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Image-Based Modeling and Rendering

Image-Based Modeling and Rendering. CS 6998 Lecture 6. Next few slides courtesy Paul Debevec; SIGGRAPH 99 course notes. IBR: Pros and Cons. Advantages Easy to capture images: photorealistic by defn Simple, universal representation Often bypass geometry estimation?

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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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