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Continuous Levels-of-Detail and Visual Abstraction for Seamless Molecular Visualization
- Publication Year :
- 2014
-
Abstract
- Molecular visualization is often challenged with rendering of large molecular structures in real time. We introduce a novel approach that enables us to show even large protein complexes. Our method is based on the level-of-detail concept, where we exploit three different abstractions combined in one visualization. Firstly, molecular surface abstraction exploits three different surfaces, solvent-excluded surface (SES), Gaussian kernels and van der Waals spheres, combined as one surface by linear interpolation. Secondly, we introduce three shading abstraction levels and a method for creating seamless transitions between these representations. The SES representation with full shading and added contours stands in focus while on the other side a sphere representation of a cluster of atoms with constant shading and without contours provide the context. Thirdly, we propose a hierarchical abstraction based on a set of clusters formed on molecular atoms. All three abstraction models are driven by one importance function classifying the scene into the near-, mid- and far-field. Moreover, we introduce a methodology to render the entire molecule directly using the A-buffer technique, which further improves the performance. The rendering performance is evaluated on series of molecules of varying atom counts.<br />Funding Agencies|PhysioIllustration research project - Norwegian Research Council [218023]; Vienna Science and Technology Fund (WWTF) [VRG11-010]; EC Marie Curie Career Integration Grant [PCIG13-GA-2013-618680]; Excellence Center at Linkoping and Lund in Information Technology (ELLIIT); Swedish Research Council through the Linnaeus Center for Control, Autonomy, and Decisionmaking in Complex Systems (CADICS); Swedish e-Science Research Centre (SeRC); VR [2011-4113]
Details
- Database :
- OAIster
- Notes :
- application/pdf, English
- Publication Type :
- Electronic Resource
- Accession number :
- edsoai.on1233828908
- Document Type :
- Electronic Resource
- Full Text :
- https://doi.org/10.1111.cgf.12349