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CSCE 641 Computer Graphics: Image-based Rendering (cont.)

CSCE 641 Computer Graphics: Image-based Rendering (cont.). Jinxiang Chai. Outline. Light field rendering Plenoptic sampling (light field sampling) Layered depth image/ Post-Rendering 3D Warping View-dependent texture mapping Unstructured lumigraph .

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CSCE 641 Computer Graphics: Image-based Rendering (cont.)

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  1. CSCE 641 Computer Graphics: Image-based Rendering (cont.) Jinxiang Chai

  2. Outline • Light field rendering • Plenoptic sampling (light field sampling) • Layered depth image/Post-Rendering 3D Warping • View-dependent texture mapping • Unstructured lumigraph

  3. Layered depth image [Shade et al, SIGGRAPH98] Layered depth image: - image with depths

  4. Layered depth image [Shade et al, SIGGRAPH98] Layered depth image: - rays with colors and depths

  5. Layered depth image [Shade et al, SIGGRAPH98] Layered depth image: (r,g,b,depth) - image with depths - rays with colors and depths

  6. Layered depth image [Shade et al, SIGGRAPH98] Rendering from layered depth image

  7. Layered depth image [Shade et al, SIGGRAPH98] Rendering from layered depth image • - Incremental in X and Y • - Forward warping one pixel with depth

  8. Layered depth image [Shade et al, SIGGRAPH98] Rendering from layered depth image • - Incremental in X and Y • - Forward warping one pixel with depth

  9. Layered depth image [Shade et al, SIGGRAPH98] Rendering from layered depth image How to deal with occlusion/visibility problem? • - Incremental in X and Y • - Forward warping one pixel with depth

  10. How to form LDIs • Synthetic world with known geometry and texture • - from multiple depth images • - modified ray tracer • Real images • - reconstruct geometry from multiple images (e.g., • voxel coloring, stereo reconstruction) • - form LDIs using multiple images and • reconstructed geometry • Kinect sensors • - record both image data and depth data

  11. Image-based Rendering Using Kinect Sensors • Capture both video/depth data using kinect sensors • Using 3D warping to render a video from a novel view point [e.g., Post-Rendering 3D Warping] • Demo: click here

  12. Outline • Light field rendering • Plenoptic sampling (light field sampling) • Layered depth image/Post-Rendering 3D Warping • View-dependent texture mapping • Unstructured lumigraph

  13. View-dependent surface representation From multiple input image - reconstruct the geometry - view-dependent texture

  14. View-dependent surface representation From multiple input image - reconstruct the geometry - view-dependent texture

  15. View-dependent surface representation From multiple input image - reconstruct the geometry - view-dependent texture

  16. View-dependent surface representation From multiple input image - reconstruct the geometry - view-dependent texture

  17. View-dependent texture mapping [Debevec et al 98]

  18. View-dependent texture mapping • - Virtual camera at point D • Textures from camera Ci mapped onto triangle faces • Blending weights in vertex V • Angle θi is used to compute the weight values: • wi = exp(-θi2/2σ2) Subject's 3D proxy points V q q 0 3 q q 1 2 C C C C 0 1 2 3 D

  19. Videos: view-dependent texture mapping

  20. Outline • Light field rendering • Plenoptic sampling (light field sampling) • Layered depth image/Post-Rendering 3D Warping • View-dependent texture mapping • Unstructured lumigraph

  21. The Image-Based Rendering Problem • Synthesize novel views from reference images • Static scenes, fixed lighting • Flexible geometry and camera configurations

  22. LF VDTM The ULR Algorithm [Siggraph01] • Designed to work over a range of image and geometry configurations # of Images Geometric Fidelity

  23. LF VDTM The ULR Algorithm [Siggraph01] • Designed to work over a range of image and geometry configurations # of Images ULR Geometric Fidelity

  24. LF VDTM The ULR Algorithm [Siggraph01] • Designed to work over a range of image and geometry configurations • Designed to satisfy desirable properties # of Images ULR Geometric Fidelity

  25. u u0 s0 s Desired Camera “Light Field Rendering,” SIGGRAPH ‘96 Desired color interpolated from “nearest cameras”

  26. u s Desired Camera “Light Field Rendering,” SIGGRAPH ‘96

  27. “The Scene” u Potential Artifact Desired Camera “The Lumigraph,” SIGGRAPH ‘96

  28. “The Scene” Desired Property #2: Use of geometric proxy Desired Camera “The Lumigraph,” SIGGRAPH ‘96

  29. “The Scene” Desired Camera “The Lumigraph,” SIGGRAPH ‘96

  30. “The Scene” Desired Property #3: Unstructured input images Desired Camera “The Lumigraph,” SIGGRAPH ‘96 Rebinning Note: all images are resampled.

  31. “The Scene” Desired Property #4: Real-time implementation Desired Camera “The Lumigraph,” SIGGRAPH ‘96

  32. “The Scene” Occluded Out of view Desired Camera View-Dependent Texture Mapping, SIGGRAPH ’96, EGRW ‘98

  33. “The Scene” Desired Property #5: Continuous reconstruction Desired Camera View-Dependent Texture Mapping, SIGGRAPH ’96, EGRW ‘98

  34. “The Scene” Desired Camera View-Dependent Texture Mapping, SIGGRAPH ’96, EGRW ‘98 θ1 θ3 θ2

  35. “The Scene” Desired Property #6: Angles measured w.r.t. proxy Desired Camera View-Dependent Texture Mapping, SIGGRAPH ’96, EGRW ‘98 θ1 θ3 θ2

  36. Desired Camera “The Scene”

  37. “The Scene” Desired Property #7: Resolution sensitivity Desired Camera

  38. Demo

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