1. Self-calibration Class 13 Read Chapter 6

2. Assignment 3 Collect potential matches from all algorithms for all pairs Matlab ASCII format, exchange data Implement RANSAC that uses combined match dataset Compute consistent set of matches and epipolar geometry Report thresholds used, match sets used, number of consistent matches obtained, epipolar geometry, show matches and epipolar geometry (plot some epipolar lines). Due next Tuesday, Nov. 2 naming convention: firstname_ij.dat chris_56.dat [F,inliers]=FRANSAC([chris_56; brian_56; …]) http://www.unc.edu/courses/2004fall/comp/290/089/assignment3/

3. Papers Each should present a paper during 20-25 minutes followed by discussion. Partially outside of class schedule to make up for missed classes. (When?) List of proposed papers will come on-line by Thursday, feel free to propose your own (suggestion: something related to your project). Make choice by Thursday, assignments will be made in class. Everybody should have read papers that are being discussed.

4. Papers Chris Nathan Brian Li Chad Seon Joo Jason Sudipta Sriram Christine http://www.unc.edu/courses/2004fall/comp/290b/089/papers/

5. 3D photography course schedule Introduction Aug 24, 26(no course) Aug.31,Sep.2(no course) Sep. 7, 9(no course) Sep. 14, 16Projective GeometryCamera Model and Calibration (assignment 1) Feb. 21, 23Camera Calib. and SVMFeature matching (assignment 2) Feb. 28, 30Feature trackingEpipolar geometry (assignment 3) Oct. 5, 7Computing FTriangulation and MVG Oct. 12, 14(university day)(fall break) Oct. 19, 21StereoActive ranging Oct. 26, 28Structure from motionSfM and Self-calibration Nov. 2, 4Shape-from-silhouettesSpace carving Nov. 9, 113D modelingAppearance Modeling Nov.12 papers (2-3pm SN115) Nov. 16, 18(VMV’04) Nov. 23, 25papers & discussion(Thanksgiving) Nov.30,Dec.2papers & discussionpapers and discussion Dec.3 papers (2-3pm SN115) Dec. 7?Project presentations

6. Ideas for a project? ChrisWide-area display reconstruction Nathan? Brian? LiVisual-hulls with occlusions ChadLaser scanner for 3D environments Seon JooCollaborative 3D tracking JasonSfM for long sequences Sudipta Combining exact silhouettes and photoconsistency SriramPanoramic cameras self-calibration Christine desktop lamp scanner

7. Dealing with dominant planar scenes USaM fails when common features are all in a plane Solution: part 1 Model selection to detect problem (Pollefeys et al., ECCV‘02)

8. Dealing with dominant planar scenes USaM fails when common features are all in a plane Solution: part 2 Delay ambiguous computations until after self-calibration (couple self-calibration over all 3D parts) (Pollefeys et al., ECCV‘02)

9. Non-sequential image collections 4.8im/pt 64 images 3792 points Problem: Features are lost and reinitialized as new features Solution: Match with other close views

10. For every view i Extract features Compute two view geometry i-1/i and matches Compute pose using robust algorithm Refine existing structure Initialize new structure Relating to more views Problem: find close views in projective frame For every view i Extract features Compute two view geometry i-1/i and matches Compute pose using robust algorithm For all close views k Compute two view geometry k/i and matches Infer new 2D-3D matches and add to list Refine pose using all 2D-3D matches Refine existing structure Initialize new structure

11. Determining close views If viewpoints are close then most image changes can be modelled through a planar homography Qualitative distance measure is obtained by looking at the residual error on the best possible planar homography Distance =

12. 9.8im/pt 4.8im/pt 64 images 3792 points 2170 points Non-sequential image collections (2)

13. Hierarchical structure and motion recovery Compute 2-view Compute 3-view Stitch 3-view reconstructions Merge and refine reconstruction F T H PM

14. Stitching 3-view reconstructions Different possibilities 1. Align (P 2,P 3 ) with (P’ 1,P’ 2 ) 2. Align X,X’ (and C,C’) 3. Minimize reproj. error 4. MLE (merge)

15. Refining structure and motion Minimize reprojection error Maximum Likelyhood Estimation (if error zero-mean Gaussian noise) Huge problem but can be solved efficiently (Bundle adjustment)

16. Sparse bundle adjustment U1U1 U2U2 U3U3 WTWT W V P1P1 P2P2 P3P3 M Non-linear min. requires to solve Jacobian of has sparse block structure 12xm 3xn (in general much larger) im.pts. view 1 Needed for non-linear minimization

17. Sparse bundle adjustment Eliminate dependence of camera/motion parameters on structure parameters Note in general 3n >> 11m WTWT V U-WV -1 W T 11xm 3xn Allows much more efficient computations e.g. 100 views,10000 points, solve  1000x1000, not  30000x30000 Often still band diagonal use sparse linear algebra algorithms

18. Self-calibration Introduction Self-calibration Dual Absolute Quadric Critical Motion Sequences

19. Motivation Avoid explicit calibration procedure Complex procedure Need for calibration object Need to maintain calibration

20. Motivation Allow flexible acquisition No prior calibration necessary Possibility to vary intrinsics Use archive footage

21. Projective ambiguity Reconstruction from uncalibrated images  projective ambiguity on reconstruction

22. Stratification of geometry 15 DOF 12 DOF plane at infinity parallelism More general More structure ProjectiveAffineMetric 7 DOF absolute conic angles, rel.dist.

23. Constraints ? Scene constraints Parallellism, vanishing points, horizon,... Distances, positions, angles,... Unknown scene  no constraints Camera extrinsics constraints –Pose, orientation,... Unknown camera motion  no constraints Camera intrinsics constraints –Focal length, principal point, aspect ratio & skew Perspective camera model too general  some constraints

24. Euclidean projection matrix Factorization of Euclidean projection matrix Intrinsics: Extrinsics: Note: every projection matrix can be factorized, but only meaningful for euclidean projection matrices (camera geometry) (camera motion)

25. Constraints on intrinsic parameters Constant e.g. fixed camera: Known e.g. rectangular pixels: square pixels: principal point known:

26. Self-calibration Upgrade from projective structure to metric structure using constraints on intrinsic camera parameters Constant intrinsics Some known intrinsics, others varying Constraints on intrincs and restricted motion (e.g. pure translation, pure rotation, planar motion) (Faugeras et al. ECCV´92, Hartley´93, Triggs´97, Pollefeys et al. PAMI´99,...) (Heyden&Astrom CVPR´97, Pollefeys et al. ICCV´98,...) (Moons et al.´94, Hartley ´94, Armstrong ECCV´96,...)

27. A counting argument To go from projective (15DOF) to metric (7DOF) at least 8 constraints are needed Minimal sequence length should satisfy Independent of algorithm Assumes general motion (i.e. not critical)

28. Outline Introduction Self-calibration Dual Absolute Quadric Critical Motion Sequences

29. The Dual Absolute Quadric The absolute dual quadric Ω * ∞ is a fixed conic under the projective transformation H iff H is a similarity 1.8 dof 2.plane at infinity π ∞ is the nullvector of Ω ∞ 3.Angles:

30. Absolute Dual Quadric and Self-calibration Eliminate extrinsics from equation Equivalent to projection of Dual Abs.Quadric Dual Abs.Quadric also exists in projective world Transforming world so that reduces ambiguity to similarity

31. ** ** projection constraints Absolute conic = calibration object which is always present but can only be observed through constraints on the intrinsics Absolute Dual Quadric and Self-calibration Projection equation: Translate constraints on K through projection equation to constraints on  *

32. Constraints on  *  Zero skewquadratic m Principal pointlinear 2m2m Zero skew (& p.p.)linear m Fixed aspect ratio (& p.p.& Skew) quadratic m-1 Known aspect ratio (& p.p.& Skew) linear m Focal length (& p.p. & Skew) linear m conditionconstrainttype #constraints

33. Linear algorithm Assume everything known, except focal length (Pollefeys et al.,ICCV´98/IJCV´99) Yields 4 constraint per image Note that rank-3 constraint is not enforced

34. Linear algorithm revisited (Pollefeys et al., ECCV‘02) assumptions Weighted linear equations

35. Projective to metric Compute T from using eigenvalue decomposition of and then obtain metric reconstruction as

36. Alternatives: (Dual) image of absolute conic Equivalent to Absolute Dual Quadric Practical when H  can be computed first Pure rotation (Hartley’94, Agapito et al.’98,’99) Vanishing points, pure translations, modulus constraint, …

37. Note that in the absence of skew the IAC can be more practical than the DIAC!

38. Kruppa equations Limit equations to epipolar geometry Only 2 independent equations per pair But independent of plane at infinity

39. Refinement Metric bundle adjustment Enforce constraints or priors on intrinsics during minimization (this is „self-calibration“ for photogrammetrist )

40. Outline Introduction Self-calibration Dual Absolute Quadric Critical Motion Sequences

41. Critical motion sequences Self-calibration depends on camera motion Motion sequence is not always general enough Critical Motion Sequences have more than one potential absolute conic satisfying all constraints Possible to derive classification of CMS (Sturm, CVPR´97, Kahl, ICCV´99, Pollefeys,PhD´99)

42. Critical motion sequences: constant intrinsic parameters Most important cases for constant intrinsics Critical motion typeambiguity pure translationaffine transformation (5DOF) pure rotation arbitrary position for   (3DOF) orbital motionproj.distortion along rot. axis (2DOF) planar motion scaling axis  plane (1DOF) Note relation between critical motion sequences and restricted motion algorithms

43. Critical motion sequences: varying focal length Most important cases for varying focal length (other parameters known) Critical motion typeambiguity pure rotation arbitrary position for   (3DOF) forward motionproj.distortion along opt. axis (2DOF) translation and rot. about opt. axis scaling optical axis (1DOF) hyperbolic and/or elliptic motion one extra solution

44. Critical motion sequences: algorithm dependent Additional critical motion sequences can exist for some specific algorithms when not all constraints are enforced (e.g. not imposing rank 3 constraint) Kruppa equations/linear algorithm: fixating a point Some spheres also project to circles located in the image and hence satisfy all the linear/kruppa self-calibration constraints

45. Non-ambiguous new views for CMS restrict motion of virtual camera to CMS use (wrong) computed camera parameters (Pollefeys,ICCV´01)

46. Next class: shape from silhouettes