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Symmetric Architecture Modeling with a Single Image

Symmetric Architecture Modeling with a Single Image. Author: Nianjuan Jiang, Ping Tan, Loong-Fah Cheong Department of Electrical & Computer Engineering, National University of Singapore. Presenter: Feilong Yan. Motivation.

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Symmetric Architecture Modeling with a Single Image

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  1. Symmetric Architecture Modeling with a Single Image Author: Nianjuan Jiang, Ping Tan, Loong-Fah Cheong Department of Electrical & Computer Engineering, National University of Singapore Presenter: Feilong Yan

  2. Motivation Model architecture from single image is common task in 3D creation due to the lack of the more images. Historic Photo: Internet Photo:

  3. Motivation Single image based modeling is very difficult! Due to the trouble on camera calibration and texture loss The recent methods only can handle simple and planar façade Pascal Mulleet al. Image-based procedural modeling of facades Changchang Wu et al. Repetition-based Dense Single-View Reconstruction

  4. Motivation But what about this one? And If we only have this single photo. Complex and not planar

  5. Motivation Fortunately , the symmetry is very prevalent in the architecture Symmetry is a breakthrough which magically can generate more images from the input Complex and not planar This is reasonable, but exciting to me

  6. Main Idea Bilateral Symmetry

  7. Main Idea Rotational Symmetry

  8. Main Idea 2 even more views Reconstruction

  9. Modeling Pipeline 3D Reconstruction Surface Modeling Texture Enhancement Input Image and Frustum Vertices Model Refinement Model Initialization Calibration and 3D Reconstruction

  10. 3D Reconstruction Camera Calibration 3D points Reconstruction

  11. Camera Calibration Previous Methods Calibrate the camera from the vanishing points of 3 mutually orthogonal directions in a single image. HARTLEY, R., AND ZISSERMAN Multiple View Geometry in Computer Vision But many photos do not have 3 vanishing points, and this method is often numerical unstable

  12. Camera Calibration Previous Methods If enough(>=6) correspondences between spatial vertices and the image pixels are known, the camera calibration may be immediately computed. WILCZKOWIAK, M. et. al Using geometric constraints through parallelepipeds for calibration and 3d modeling Parallelipiped is used to represent a building block. Under the constraint of parallelipiped, the visible 6 spatial vertices may be estimated. This method is stable but not very suitable for some architecture

  13. Camera Calibration New Method: Inspired by parallelipiped method, the author found the frustum more general to represent the architecture

  14. Camera Calibration Demo: Frustum is symmetric

  15. Camera Calibration 6 4 5 3 1 2 Coordinate represented in world: Of this example

  16. Camera Calibration 6 4 5 3 1 2 K= = = Quaternion( unit vector(x,y,z),) t =t(x, y, z) 15 parameters to estmate =1

  17. Camera Calibration 6 4 5 3 1 2 Simplification: K= 11 parameters to estimate, now the calibration is formulated as a non-linear optimization

  18. Camera Calibration Optimization Initialization: = = The Quadratic : Extend the right multiplication, since the R is unit orthogonal matrix, then we obtain: User gives the

  19. 3D Points Reconstruction Symmetry-Based Triangulation:

  20. 3D Points Reconstruction Symmetry-Based Triangulation:

  21. Surface Modeling User-Interaction Assisted Modeling Geometry Modeling Model Refinement Texture Mapping

  22. Geometry Modeling Roof Planar Structure

  23. Model Refinement

  24. Texture Mapping Single image inevitably lack texture samples due to the foreshortening and occlusion. But to achieve a good texture effect, there are 2 requirements: 1. the final texture should be consistent with the foreshortened image ; 2, the final texture should have consistent weathering pattern. We need to know where is well textured and where not Refine low quality region Detect Texture Quality Texture the occluded region

  25. Texture Quality Detection Back Project Ratio = Triangle.size / imageProjection.size Ratio > Threshold and Ratio is finite: large texture distortion Ratio<Threshold: distortion free Ratio is infinite: occluded Texture in distortion free region will be used as the texture sample

  26. Refinement for Low-Quality Super- Resolution Problem

  27. Occluded Region Texturing The simplest way is to repeat the same texture as those of their symmetric counterparts, but this makes the model look artificial. It is better to synthesize the texture in these region with common method Another feature of the texture is weathering pattern, a constraint texture map is used according to the height of the architecture. Synthesized Input sample

  28. Result

  29. Result

  30. Conclusion • Contribution: • Novel Calibration Method • Texture Enhance Method • Limitations: • Strong assumption for simplification of camera calibration

  31. Message from this Paper

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