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Lecture 6 Multiple-View Reconstruction from Scene Knowledge

Multiple-View Geometry for Image-Based Modeling. Lecture 6 Multiple-View Reconstruction from Scene Knowledge. INTRODUCTION: Scene knowledge and symmetry. Symmetry is ubiquitous in man-made or natural environments. INTRODUCTION: Scene knowledge and symmetry. Parallelism (vanishing point).

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Lecture 6 Multiple-View Reconstruction from Scene Knowledge

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  1. Multiple-View Geometry for Image-Based Modeling Lecture 6 Multiple-View Reconstruction from Scene Knowledge Invitation to 3D vision

  2. INTRODUCTION: Scene knowledge and symmetry Symmetry is ubiquitous in man-made or natural environments Invitation to 3D vision

  3. INTRODUCTION: Scene knowledge and symmetry Parallelism (vanishing point) Orthogonality Congruence Self-similarity Invitation to 3D vision

  4. INTRODUCTION: Wrong assumptions Ames room illusion Necker’s cube illusion Invitation to 3D vision

  5. INTRODUCTION: Related literature Mathematics: Hilbert 18th problem: Fedorov 1891, Hilbert 1901, Bieberbach 1910, George Polya 1924, Weyl 1952 Cognitive Science: Figure “goodness”: Gestalt theorists (1950s), Garner’74, Chipman’77, Marr’82, Palmer’91’99… Computer Vision (isotropic & homogeneous textures): Gibson’50, Witkin’81, Garding’92’93, Malik&Rosenholtz’97, Leung&Malik’97… Detection & Recognition (2D & 3D): Morola’89, Forsyth’91, Vetter’94, Mukherjee’95, Zabrodsky’95, Basri & Moses’96, Kanatani’97, Sun’97, Yang, Hong, Ma’02 Reconstruction (from single view): Kanade’81, Fawcett’93, Rothwell’93, Zabrodsky’95’97, van Gool et.al.’96, Carlsson’98, Svedberg and Carlsson’99, Francois and Medioni’02, Huang, Yang, Hong, Ma’02,03 Invitation to 3D vision

  6. Multiple-View Reconstruction from Scene Knowledge • SYMMETRY & MULTIPLE-VIEW GEOMETRY • Fundamental types of symmetry • Equivalent views • Symmetry based reconstruction • MUTIPLE-VIEW MULTIPLE-OBJECT ALIGNMENT • Scale alignment: adjacent objects in a single view • Scale alignment: same object in multiple views • ALGORITHMS & EXAMPLES • Building 3-D geometric models with symmetry • Symmetry extraction, detection, and matching • Camera calibration SUMMARY: Problems and future work Invitation to 3D vision

  7. SYMMETRY & MUTIPLE-VIEW GEOMETRY • Why does an image of a symmetric object give away its structure? • Why does an image of a symmetric object give away its pose? • What else can we get from an image of a symmetric object? Invitation to 3D vision

  8. 90O Equivalent views from rotational symmetry Invitation to 3D vision

  9. Equivalent views from reflectional symmetry Invitation to 3D vision

  10. Equivalent views from translational symmetry Invitation to 3D vision

  11. GEOMETRY FOR SINGLE IMAGES – Symmetric Structure Definition. A set of 3-D features S is called a symmetric structure if there exists a nontrivial subgroup G of E(3) that acts on it such that for every g in G, the map is an (isometric) automorphism of S. We say the structure S has a group symmetry G. Invitation to 3D vision

  12. GEOMETRY FOR SINGLE IMAGES – Multiple “Equivalent” Views Invitation to 3D vision

  13. GEOMETRY FOR SINGLE IMAGES – Symmetric Rank Condition Solving g0 from Lyapunov equations: with g’i and gi known. Invitation to 3D vision

  14. THREE TYPES OF SYMMETRY – Reflective Symmetry Pr Invitation to 3D vision

  15. THREE TYPES OF SYMMETRY – Rotational Symmetry Invitation to 3D vision

  16. THREE TYPES OF SYMMETRY – Translatory Symmetry Invitation to 3D vision

  17. SINGLE-VIEW GEOMETRY WITH SYMMETRY – Ambiguities “(a+b)-parameter” means there are an a-parameter family of ambiguity in R0 of g0 and a b-parameter family of ambiguity in T0 of g0. P Pr Pr N Invitation to 3D vision

  18. Reflectional symmetry Virtual camera-camera Symmetry-based reconstruction (reflection) (1) 2 (2) 1 4 (3) (4) 3 Invitation to 3D vision

  19. Homography Symmetry-based reconstruction Epipolar constraint 2 (1) (2) 1 4 (3) (4) 3 Invitation to 3D vision

  20. Symmetry-based reconstruction (algorithm) 2 pairs of symmetric image points Recover essential matrix or homography Decompose or to obtain Solve Lyapunov equation to obtain and then . Invitation to 3D vision

  21. Symmetry-based reconstruction (reflection) Invitation to 3D vision

  22. Symmetry-based reconstruction (rotation) Invitation to 3D vision

  23. Symmetry-based reconstruction (translation) Invitation to 3D vision

  24. ALIGNMENT OF MULTIPLE SYMMETRIC OBJECTS ? Invitation to 3D vision

  25. Correct scales within a single image Pick the image of a point on the intersection line Invitation to 3D vision

  26. Correct scale within a single image Invitation to 3D vision

  27. Correct scales across multiple images Invitation to 3D vision

  28. Correct scales across multiple images Invitation to 3D vision

  29. Correct scales across multiple images Invitation to 3D vision

  30. Image alignment after scales corrected Invitation to 3D vision

  31. ALGORITHM: Building 3-D geometric models 1. Specify symmetric objects and correspondence Invitation to 3D vision

  32. Building 3-D geometric models 2. Recover camera poses and scene structure Invitation to 3D vision

  33. Building 3-D geometric models 3. Obtain 3-D model and rendering with images Invitation to 3D vision

  34. ALGORITHM: Symmetry detection and matching Extract, detect, match symmetric objects across images, and recover the camera poses. Invitation to 3D vision

  35. Segmentation & polygon fitting • Color-based segmentation (mean shift) • 2. Polygon fitting Invitation to 3D vision

  36. Symmetry verification & recovery • 3. Symmetry verification (rectangles,…) • 4. Single-view recovery Invitation to 3D vision

  37. Symmetry-based matching 5. Find the only one set of camera poses that are consistent with all symmetry objects Invitation to 3D vision

  38. # of possible matching Cell in image 1 (1,2,1, g) 1 3 2 Cell in image 2 Camera transformation 1 3 2 MATCHING OF SYMMETRY CELLS – Graph Representation The problem of finding the largest set of matching cells is equivalent to the problem of finding the maximal complete subgraphs (cliques) in the matching graph. 36 possible matchings Invitation to 3D vision

  39. Camera poses and 3-D recovery Side view Top view Generic view Invitation to 3D vision

  40. Multiple-view matching and recovery (Ambiguities) Invitation to 3D vision

  41. Multiple-view matching and recovery (Ambiguities) Invitation to 3D vision

  42. ALGORITHM: Calibration from symmetry Calibrated homography Uncalibrated homography (vanishing point) Invitation to 3D vision

  43. ALGORITHM: Calibration from symmetry Calibration with a rig is also simplified: we only need to know that there are sufficient symmetries, not necessarily the 3-D coordinates of points on the rig. Invitation to 3D vision

  44. SUMMARY: Multiple-View Geometry + Symmetry • Multiple (perspective) images = multiple-view rank condition • Single image + symmetry =“multiple-view” rank condition • Multiple images + symmetry =rank condition + scale correction • Matching + symmetry = rank condition + scale correction • + clique identification Invitation to 3D vision

  45. SUMMARY • Multiple-view 3-D reconstruction in presence of symmetry • Symmetry based algorithms are accurate, robust, and simple. • Methods are baseline independent and object centered. • Alignment and matching can and should take place in 3-D space. • Camera self-calibration and calibration are simplified and linear. • Related applications • Using symmetry to overcome occlusion. • Reconstruction and rendering with non-symmetric area. • Large scale 3-D map building of man-made environments. Invitation to 3D vision

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