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Reconstructing Building Interiors from Images

Reconstructing Building Interiors from Images. Yasutaka Furukawa Brian Curless Steven M. Seitz University of Washington, Seattle, USA 2011/01/16 蔡禹婷. Outline. Introduction Goal Challenges System pipeline Algorithmic details (technical contribution) Experimental Results

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Reconstructing Building Interiors from Images

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  1. Reconstructing Building Interiors from Images Yasutaka Furukawa Brian Curless Steven M. Seitz University of Washington, Seattle, USA 2011/01/16 蔡禹婷

  2. Outline • Introduction • Goal • Challenges • System pipeline • Algorithmic details (technical contribution) • Experimental Results • Conclusion and future work • Reference

  3. Outline • Introduction • Goal • Challenges • System pipeline • Algorithmic details (technical contribution) • Experimental Results • Conclusion and future work • Reference

  4. Reconstruction and Visualization of Architectural Scenes • Semi-automatic(Manual ) • Google Earth & Virtual Earth • Façade :  Building facade made for use as a real-time video game engine environment. • Automatic • Ground-level images • Aerial images: A projected image which is "floating in air", and cannot be viewed normally. Aerial images Google Earth 4  Virtual Earth

  5. Reconstruction and Visualization of Architectural Scenes • Difficulty • Little attention paid to indoor scenes • If you walk inside your home and take photographs, generating a compelling 3D reconstruction and visualization becomes much more difficult. ? ? Google Earth 4  Virtual Earth ? ? Aerial images

  6. Outline • Introduction • Goal • Challenges • System pipeline • Algorithmic details (technical contribution) • Experimental Results • Conclusion and future work • Reference

  7. Goal • Fully automatic system for interiors / outdoors • Reconstructs a simple 3D model from images • Provides real-time interactive visualization

  8. Outline • Introduction • Goal • Challenges • System pipeline • Algorithmic details (technical contribution) • Experimental Results • Conclusion and future work • Reference

  9. Challenges • Reconstruction • Multi-view stereo (MVS) typically produces a dense model • We want the model to be • Simple for real-time interactive visualization of a large scene (e.g., a whole house) • Accurate for high-quality image-based rendering • Simple mode is effective for compelling visualization

  10. Challenges • Indoor Reconstruction Texture-poor surfaces Complicated visibility Prevalence of thin structures (doors, walls, tables)

  11. Outline • Introduction • Goal • Challenges • System pipeline • Algorithmic details (technical contribution) • Experimental Results • Conclusion and future work • Reference

  12. System pipeline • 3D reconstruction and visualization system for architectural scenes.

  13. System pipeline • Image-based • SFM • MVS • MWS • Merging

  14. System pipeline Image-based Image-based SFM MVS MWS Merging Image-based

  15. System pipeline Image-based SFM MVS MWS Merging Structure-from-Motion Bundler by Noah Snavely Structure from Motion for unordered image collections WEB: http://phototour.cs.washington.edu/bundler/

  16. System pipeline Image-based SFM MVS MWS Merging Multi-view Stereo PMVS by Yasutaka Furukawa and Jean Ponce Patch-based Multi-View Stereo Software/

  17. System pipeline Image-based SFM MVS MWS Merging Manhattan-world Stereo

  18. System pipeline Image-based SFM MVS Manhattan-world Stereo

  19. System pipeline Image-based SFM MVS MWS Merging Manhattan-world Stereo Result

  20. System pipeline Image-based SFM MVS MWS Merging Axis-aligned depth map merging (Paper contribution)

  21. Outline • Introduction • Goal • Challenges • System pipeline • Algorithmic details (technical contribution) • Experimental Results • Conclusion and future work • Reference

  22. Axis-aligned Depth-map Merging • Basic framework is similar to volumetric MRF

  23. Axis-aligned Depth-map Merging • Basic framework is similar to volumetric MRF

  24. Key Feature 1 - Penalty terms

  25. Key Feature 1 - Penalty terms Binary penalty Binary encodes smoothness & data

  26. Key Feature 1 - Penalty terms Binary penalty Binary encodes smoothness & dataUnary is often constant (inflation)

  27. Key Feature 1 - Penalty terms • Weak regularization at interesting places • Focus on a dense model Binary penalty Binary encodes smoothness & dataUnary is often constant (inflation)

  28. Key Feature 1 - Penalty terms • Weak regularization at interesting places • Focus on a dense model • We want a simple model Binary penalty Binary encodes smoothness & dataUnary is often constant (inflation)

  29. Key Feature 1 - Penalty terms Binary penalty Binary encodes smoothness & data Unary is often constant (inflation)

  30. Key Feature 1 - Penalty terms Binary penalty Binary encodes smoothness & data Unary is often constant (inflation)

  31. Key Feature 1 - Penalty terms Binary penalty Binary encodes smoothness & data Unary is often constant (inflation) Unary encodes data

  32. Key Feature 1 - Penalty terms Binary penalty Binary encodes smoothness & data Unary is often constant (inflation) Binary is smoothness Unary encodes data

  33. Key Feature 1 - Penalty terms Binary penalty Regularization becomes weakDense 3D model Regularization is data-independent Simpler 3D model

  34. Axis-aligned Depth-map Merging • Align-voxel grid withthe dominant axes

  35. Axis-aligned Depth-map Merging • Align-voxel grid withthe dominant axes • Data term (unary)

  36. Axis-aligned Depth-map Merging • Align voxel grid withthe dominant axes • Data term (unary)

  37. Axis-aligned Depth-map Merging • Align voxel grid withthe dominant axes • Data term (unary)

  38. Axis-aligned Depth-map Merging • Align voxel grid withthe dominant axes • Data term (unary) • Smoothness (binary)

  39. Axis-aligned Depth-map Merging • Align voxel grid withthe dominant axes • Data term (unary) • Smoothness (binary)

  40. Axis-aligned Depth-map Merging • Align voxel grid withthe dominant axes • Data term (unary) • Smoothness (binary) • Graph-cuts

  41. Key Feature 2 – Regularization • For large scenes, data info are not complete • Typical volumetric MRFs bias to general minimal surface • We bias to piece-wise planar axis-aligned for architectural scenes

  42. Key Feature 2 – Regularization

  43. Key Feature 2 – Regularization

  44. Key Feature 2 – Regularization

  45. Key Feature 2 – Regularization

  46. Key Feature 2 – Regularization

  47. Key Feature 2 – Regularization Same energy (ambiguous)

  48. Key Feature 2 – Regularization Same energy (ambiguous) Data penalty: 0

  49. Key Feature 2 – Regularization Same energy (ambiguous) Data penalty: 0 Smoothness penalty: Data penalty: 0 Smoothness penalty: 24 Data penalty: 0

  50. Key Feature 2 – Regularization shrinkage

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