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Image-Based Rendering to Accelerate Interactive Walkthroughs

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This document presents groundbreaking research by Daniel G. Aliaga at Lucent Technologies, Bell Labs, focusing on image-based rendering techniques aimed at improving interactive walkthrough experiences. By replacing complex geometry with high-quality images, the approach ensures high visual fidelity and efficiency, independent of scene complexity. Key aspects include texture mapping, error metrics, and geometry warping. The research outlines automatic image placement methods, optimizing rendering while maintaining temporal and geometric continuity, which is crucial for immersive environments such as flight simulators and virtual tours.

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Image-Based Rendering to Accelerate Interactive Walkthroughs

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  1. Lucent Technologies Bell Labs Innovations Image-Based Rendering to Accelerate Interactive Walkthroughs Daniel G. Aliaga Visual Communications Research Lucent Technologies Bell Labs <aliaga@bell-labs.com>

  2. 3D Models 2.0M tris 1.7M tris 1.0M tris 0.9M tris

  3. Why Use Images? • Independent of scene complexity 640x480 pixels 640x480 pixels

  4. Flight Simulators • Mid-1980’s • E&S CT-6 one of first to use real-time photo textures • Hand-selected objects: • Terrain, trees, airplanes, buildings, etc. • 30-60 Hz • High visual fidelity

  5. Overview • Replacing Geometry with Images • Displaying Images • Texture-mapping and error metrics • Geometry and image warping • Meshes, Lightfield/Lumigraph • Image Placement • Automatically Bounding Model Complexity • Cells and Portals • Conclusions

  6. Replacing Geometry with Images • Algorithm • Select subset of model • Create image of the subset • Cull subset and replace with image • Why? • Image displayed in (approx.) constant time • Image reused for several frames

  7. Simple Example

  8. Simple Example

  9. Simple Example

  10. Overview • Replacing Geometry with Images • Displaying Images • Texture-mapping and error metrics • Geometry and image warping • Meshes, Lightfield/Lumigraph • Image Placement • Automatically Bounding Model Complexity • Cells and Portals • Conclusions

  11. Geometric Discontinuity • If we move from the center-of-projection, discontinuities appear at the border

  12. Temporal Discontinuity • While moving, if we switch between geometry and image, a sudden pop occurs

  13. Approaches • Geometric and Temporal Continuity • Error metrics • Geometry warping • Image warping • Lightfield/Lumigraph

  14. c1  c2 Error Metrics • Use an error metric to control amount of discontinuity [Maciel95][Shade96][Schaufler96]

  15. Error Metric • Relies on “angular-deviation” measuring the visual quality of using the (same) image

  16. Video Segment I • Pre-rendered Impostors • Maciel95 • Dynamic Image-Caching • Shade96, Schaufler96

  17. Geometry Warping [Aliaga96]

  18. Geometry Warping

  19. Geometry Warping

  20. Geometry Warping

  21. Geometry Warping

  22. Geometry Warping

  23. Geometry Warping

  24. Geometry Warping

  25. Geometry Warping Surrounding geometry warped (incorrect perspective) Correct perspective

  26. Viewpoint Center-of-projection Surrounding Geometry Geometry Warping • Surrounding geometry warped to match image

  27. Video Segment II • Geometry Warping • Aliaga96

  28. Image Warping • Change the image itself • Re-project the image to the current viewpoint • [Chen93][McMillan95][Max95][Shade98] • Display image as a (simplified, textured) mesh • [Darsa97][Sillion97]

  29. Image Warping • A raster scan of each sheet produces a back-to-front ordering of warped pixels eye Reference COP

  30. x2 = (x1) P2-1(c1 - c2) + P2-1P1 x1 Move pixels based on distance to eye ~Texture mapping d c1 x1 x2 c2 Image Warping • McMillan and Bishop’s Warping Equation

  31. Example... • Image outlined in yellow • Viewed from image’s center-of-projection

  32. 3D Image Warp • Single sample per pixel

  33. Layered Depth Image Warp • Multiple samples per pixel • Previous occlusions are filled-in [Popescu98]

  34. Meshes • (Simplified) Textured Depth Mesh • Per-pixel depth creates mesh that approximates 3D parallax effects • Image is texture-mapped onto mesh

  35. Video Segment III • 3D Image Warping • McMillan95 • Textured Meshes • Darsa97, Sillion97

  36. (u,v) (s,t) Lightfield/Lumigraph • Flow of light at all positions and directions • [Levoy96][Gortler96] • Large number of images are used as 2D slices of a 4D light function

  37. Video Segment IV • Light field • Levoy96 • Lumigraph • Gortler96

  38. Overview • Replacing Geometry with Images • Displaying Images • Texture-mapping and error metrics • Geometry and image warping • Meshes, Lightfield/Lumigraph • Image Placement • Automatically Bounding Model Complexity • Cells and Portals • Conclusions

  39. Automatic Image-Placement • As a preprocess • Select geometry to replace with an image in order to limit the number of primitives to render for any frame • At run time • Display selected geometry as a (depth) image • Render remaining geometry normally

  40. Automatic Image-Placement Preprocess Run time

  41. Example Rendering Geometry + = Final Scene Image

  42. - Overview - Image Placement - Displaying Images - Conclusions Key Observation • Example model • Too much geometry in view frustum Viewpoint

  43. Key Observation • Geometry is replaced by image to limit the number of primitives to render Viewpoint

  44. Key Observation • Less geometry is in the view frustum from the eye than the one from the grid viewpoint Eye Grid viewpoint

  45. Recursive Subdivision Algorithm 2k+1 2k Even to odd 2k+1 2k+2 Odd to even

  46. Example Grid 3D grid of 1557 viewpoints Wireframe rendering

  47. 700000 600000 500000 400000 300000 200000 100000 0 0 50 100 150 200 250 Sample Path View Frustum Culling Images Present Polygons 300 Frame Power Plant, 2M tris, SGI Onyx2 w/ IR

  48. Images Per Triangle Power Plant 0.004 Torpedo Room Brooks House Pipes Best Case 0.003 Worst Case Images Per Model Triangle 0.002 0.001 0 0 10 20 30 40 50 Maximum Rendered Triangles (% of Model)

  49. Preprocessing Summary

  50. Video Segment V • Automatically Bounding Geometric Complexity by Using Images • Aliaga99

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