Multiple-View Geometry for Image-Based Modeling (Course 42)

2. Multiple-View Geometry for Image-Based Modeling (Course 42) Lecturers: Yi Ma (UIUC) Stefano Soatto (UCLA) Jana Kosecka (GMU) Rene Vidal (UC Berkeley) Yizhou Yu (UIUC)

3. COURSE LECTURE OUTLINE • A. Introduction (Ma) • B. Preliminaries: geometry & image formation (Ma) • C. Image primitives & correspondence (Soatto) • D. Two calibrated views (Kosecka) • E. Uncalibrated geometry and stratification (Soatto) • F. Multiple-view geometry & algebra (Vidal, Ma) • G. Reconstruction from scene knowledge (Ma) • H. Step-by-step building of 3D model (Kosecka, Soatto) • I. Image-based texture mapping and rendering (Yu)

4. Multiple-View Geometry for Image-Based Modeling Introduction (Lecture A) Yi Ma Perception & Decision Laboratory Decision & Control Group, CSL Image Formation & Processing Group, Beckman Electrical & Computer Engineering Dept., UIUC http://decision.csl.uiuc.edu/~yima

5. IMAGES AND GEOMETRY – A Little History of Perspective Imaging • Pinhole (perspective) imaging, in most ancient civilizations. • Euclid, perspective projection, 4th century B.C., Alexandria • Pompeii frescos, 1st century A.D. Image courtesy of C. Taylor

6. IMAGES AND GEOMETRY – A Little History of Perspective Imaging • Fillippo Brunelleschi, first Renaissance artist painted with • correct perspective,1413 • “Della Pictura”, Leone Battista Alberti, 1435 • Leonardo Da Vinci, stereopsis, shading, color, 1500s • “The scholar of Athens”, Raphael, 1518 Image courtesy of C. Taylor

7. IMAGES AND GEOMETRY – The Fundamental Problem Input: Corresponding “features” in multiple images. Output: Camera calibration, pose, scene structure, surface photometry. Jana’s apartment

8. IMAGES AND GEOMETRY – History of “Modern” Geometric Vision • Chasles, formulated the two-view seven-point problem,1855 • Hesse, solved the above problem, 1863 • Kruppa, solved the two-view five-point problem, 1913 • Longuet-Higgins, the two-view eight-point algorithm, 1981 • Liu and Huang, the three-view trilinear constraints, 1986 • Huang and Faugeras, SVD based eight-point algorithm, 1989 • Tomasi and Kanade, (orthographic) factorization method, 1992 • Ma, Huang, Kosecka, Vidal, multiple-view rank condition, 2000

9. APPLICATIONS – 3-D Modeling and Rendering

10. APPLICATIONS – 3-D Modeling and Rendering Image courtesy of Paul Debevec

11. APPLICATIONS – Image Morphing, Mosaicing, Alignment Images of CSL, UIUC

12. APPLICATIONS – Real-Time Sports Coverage First-down line and virtual advertising Image courtesy of Princeton Video Image, Inc.

13. APPLICATIONS – Real-Time Virtual Object Insertion UCLA Vision Lab

14. APPLICATIONS – Autonomous Highway Vehicles Image courtesy of E.D. Dickmanns

15. APPLICATIONS – Unmanned Aerial Vehicles (UAVs) Rate: 10Hz Accuracy: 5cm, 4o Berkeley Aerial Robot (BEAR) Project

16. Multiple-View Geometry for Image-Based Modeling Preliminaries: Imaging Geometry & Image Formation (Lecture B) Yi Ma Perception & Decision Laboratory Decision & Control Group, CSL Image Formation & Processing Group, Beckman Electrical & Computer Engineering Dept., UIUC http://decision.csl.uiuc.edu/~yima

17. Preliminaries: Imaging Geometry and Image Formation INTRODUCTION • 3D EUCLIDEAN SPACE & RIGID-BODY MOTION • Coordinates and coordinate frames • Rigid-body motion and homogeneous coordinates • GEOMETRIC MODELS OF IMAGE FORMATION • Lens & Lambertian surfaces • Pinhole camera model • CAMERA INTRINSIC PARAMETERS & RADIAL DISTORTION • From space to pixel coordinates • Notation: image, preimage, and coimage • Radial distortion and correction SUMMARY OF NOTATION

18. 3D EUCLIDEAN SPACE - Cartesian Coordinate Frame Coordinates of a point in space: Standard base vectors:

19. 3D EUCLIDEAN SPACE - Vectors A “free” vector is defined by a pair of points : Coordinates of the vector :

20. 3D EUCLIDEAN SPACE – Inner Product and Cross Product Inner product between two vectors: Cross product between two vectors:

21. RIGID-BODY MOTION – Rotation Rotation matrix: Coordinates are related by:

22. RIGID-BODY MOTION – Rotation and Translation Coordinates are related by:

23. RIGID-BODY MOTION – Homogeneous Coordinates 3D coordinates are related by: Homogeneous coordinates are related by: Homogeneous coordinates of a vector:

24. IMAGE FORMATION – Lens, Light, and Surfaces image irradiance surface radiance BRDF Lambertian thin lens small FOV

25. IMAGE FORMATION – Pinhole Camera Model Pinhole Frontal pinhole

26. IMAGE FORMATION – Pinhole Camera Model 2D coordinates Homogeneous coordinates

27. CAMERA PARAMETERS – Pixel Coordinates calibrated coordinates Linear transformation pixel coordinates

28. CAMERA PARAMETERS – Calibration Matrix and Camera Model Ideal pinhole Pixel coordinates Calibration matrix (intrinsic parameters) Projection matrix Camera model

29. CAMERA PARAMETERS – Radial Distortion Nonlinear transformation along the radial direction Distortion correction: make lines straight

30. IMAGE FORMATION – Image of a Point Homogeneous coordinates of a 3-D point Homogeneous coordinates of its 2-D image Projection of a 3-D point to an image plane

31. NOTATION – Image, Coimage, Preimage of a Point Image of a 3-D point Coimage of the point Preimage of the point

32. NOTATION – Image, Coimage, Preimage of a Line Coimage of a 3-D line Preimage of the line Image of the line

33. IMAGE FORMATION – Coimage of a Line Homogeneous representation of a 3-D line Homogeneous representation of its 2-D coimage Projection of a 3-D line to an image plane

34. IMAGE FORMATION – Multiple Images “Preimages” are all incident at the corresponding features. . . .

35. LIST OF REFERENCES Chapters 2 & 3 An Invitation to 3-D Vision: From Images to Geometric Models, Ma, Soatto, Kosecka, Sastry, Springer-Verlag, 2003.