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Image-Based Modeling and Rendering

Learn about the advantages and disadvantages of Image-Based Modeling and Rendering (IBR), along with a brief history of its development. Discover basic approaches, such as Image Warping and Light Fields, and explore recent works in the field. Explore camera geometry concepts and the acquisition of Reflectance Fields for human faces. Understand the tradeoff between compactness, flexibility, and rendering speed in IBR.

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Image-Based Modeling and Rendering

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  1. Image-Based Modeling and Rendering CS 6998 Lecture 6

  2. Next few slides courtesy Paul Debevec; SIGGRAPH 99 course notes

  3. IBR: Pros and Cons • Advantages • Easy to capture images: photorealistic by defn • Simple, universal representation • Often bypass geometry estimation? • Independent of scene complexity? • Disadvantages • WYSIWYG but also WYSIAYG • Explosion of data as flexibility increased • Often discards intrinsic structure of model?

  4. IBR: A brief history • Texture maps, bump maps, env. maps [70s] • Poggio et al. MIT: Faces, image-based analysis/synthesis • Modern Era • Chen and Williams 93, View Interpolation [Images with depth] • Chen 95 Quicktime VR [Images from many viewpoints] • McMillan and Bishop 95 Plenoptic Modeling [Images w disparity] • Gortler et al, Levoy and Hanrahan 96 Light Fields [4D] • Shade et al. 98 Layered Depth Images [2.5D] • Debevec et al. 00 Reflectance Field [4D] • Inverse rendering methods (Sato,Yu,Marschner,Boivin,…) • Fundamentally, sampled representations in graphics

  5. Outline • Overview of IBR • Basic approaches • Image Warping • Light Fields • Survey of some recent work • Later and next week: Paper presentations

  6. Warping slides courtesy Leonard McMillan, SIGGRAPH 99 course notes

  7. Outline • Overview of IBR • Basic approaches • Image Warping • [2D + depth. Requires correspondence/disparity] • Light Fields [4D] • Survey of some recent work • Later and next week: Paper presentations

  8. Outline • Overview of IBR • Basic approaches • Image Warping • [2D + depth. Requires correspondence/disparity] • Light Fields [4D] • Survey of some recent work • Later and next week: Paper presentations

  9. Camera Geometry Refresher: LDIs • Layered depth images [Shade et al. 98] Slide from Agrawala, Ramamoorthi, Heirich, Moll, SIGGRAPH 2000

  10. Refresher: LDIs • Layered depth images [Shade et al. 98] LDI

  11. Refresher: LDIs • Layered depth images [Shade et al. 98] LDI (Depth, Color)

  12. Surface Light Fields • Miller 98, Nishino 99, Wood 00 • Reflected light field (lumisphere) on surface • Explicit geometry as against light fields. Easier compress

  13. Acquiring Reflectance Field of Human Face [Debevec et al. SIGGRAPH 00] Illuminate subject from many incident directions

  14. Example Images Images from Debevec et al. 00

  15. Conclusion (my views) • Real issue is compactness/flexibility vs. rendering speed • IBR is use of sampled representations. Easy to interpolate, fast to render. If samples images, easy to acquire. • IBR in pure form not really practical • WYSIAYG • Explosion as increase dimensions (8D transfer function) • Ultimately, compression, flexibility needs geometry/materials • Right question is tradeoff compactness/efficiency • Factored representations • Understand sampling rates and reconstruction

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