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CS 395: Adv. Computer Graphics

CS 395: Adv. Computer Graphics. Image-Based Modeling and Rendering Jack Tumblin jet@cs.northwestern.edu. GOAL: First-Class Primitive. Want images as ‘first-class’ primitives Useful as BOTH input and output Convert to/from traditional scene descriptions

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CS 395: Adv. Computer Graphics

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  1. CS 395: Adv. Computer Graphics Image-Based Modeling and Rendering Jack Tumblin jet@cs.northwestern.edu

  2. GOAL: First-Class Primitive • Want images as ‘first-class’ primitives • Useful as BOTH input and output • Convert to/from traditional scene descriptions • Want to mix real & synthetic scenes freely • Want to extend photography • Easily capture scene:shape, movement, surface/BRDF, lighting … • Modify & Render the captured scene data • “You can’t always get what you want”–(Mick Jagger 1968)

  3. Back To Basics: Scene & Image Light + 3D Scene: Illumination, shape, movement, surface BRDF,… 2D Image: Collection of rays through a point Image Plane I(x,y) Position(x,y) Angle(,)

  4. Trad. Computer Graphics Light + 3D Scene: Illumination, shape, movement, surface BRDF,… 2D Image: Collection of rays through a point Reduced, Incomplete Information Image Plane I(x,y) Position(x,y) Angle(,)

  5. Trad. Computer Vision Light + 3D Scene: Illumination, shape, movement, surface BRDF,… 2D Image: Collection of rays through a point !TOUGH! ‘ILL-POSED’ Many Simplifications, External knowledge… Image Plane I(x,y) Position(x,y) Angle(,)

  6. Plenoptic Function (Adelson, Bergen `91) • for a given scene, describe: • ALLrays through • ALLpixels, of • ALL cameras, at • ALL wavelengths, • ALL time F(x,y,z,,,, t) “Eyeballs Everywhere” function (7-D!) … … … … … … … … … … …

  7. A Big Plenoptic Question: Image entraps a partial scene description,… • Computer Vision problem: 3D->2D • Image point  scene surface point (usually) • Occlusion hides some scene surfaces • (BRDF * irradiance) tough to split apart! ? Does Plenoptic fcn. contain full scene ? • Exhaustive record of all image rays • Even SIMPLEST scene is huge, redundant, • The ‘consequences’ of all possible renderings* so

  8. ‘Scene’ causes Light Field Light field: holds all outgoing light rays Shape, Position, Movement, Emitted Light Reflected, Scattered, Light … BRDF, Texture, Scattering Scene modulates outgoing light; light field captures it all.

  9. A Big Plenoptic Question: Image entraps a partial scene description • Many-to-One map; 3D->2D • Occlusion hides some scene features • (BRDF * irradiance) tough to split! • limited resolution ? Does Plenoptic fcn. contain full scene ? Two Options for IBMR methods: • Find a limited subset of scene info, • Use MORE than plenoptic function data: (vary lights, etc.) !NO!

  10. 8-to-10-Dimensional Ideal? Light field(4D) + light sources(4D) + time +  Emitted Light Shape, Position, Movement, Reflected, Scattered, Light … BRDF, Texture, Scattering

  11. It gets worse… A ‘Circular problem’: PLUS! depth-of-focus, sampling, indirect illum… SurfaceNormal BRDF Shape Irradiance

  12. Practical IBMR What useful partial solutions are possible? • Texture Maps++: • Image(s)+Depth: (3D shell) • Estimating Depth & Silhouettes • ‘Light Probe’ measures real-world light • Light control measures BRDF • Hybrids: BTF, stitching, …

  13. Texture Maps ++ Re-use rendering results: ‘Impostors’, ‘Billboards’, ‘3D sprites’ • Render portion of scene as a texture • Apply to mesh or plane  to C.O.P.; • Replace if eyepoint changes too much

  14. Images + Depth • 1 Image + Depth: a ‘thin shell’ • Reprojection (well known); Z-buffers can help • McMillan`95: 4-way raster ensures depth order • Problem: ‘holes’, occlusion, matching • Multiple Images: • LDI, LDI trees for multiresolution • Limitations: • Presumes diffuse-only environment • Depth capture tough: laser TOF reflectometer, manual scanner, structured light, or …

  15. Shape Problems: Correspondence Can you find ray intersections? Or ray depth? Ray colors might not match for non-diffuse materials (BRDF)

  16. Shape Problems: Correspondence Can you find ray intersections? Or ray depth? Ray colors might not match for non-diffuse materials (BRDF)

  17. Estimating Depth, Silhouettes Mildly new IBMR methods can help… • Sparse, manual image correspondences (Debevec, Seitz,) • Video sequences with camera motion tracking • Image (silhouette)-based Visual Hulls, ‘voxel carving’ (VIDEO!) Mostly a Classic Computer Vision Problem: • Epipolar Geometry: reduce search for correspondences • Global & local tracking & alignment methods…

  18. Light Probe: Irradiance Estimate • Place mirrored ball in scene, • Photograph (careful! High contrast image!) • Map position on sphere to incoming angle, intensity to irradiance. • Repeat where illumination changes greatly (in shadows, etc.) Uses: -mixing real & synthetic objects (Ward 96) -separating reflectance & illum (Yu 97)

  19. Light Control Methods Form estimates of surface properties (BRDF vs. position) by moving camera, light source, or both. • Carefully control incoming light direction (light stages, whirling banks of lights, etc) • Establish surface geometry (before, during) • Sort pixels by incoming/outgoing surf. Angle • Scattered data interpolation to get BRDF.

  20. Conclusion • Very active area • Heavy overlap with computer vision: careful not to re-invent & re-name! • Compute-intensive, but easily parallel; applies graphics hardware to broader probs.

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