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Interactively Modeling with Photogrammetry

Interactively Modeling with Photogrammetry. Pierre Poulin Mathieu Ouimet Marie-Claude Frasson Dép. Informatique et recherche opérationnelle Université de Montréal. Motivation. Photo-realism is difficult to achieve Important recent progress in rendering

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Interactively Modeling with Photogrammetry

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  1. Interactively Modeling with Photogrammetry Pierre Poulin Mathieu Ouimet Marie-Claude Frasson Dép. Informatique et recherche opérationnelle Université de Montréal

  2. Motivation • Photo-realism is difficult to achieve • Important recent progress in rendering • Acquiring realistic 3D models is still a major hurtle • Important needs for realism, special effects in movies, CAR, etc. Extracting 3D models from photographs

  3. Computer Vision / Robotics • 3D models do not satisfy most of the visual accuracy necessary in graphics • Fully automatic systems are challenging : • false correspondences • missed edge detections • noise • textures • Provide much inspiration in our system

  4. Our Interactive Reconstruction System • User knows the 3D models / textures • User is responsible for everything • User interactions : • User draws 2D primitives • User puts the 2D primitives in correspondences • User adds 3D constraints • User extracts a unified texture

  5. Drawing 2D Primitives

  6. Correspondences

  7. 3D Constraints Co-planarity Perpendicularity Parallelism

  8. Extracted Textures

  9. Reconstruction Process • Incremental • Robust • Intuitive • Provides good graphics models • Labor-intensive

  10. The Camera • Our camera is a transformation matrix • No explicit need for real camera parameters

  11. Reconstructing a Camera • 6 or more 2D-to-3D point correspondences (0,1,0) (1,1,0) (0,1,1) (1,0,0) (1,0,1) (0,0,1)

  12. Reconstructing a Camera • Least-squares to compute all Ti • Solution with SVD • Fast • Robust • Always provides a solution • Conditions for accuracy similar to non-linear

  13. Reconstructing a 3D Point • Incidence of 3D point on planes • Least-squares to compute each (x,y,z) • Polygons as set of 3D points

  14. Reconstructing a 3D Line • Plücker coordinates of a 3D line

  15. Additional 3D Constraints • Co-planarity • Parallelism • Perpendicularity • Weights can be used to alter the importance of certain constraints Weights

  16. Iterating • Better cameras give better 3D geometry • Better 3D geometry give better cameras • Iterations between the two improve both

  17. Convergence

  18. Recovering Texel Colors t v v t s t u s u Texture map 3D Polygon s 2D Images

  19. Occlusion Testing Zones of Occlusion 3D Model 2D Image

  20. Linear Fit • Misalignments due to imprecisions in the 3D model and its cameras • 2D transformation matrix using least-squares

  21. Unifying Texel Criteria • Clustering to discriminate view-dependent colors for a texel • Other metrics used to weight valid texels : • Projected area (adaptive sampling) • Texture quality

  22. Two Scenes with Cubes

  23. Desktop

  24. Lego Tower

  25. Coffee Pot

  26. Conclusions • User knows best • Satisfying 3D models and extracted textures • Labor-intensive

  27. Future Work • Better user interface • Error detection • Radiances, reflectances, and global illumination • Displacement maps on 3D primitives • Bounds on reconstructed information

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