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IMGD 4000: Computer Graphics in Games

IMGD 4000: Computer Graphics in Games. Emmanuel Agu. Professor Background. Dr. Emmanuel Agu (professor, “Emmanuel”) Research areas Computer Graphics (GPU rendering, mobile graphics, etc) wireless networking and mobile computing Advise MQPs, MS and PhD theses. Graphics Trends for Games.

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IMGD 4000: Computer Graphics in Games

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  1. IMGD 4000: Computer Graphics in Games Emmanuel Agu

  2. Professor Background • Dr. Emmanuel Agu (professor, “Emmanuel”) • Research areas • Computer Graphics (GPU rendering, mobile graphics, etc) • wireless networking and mobile computing • Advise MQPs, MS and PhD theses

  3. Graphics Trends for Games • Hardware GPUs • Capture • Ray tracing

  4. What is Computer Graphics? • Use a computer to create pictures • Started early ’60s: Ivan Sutherland (MIT) • SIGGRAPH conference: • started 1969, about 30,000 annually. E.g. summer 2006: Boston • Attendees: artists and computer scientists • CG has many aspects (technical, art) • Computer Scientists create libraries, tools that artists/non-techies can use to create pretty pictures • Artist uses CG tools to create pretty pictures • Irony: most hobbyists follow artsy path

  5. Computer Graphics • What is Computer graphics? • Algorithms, mathematics, data structures ..… that enable a computer to make PRETTY PICTURES • Main techniques evolved into Graphics libraries • Functions/routines to draw line or circle, etc • Elaborate: pull-down menus, 3D coordinate system, etc • OpenGL and DirectX are one of most popular graphics libraries • Current trend: Implement OpenGL, DirectX on a specialized chip (Graphics Processing Unit (GPU) on your graphics card

  6. Trend 1: Graphics Processing Unit • Powerful and inexpensive: Many FLOPS! • Initially, just hardcode graphics operations onto chip, increase speed • Huge computation ability and bigger growth rate in a consumer graphics card. • Programmable: in recent 2-3 years New operations just added. Possibility to apply to non-graphics application. • Increasing precision

  7. Computational Power • NVIDIA GeForce 7900 GTX ($378) 51.2 GB/sec memory bandwidth; • ATI Radeon X1900 XTX ($355) 240 GFLOPS, both measured with GPUBench. • Dual-core 3.7 GHz Intel Pentium Extreme Edition 965.(Around $1000) 8.5 GB/sec and 25.6 GFLOPS theoretical peak for the SSE units

  8. GPU Computational Power Growth

  9. Nvidia 8800 Block diagram of the GeForce 8800. Source: NVIDIA 681million transistors 1.35GHz 128 stream processors

  10. Programmable GPU? • GPGPU: General-Purpose Computation on GPU. Non-graphics application • Programmable: can hack non-graphics applications onto GPU • Program applications as collection of shaders • GPGPU applications: • Physically based simulation: fluid Dynamics; Cloth simulation, • Signal and Image Processing • Medical imaging • Database query/data mining • Global illumination algorithms: Ray tracing, photon mapping

  11. Why are GPUs getting so fast? • Arithmetic intensity: use more transistors for computation and less for decision logic. • Economics: Demand is high thanks to multi-billion dollar game industry. • More chips produced => lower price • AMD + ATI => XPUs….Cool idea?

  12. Computer Graphics in Games • Elements? • Model geometry • Apply colors, shading • Shadows • Texture mapping • Fog • Transparency and blending • Anti-aliasing Courtesy: Madden NFL game

  13. Trend 2: Capture • Old way: write models, equations to model: • Object geometry, lighting (Phong), animation, etc • New way: capture parameters from real world • Example: motion in most sports games (e.g. NBA 2K live) is captured. • How? Put sensors on actors • Let them play game • Capture their motion • Put motion in a database • Replay database when real players play game

  14. How is capture done? • Capture: • Digitize real object geometry and attributes • Use cameras, computer vision techniques to capture rendering data • Place data in database, many people can re-use • Question: What is computer vision?

  15. Geometry Capture: 3D Scanning • Capturing geometry trend: Projects on precise 3D scanning (Stanford, IBM,etc) produce very large polygonal models • Some models too large to be loaded by most machines Model: David Largest dataset Size: 2 billion polygons, 7000 color images!! Courtesy: Stanford Michael Angelo 3D scanning project

  16. 1. Appearance (volume, scattering, transparency, translucency, etc) Exactly What Can We Capture? 2. Geometry 3. Reflectance & Illumination 4. Motion

  17. Light Probes: Capturing light Amazing graphics, High Dynamic Range?

  18. Why effort to capture? • Big question: If we can capture real world parameters, what advantages does computer graphics have?

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