOIUrl":"https://doi.org/10.2312/EGS.20211014","url":null,"abstract":"Advances in high-throughput imaging techniques enable the creation of networks depicting spatio-temporal biological and neurophysiological processes with unprecedented size and magnitude. These networks involve thousands of nodes, which can not be compared over time by traditional methods due to complexity and clutter. When investigating networks over multiple time steps, a crucial question for the visualisation research community becomes apparent: How to visually trace changes of the connectivity over several transitions? Therefore, we developed an easy-to-use method that maps multiple networks to a common embedding space. Visualising the distribution of node-clusters of interest (e.g. brain regions) enables their tracing over time. We demonstrate this approach by visualizing spatial co-evolution networks of different evolutionary timepoints as small multiples to investigate how the human brain genetically and functionally evolved over the mammalian lineage.","PeriodicalId":72958,"journal":{"name":"Eurographics ... Workshop on 3D Object Retrieval : EG 3DOR. Eurographics Workshop on 3D Object Retrieval","volume":"125 1","pages":"21-24"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83720674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dong-Hoon Han, Changyoon Lee, Sangbin Lee, Hyeongseok Ko
When simulating thin deformable objects such as clothes, collision detection alone takes a lot of computation. One way of reducing the computation is culling false-positives as much as possible. In the context of bounding volume hierarchy, Provot proposed a culling method that is based on hierarchical merging of normal enclosing cones. In this work, we investigate Provot’s merging algorithm and show that there is some room for improvement. We propose a new merging algorithm, in the context of discrete collision detection, which always produces an equal or tighter mergence than Provot’s merging. We extend the above algorithm so that it can be used in the context of continuous collision detection. Experiments show that the proposed method makes about 25% reduction in the number of triangle pairs for which vertex-triangle or edge-edge collision test has to be performed, and 18% reduction in time for collision detection. CCS Concepts • Computing methodologies → Collision detection;
{"title":"Tight Normal Cone Merging for Efficient Collision Detection of Thin Deformable Objects","authors":"Dong-Hoon Han, Changyoon Lee, Sangbin Lee, Hyeongseok Ko","doi":"10.2312/EGS.20211021","DOIUrl":"https://doi.org/10.2312/EGS.20211021","url":null,"abstract":"When simulating thin deformable objects such as clothes, collision detection alone takes a lot of computation. One way of reducing the computation is culling false-positives as much as possible. In the context of bounding volume hierarchy, Provot proposed a culling method that is based on hierarchical merging of normal enclosing cones. In this work, we investigate Provot’s merging algorithm and show that there is some room for improvement. We propose a new merging algorithm, in the context of discrete collision detection, which always produces an equal or tighter mergence than Provot’s merging. We extend the above algorithm so that it can be used in the context of continuous collision detection. Experiments show that the proposed method makes about 25% reduction in the number of triangle pairs for which vertex-triangle or edge-edge collision test has to be performed, and 18% reduction in time for collision detection. CCS Concepts • Computing methodologies → Collision detection;","PeriodicalId":72958,"journal":{"name":"Eurographics ... Workshop on 3D Object Retrieval : EG 3DOR. Eurographics Workshop on 3D Object Retrieval","volume":"19 1","pages":"49-52"},"PeriodicalIF":0.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84021494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-03DOI: 10.14324/111.444.2396-9008.1168
Object Editorial Team
{"title":"Contributors","authors":"Object Editorial Team","doi":"10.14324/111.444.2396-9008.1168","DOIUrl":"https://doi.org/10.14324/111.444.2396-9008.1168","url":null,"abstract":"","PeriodicalId":72958,"journal":{"name":"Eurographics ... Workshop on 3D Object Retrieval : EG 3DOR. Eurographics Workshop on 3D Object Retrieval","volume":"40 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84983569","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-12-03DOI: 10.14324/111.444.2396-9008.1167
Object Editorial Team
{"title":"Theses in Progress","authors":"Object Editorial Team","doi":"10.14324/111.444.2396-9008.1167","DOIUrl":"https://doi.org/10.14324/111.444.2396-9008.1167","url":null,"abstract":"","PeriodicalId":72958,"journal":{"name":"Eurographics ... Workshop on 3D Object Retrieval : EG 3DOR. Eurographics Workshop on 3D Object Retrieval","volume":"128 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2020-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83292250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Dan Koschier, Jan Bender, B. Solenthaler, M. Teschner
Graphics research on Smoothed Particle Hydrodynamics (SPH) has produced fantastic visual results that are unique across the board of research communities concerned with SPH simulations. Generally, the SPH formalism serves as a spatial discretization technique, commonly used for the numerical simulation of continuum mechanical problems such as the simulation of fluids, highly viscous materials, and deformable solids. Recent advances in the field have made it possible to efficiently simulate massive scenes with highly complex boundary geometries on a single PC [Com16b, Com16a]. Moreover, novel techniques allow to robustly handle interactions among various materials [Com18,Com17]. As of today, graphics-inspired pressure solvers, neighborhood search algorithms, boundary formulations, and other contributions often serve as core components in commercial software for animation purposes [Nex17] as well as in computer-aided engineering software [FIF16]. This tutorial covers various aspects of SPH simulations. Governing equations for mechanical phenomena and their SPH discretizations are discussed. Concepts and implementations of core components such as neighborhood search algorithms, pressure solvers, and boundary handling techniques are presented. Implementation hints for the realization of SPH solvers for fluids, elastic solids, and rigid bodies are given. The tutorial combines the introduction of theoretical concepts with the presentation of actual implementations.
{"title":"Smoothed Particle Hydrodynamics Techniques for the Physics Based Simulation of Fluids and Solids","authors":"Dan Koschier, Jan Bender, B. Solenthaler, M. Teschner","doi":"10.2312/egt.20191035","DOIUrl":"https://doi.org/10.2312/egt.20191035","url":null,"abstract":"Graphics research on Smoothed Particle Hydrodynamics (SPH) has produced fantastic visual results that are unique across the board of research communities concerned with SPH simulations. Generally, the SPH formalism serves as a spatial discretization technique, commonly used for the numerical simulation of continuum mechanical problems such as the simulation of fluids, highly viscous materials, and deformable solids. Recent advances in the field have made it possible to efficiently simulate massive scenes with highly complex boundary geometries on a single PC [Com16b, Com16a]. Moreover, novel techniques allow to robustly handle interactions among various materials [Com18,Com17]. As of today, graphics-inspired pressure solvers, neighborhood search algorithms, boundary formulations, and other contributions often serve as core components in commercial software for animation purposes [Nex17] as well as in computer-aided engineering software [FIF16]. \u0000This tutorial covers various aspects of SPH simulations. Governing equations for mechanical phenomena and their SPH discretizations are discussed. Concepts and implementations of core components such as neighborhood search algorithms, pressure solvers, and boundary handling techniques are presented. Implementation hints for the realization of SPH solvers for fluids, elastic solids, and rigid bodies are given. The tutorial combines the introduction of theoretical concepts with the presentation of actual implementations.","PeriodicalId":72958,"journal":{"name":"Eurographics ... Workshop on 3D Object Retrieval : EG 3DOR. Eurographics Workshop on 3D Object Retrieval","volume":"62 1 1","pages":"1-41"},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85053927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Visualization literacy, the ability to interpret and understand visual designs, has gained momentum in the educational and information visualization communities. The goal of this research is to identify and address barriers to treemap literacy – a popular visual design, with a view to improve a non-expert user’s treemap visualization literacy skills. In this paper we present the results of two years of an information visualization assignment, which are used to identify the barriers to and challenges of understanding and creating treemaps. From this, we develop a treemap visualization literacy test. Then, we propose a pedagogical tool that facilitates both teaching and learning of treemaps and advances treemap visualization literacy. To investigate the efficiency of this educational software, we then conduct a classroom-based study with 25 participants. We identify the properties of treemaps that can hinder literacy and cognition based on the results from the treemap visualization literacy test. Results also provide further support for the use of our tool that had a positive effect on treemap literacy skills of university students.
{"title":"Treemap Literacy: A Classroom-Based Investigation","authors":"Elif E. Firat, A. Denisova, R. Laramee","doi":"10.2312/eged.20201032","DOIUrl":"https://doi.org/10.2312/eged.20201032","url":null,"abstract":"Visualization literacy, the ability to interpret and understand visual designs, has gained momentum in the educational and information visualization communities. The goal of this research is to identify and address barriers to treemap literacy – a popular visual design, with a view to improve a non-expert user’s treemap visualization literacy skills. In this paper we present the results of two years of an information visualization assignment, which are used to identify the barriers to and challenges of understanding and creating treemaps. From this, we develop a treemap visualization literacy test. Then, we propose a pedagogical tool that facilitates both teaching and learning of treemaps and advances treemap visualization literacy. To investigate the efficiency of this educational software, we then conduct a classroom-based study with 25 participants. We identify the properties of treemaps that can hinder literacy and cognition based on the results from the treemap visualization literacy test. Results also provide further support for the use of our tool that had a positive effect on treemap literacy skills of university students.","PeriodicalId":72958,"journal":{"name":"Eurographics ... Workshop on 3D Object Retrieval : EG 3DOR. Eurographics Workshop on 3D Object Retrieval","volume":"211 12 1","pages":"29-38"},"PeriodicalIF":0.0,"publicationDate":"2020-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76878927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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