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Reconstructing Building Interiors from Images

This paper presents a comprehensive approach to reconstructing and visualizing architectural interiors from images. It outlines the goals of developing a fully automated system that generates simple and accurate 3D models suitable for real-time interactive applications. Key challenges addressed include texture-poor surfaces and complex visibility in indoor environments. The paper details the system pipeline, algorithmic advancements, and experimental results that demonstrate the efficacy of the proposed methodology. Future work and references are also discussed.

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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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