CITRIS Founding Corporate Members Meeting

Transcription

1 Massive Data Exploration Virtual Reality and Visualization Oliver Staadt, Bernd Hamann CIPIC and CS Department, UC Davis CITRIS Founding Corporate Members Meeting Thursday, February 27, 2003 University of California Berkeley University of California Davis University of California Merced University of California Santa Cruz FCM MEETING 1

2 CIPIC Visualization and Graphics Part of the Center for Image Processing and Integrated Computing Seven faculty from three departments within College of Engineering at UC Davis Current research areas in Graphics and Visualization Scientific visualization Medical data visualization Vector field visualization Geometric modeling Computer aided design (CAD) Animation Virtual reality and immersive environments Parallel/distributed computing for visualization Current research areas (cont d) Computational geometry Network security data visualization User interface technology for data visualization Photorealistic and nonphotorealistic rendering methods Computer architectures for graphics Rendering algorithms More information: Come and see our posters! FCM MEETING 2

3 Massive Data Exploration The Challenge Massive amounts of data acquired by millions of multimodal sensors embedded in civil infrastructure Exploration for multiple purposes Traffic flow monitoring Behavior of structures during earthquakes Environmental monitoring (water, air, land) Crisis management Homeland security Automatic filtering and compression of data Real time visualization for different target groups Decision and policy makers Emergency response teams Civil engineers Major technological challenges! FCM MEETING 3

4 The Technological Challenges Today s technology cannot visualize massive dynamic data in real time Automatically reduce data volume and preserve relevant characteristics Develop visualization systems providing support for distributed collaborative data exploration FCM MEETING 4

5 Collaborative Visualization Connection of multiple data exploration and visualization centers Collaborative data exploration by interdisciplinary expert teams FCM MEETING 5

6 Distributed Environment Data Exploration Centers Visualization Portals (blue ETHZ) PDAs CITRISnet Sensors FCM MEETING 6

7 Activities & Contributions I Compression of massive data streams supporting analysis at multiple levels of abstraction quantitative / qualitative Parallel and distributed computing in support of scalable, multiresolution based data exploration techniques Efficient and automatic feature extraction Interactive visualization real time FCM MEETING 7

8 Activities & Contributions II Visualization in immersive and non immersive three dimensional environments Techniques for large, room size display walls Scalable techniques for virtual environments Hybrid display environments virtual environments, augmented virtuality, augmented reality, Collaborative visualization for distributed viewing environments FCM MEETING 8

9 VR based Remote Data Visualization Collaborative virtual reality techniques necessary to naturally interact with 3D, dynamic data streams Using CITRISnet to connect data centers and portals Portals to concentrate information and provide multiple views of data Using wireless connections between small devices and visualization centers Multiple groups interacting with data in real time Davis Berkeley CITRISnet Merced Santa Cruz FCM MEETING 9

10 Visualization of Sensor Network Data Sensor Networks: Self organizing networks of independent small sensors ( smart dust ) Can be deployed almost anywhere: In the environment (a la movie Twister ) Inside smart buildings Can measure almost anything: Wind velocities Temperature Presence of hazardous substances Structural damage in buildings FCM MEETING 10

11 Sensor Data Reconstruction Sensors deliver point sampled measurements, i.e., measures continuous fields at discrete points only To analyze sensor data, original fields must be reconstructed Scattered data methods can estimate pointsampled fields at any position in space, and are the basis for sensor data visualization Several methods exist; quality and performance can be traded off on a per application basis FCM MEETING 11

12 Scattered Data: From Points FCM MEETING 12

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14 To Pictures FCM MEETING 14

15 Smart Buildings Sensor data analysis inside buildings poses additional problems not addressed by classical scattered data methods Architecture must be considered during reconstruction: Walls Air ducts Doors New methods that respect these boundary conditions must be developed FCM MEETING 15

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17 CITRIS Research at DAVIS: Visualization and Virtual Reality Flow Visualization Flow Visualization Semi Immersive Visualization Immersive Collaborative Environments Medical Visualization FCM MEETING 17