January 5, 2006

Vermelding onderdeel organisatie

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Feature Tracking in VR for Cumulus Cloud Life-Cycle Studies

E. J. Griffith, F. H. Post, M. Koutek, T. Heus and H. J. J. Jonker

11th Eurographics Workshop on Virtual Environments

Eric Griffith

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Motivation – Data Visualization

• “The purpose of computation is insight, not numbers.”- Richard Hamming

• Insight is facilitated by a human in the loop• Data visualization puts the human in the loop

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0.9660.8660.7070.50.2590

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Motivation – Virtual Reality

• Why data visualization in VR?• An extra visible

dimension• Improved perception

• High interactivity• Dynamic viewpoint• Dynamic data view

• Simplified, direct interaction with 3D data

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Overview

1) Project Overview2) Data Preprocessing3) Interactive Visualization4) Results5) Conclusions

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Overview

I. Project OverviewII. Data PreprocessingIII. Interactive VisualizationIV. ResultsV. Conclusions

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Project Overview

• Cumulus cloud studies• Largest source of uncertainty in climate

models• Use LES to explore cloud behavior in 3D• Provide a clearer description of cloud

dynamics• Exploration in VR• Interactive visualization of very large, time

dependent data sets• Meaningful visual representations of

multivariate, 3D, time-dependent data sets

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Large Eddy Simulation - LES

• Provides insight into flows in the Atmospheric Boundary Layer• Solves for large-scale flows models small scale flows

• Used to simulate atmospheric conditions over tropical ocean• Typically 6.4 x 6.4 x 3.2 km domains with 2 second

time steps• Grid sizes of 2563 produce 112 GB of data for 1

simulated hour

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Cloud Life-Cycles

• What is the “typical” cloud life-cycle?• How do the clouds behave in each stage of the life-cycle?• What causes the transition between stages?

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The First Step – Cloud Selection

• Suitable clouds must be selected for study

• The challenges:• Very large data sets• Unpredictable and

dynamic cloud behavior• Qualitative selection

criteria

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Overview

I. Project OverviewII. Data PreprocessingIII. Interactive VisualizationIV. ResultsV. Conclusions

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Data Preprocessing

• The objective of preprocessing is to convert the data into a format that can be meaningfully visualized interactively

• It is divided into two stages:• Cloud tracking• Isosurface creation

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Cloud Tracking

• Cloud tracking involves the detection and tracking of features, which are clouds• Tracking is simplified through two key

observations:• Clouds move and develop slowly• Clouds that merge or split are either the

same cloud or uninteresting to atmospheric scientists

• Detection is combined with tracking, and they are done via a 4D connected components algorithm

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Cloud Tracking

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Cloud Tracking

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Cloud Tracking

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Cloud Tracking

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Cloud Tracking

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Cloud Tracking

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Isosurface Creation

• Isosurface creation transforms the raw data into visually meaningful representations of the clouds

• The creation follows a 5 stage pipeline process

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Isosurface Pipeline

1. Prepare the data for isosurface creation2. Create initial triangle meshes with Marching Cubes3. Identify which meshes correspond with which clouds4. Refine the meshes with a series of filters5. Convert the meshes into triangle strips

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Isosurface Pipeline

1. Prepare the data for isosurface creation2. Create initial triangle meshes with Marching Cubes3. Identify which meshes correspond with which clouds4. Refine the meshes with a series of filters5. Convert the meshes into triangle strips

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Isosurface Pipeline

1. Prepare the data for isosurface creation2. Create initial triangle meshes with Marching Cubes3. Identify which meshes correspond with which clouds4. Refine the meshes with a series of filters5. Convert the meshes into triangle strips

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Isosurface Pipeline

1. Prepare the data for isosurface creation2. Create initial triangle meshes with Marching Cubes3. Identify which meshes correspond with which clouds4. Refine the meshes with a series of filters5. Convert the meshes into triangle strips

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Isosurface Pipeline

1. Prepare the data for isosurface creation2. Create initial triangle meshes with Marching Cubes3. Identify which meshes correspond with which clouds4. Refine the meshes with a series of filters5. Convert the meshes into triangle strips

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Overview

I. Project OverviewII. Data PreprocessingIII. Interactive VisualizationIV. ResultsV. Conclusions

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Cloud Explorer

• Cloud Explorer is our prototype of a VR visualization environment for cloud data

• It has been designed to enable atmospheric scientists to identify suitable clouds for study

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Cloud Explorer

• Cloud Explorer components:

a) Cloud fieldb) Volume graphc) World-in-Miniatured) Buttonse) Time control panel

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Cloud Explorer

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Overview

I. Project OverviewII. Data PreprocessingIII. Interactive VisualizationIV. ResultsV. Conclusions

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Results – Data Preprocessing

•The preprocessing results are in terms of cpu time, output size and composition, and data compression•The data sets were processed

twice: once for all clouds and once for “complete” clouds

254.16 GB

42.36 GB

(256 x 256 x 160) x 2169

Total Size

ql SizeGrid Size x Time Steps

1011 MB1600 MB2h 4m 58s

1836 MB2557 MB11h 38m

22s

Avgerage Memory Usage

Peak Memory Usage

CPU Time

64 : 1384 :

1679 MB

46,824,994

6 : 136 : 16.9 GB517,110,15

2

Data Compression Ratios (vs ql)

Output Size

Number of Triangles

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Results – Cloud Explorer

• Depending on the number and size of visible clouds, Cloud Explorer provides frame rates between 20 and 30 FPS in stereo

• Currently, a session with Cloud Explorer results in the set of grid cells for each interesting cloud

• This data is then post processed to analyze the properties of the selected clouds• “Fluffiness”, velocity profiles, mass flux, etc

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Results – Cloud Mass Flux

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Overview

I. Project OverviewII. Data PreprocessingIII. Interactive VisualizationIV. ResultsV. Conclusions

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Conclusions

• Data preprocessing can provide sufficient data reduction to allow interactive VR visualization

• VR visualization enables scientists to combine theoretical and observational considerations

• Feature tracking with VR visualization can help scientists make sense out of very large, time-dependent data sets• In depth studies of cloud behavior are made

possible through cloud selection in VR

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Future Directions

• The primary goal is to increase the amount and quality of time atmospheric scientists can spend using Cloud Explorer for cloud studies• Large data handling facilities (e.g. out-of-core

and multiresolution) for arbitrarily sized data sets

• Enhanced selection and interaction methods• Visual information about multiple variables• More context information about selected

clouds

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Acknowledgements

• Netherlands Organization for Scientific Research

• Netherlands National Computer Facility at SARA• René Molenaar

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Questions