Lecture 12: Structure from motion

1. CS6670: Computer Vision Noah Snavely Lecture 12: Structure from motion

2. Readings • Szeliski, Chapter 7.1 – 7.4

3. Announcements • Project 2 due Sunday, March 13 • Two-person groups • Quiz on Monday

4. Large-scale structure from motion Dubrovnik, Croatia. 4,619 images (out of an initial 57,845). Total reconstruction time: 23 hours Number of cores: 352

5. Structure from motion • Given many images, how can we a) figure out where they were all taken from? b) build a 3D model of the scene? This is (roughly) the structure from motion problem

6. Structure from motion Reconstruction (side) (top) • Input: images with points in correspondence pi,j = (ui,j,vi,j) • Output • structure: 3D location xi for each point pi • motion: camera parameters Rj ,tj possibly Kj • Objective function: minimize reprojection error

7. Camera calibration and triangulation • Suppose we know 3D points • And have matches between these points and an image • How can we compute the camera parameters? • Suppose we have know camera parameters, each of which observes a point • How can we compute the 3D location of that point?

8. Structure from motion • SfM solves both of these problems at once • A kind of chicken-and-egg problem • (but solvable)

9. Photo Tourism

10. First step: how to get correspondence? • Feature detection and matching

11. Feature detection Detect features using SIFT [Lowe, IJCV 2004]

12. Feature detection Detect features using SIFT [Lowe, IJCV 2004]

13. Feature matching Match features between each pair of images

14. Feature matching Refine matching using RANSAC to estimate fundamental matrix between each pair

15. Image connectivity graph (graph layout produced using the Graphviz toolkit: http://www.graphviz.org/)

16. x4 x1 x3 x2 x5 x7 x6 p1,1 p1,3 p1,2 Image 1 Image 3 R3,t3 R1,t1 Image 2 R2,t2

17. p4 minimize p1 p3 f(R,T,P) p2 p5 p7 p6 Structure from motion Camera 1 Camera 3 R1,t1 R3,t3 Camera 2 R2,t2

18. Problem size • Trevi Fountain collection 466 input photos + > 100,000 3D points = very large optimization problem

19. Incremental structure from motion

20. Incremental structure from motion

21. Incremental structure from motion

22. Photo Explorer

27. Questions? • 3-minute break

28. (xn,yn) copy of first image Related topic: Drift (x1,y1) • add another copy of first image at the end • this gives a constraint: yn = y1 • there are a bunch of ways to solve this problem • add displacement of (y1 – yn)/(n - 1) to each image after the first • compute a global warp: y’ = y + ax • run a big optimization problem, incorporating this constraint • best solution, but more complicated • known as “bundle adjustment”

29. Global optimization track • Minimize a global energy function: • What are the variables? • The translation tj = (xj, yj) for each image Ij • What is the objective function? • We have a set of matched features pi,j = (ui,j, vi,j) • We’ll call these tracks • For each point match (pi,j, pi,j+1): pi,j+1 – pi,j = tj+1 – tj p4,4 p1,2 p3,4 p4,1 p3,3 p1,1 p1,3 p2,3 p2,4 p2,2 I1 I2 I3 I4

30. Global optimization p4,4 p1,2 p3,4 p4,1 p3,3 p1,1 p1,3 p2,3 p2,4 p2,2 I1 I2 I3 I4 wij= 1 if track i is visible in images j and j+1 0otherwise minimize p1,2 –p1,1 = t2 – t1 p1,3 –p1,2 = t3 – t2 p2,3 –p2,2 = t3 – t2 … v4,1 –v4,4 = y1 – y4

31. Global optimization p4,4 p1,2 p3,4 p4,1 p3,3 p1,1 p1,3 p2,3 p2,4 p2,2 I1 I2 I3 I4 x b A 2m x 1 2n x 1 2m x 2n

32. Global optimization x b A 2m x 1 2n x 1 2m x 2n Defines a least squares problem: minimize • Solution: • Problem: there is no unique solution for ! (det = 0) • We can add a global offset to a solution and get the same error

33. Ambiguity in global location • Each of these solutions has the same error • Called the gauge ambiguity • Solution: fix the position of one image (e.g., make the origin of the 1st image (0,0)) (0,0) (-100,-100) (200,-200)

34. Solving for camera rotation • Instead of spherically warping the images and solving for translation, we can directly solve for the rotation Rj of each camera • Can handle tilt / twist

35. Solving for rotations p11 = (u11, v11) R1 f I1 R2 (u11, v11, f) = p11 p12 = (u12, v12) R2p22 R1p11 I2

36. Solving for rotations minimize

37. 3D rotations • How many degrees of freedom are there? • How do we represent a rotation? • Rotation matrix (too many degrees of freedom) • Euler angles (e.g. yaw, pitch, and roll) – bad idea • Quaternions (4-vector on unit sphere) • Usually involves non-linear optimization

38. x4 x1 x3 x2 x5 x7 x6 p1,1 p1,3 p1,2 Image 1 Image 3 R3,t3 R1,t1 Image 2 R2,t2

39. SfM objective function • Given point xand rotation and translation R, t • Minimize sum of squared reprojection errors: predicted image location observed image location

40. Solving structure from motion • Minimizing g is difficult • g is non-linear due to rotations, perspective division • lots of parameters: 3 for each 3D point, 6 for each camera • difficult to initialize • gauge ambiguity: error is invariant to a similarity transform (translation, rotation, uniform scale) • Many techniques use non-linear least-squares (NLLS) optimization (bundle adjustment) • Levenberg-Marquardt is one common algorithm for NLLS • Lourakis, The Design and Implementation of a Generic Sparse Bundle Adjustment Software Package Based on the Levenberg-Marquardt Algorithm, http://www.ics.forth.gr/~lourakis/sba/ • http://en.wikipedia.org/wiki/Levenberg-Marquardt_algorithm

41. Extensions to SfM • Can also solve for intrinsic parameters (focal length, radial distortion, etc.) • Can use a more robust function than squared error, to avoid fitting to outliers • For more information, see: Triggs, et al, “Bundle Adjustment – A Modern Synthesis”, Vision Algorithms 2000.

42. Questions?