Image-based Exploration of Iso-surfaces for Large Multi- Variable Datasets using Parameter Space

الموضوعات

علوم تطبيقية وتكنولوجية

الملخص

With an increase in processing power, more complex simulations have resulted in larger data size, with higher resolution and more variables. Many techniques have been developed to help the user to visualize and analyze data from such simulations. However, dealing with a large amount of multivariate data is challenging, time- consuming and often requires high-end clusters. Consequently, novel visualization techniques are needed to explore such data. Many users would like to visually explore their data and change certain visual aspects without the need to use special clusters or having to load a large amount of data. This is the idea behind explorable images (EI). Explorable images are a novel approach that provides limited interactive visualization without the need to re-render from the original data [40]. In this work, the concept of EI has been used to create a workflow that deals with explorable iso-surfaces for scalar fields in a multivariate, time-varying dataset. As a pre-processing step, a set of iso-values for each scalar field is inferred and extracted from a user-assisted sampling technique in time-parameter space. These iso-values are then used to generate iso- surfaces that are then pre-rendered (from a fixed viewpoint) along with additional buffers (i.e. normals, depth, values of other fields, etc.) to provide a compressed representation of iso-surfaces in the dataset. We present a tool that at run-time allows the user to interactively browse and calculate a combination of iso-surfaces superimposed on each other. The result is the same as calculating multiple iso- surfaces from the original data but without the memory and processing overhead. Our tool also allows the user to change the (scalar) values superimposed on each of the surfaces, modify their color map, and interactively re-light the surfaces. We demonstrate the effectiveness of our approach over a multi-terabyte combustion dataset. We also illustrate the efficiency and accuracy of our technique by comparing our results with those from a more traditional visualization pipeline.

روابط وملفات

التعريف والنوع

رقم الوثيقة

f1c8bcdf-c053-4016-921b-3bf772cd7701

رقم العقد

0

نوع الوسائط

Crawler

نوع المحتوى

الرسائل العلمية

صيغة المصدر

رسائل ماجيستير

نوع الملف

pdf text

أسماء الملفات

1700540_1.pdf

بيانات النشر

ألقاب المؤلفين

[{"name_ar":" Markus Hadwiger","title_ar":"اشراف","title_en":"Supervision"},{"name_ar":"Madhu Srinivasan","title_ar":"اشراف","title_en":"Supervision"},{"name_ar":"Roba S. Binyahib","title_ar":"اعداد","title_en":"Preparation"}]

اللغة

English

المصدر والدورية

اسم المصدر

Image-based Exploration of Iso-surfaces for Large Multi- Variable Datasets using Parameter Space

المحتوى والصفحات

عدد الصفحات

0

كلمات الباحثين

deep images , deferred shading , explorable images , image-based rendering , iso-surfaces , remote visualization

إشراف وإعداد

الإشراف

Markus Hadwiger, Madhu Srinivasan

الإعداد

Roba S. Binyahib

الاقتباسات الببليوغرافية