Multiple View Geometry Comp 290-089 Marc Pollefeys Computing F class 13 Multiple View Geometry Comp 290-089 Marc Pollefeys

Content Background: Projective geometry (2D, 3D), Parameter estimation, Algorithm evaluation. Single View: Camera model, Calibration, Single View Geometry. Two Views: Epipolar Geometry, 3D reconstruction, Computing F, Computing structure, Plane and homographies. Three Views: Trifocal Tensor, Computing T. More Views: N-Linearities, Multiple view reconstruction, Bundle adjustment, auto-calibration, Dynamic SfM, Cheirality, Duality

Multiple View Geometry course schedule (subject to change) Jan. 7, 9 Intro & motivation Projective 2D Geometry Jan. 14, 16 (no class) Jan. 21, 23 Projective 3D Geometry Jan. 28, 30 Parameter Estimation Feb. 4, 6 Algorithm Evaluation Camera Models Feb. 11, 13 Camera Calibration Single View Geometry Feb. 18, 20 Epipolar Geometry 3D reconstruction Feb. 25, 27 Fund. Matrix Comp. Structure Comp. Mar. 4, 6 Planes & Homographies Trifocal Tensor Mar. 18, 20 Three View Reconstruction Multiple View Geometry Mar. 25, 27 MultipleView Reconstruction Bundle adjustment Apr. 1, 3 Auto-Calibration Papers Apr. 8, 10 Dynamic SfM Apr. 15, 17 Cheirality Apr. 22, 24 Duality Project Demos

Two-view geometry Epipolar geometry 3D reconstruction F-matrix comp. Structure comp.

Fundamental matrix (3x3 rank 2 matrix) Epipolar geometry l2 C1 m1 L1 m2 L2 M C2 C1 C2 l2 p l1 e1 e2 m1 L1 m2 L2 M C1 C2 l2 p l1 e1 e2 Underlying structure in set of matches for rigid scenes m1 m2 lT1 l2 Fundamental matrix (3x3 rank 2 matrix) Computable from corresponding points Simplifies matching Allows to detect wrong matches Related to calibration Canonical representation:

The projective reconstruction theorem If a set of point correspondences in two views determine the fundamental matrix uniquely, then the scene and cameras may be reconstructed from these correspondences alone, and any two such reconstructions from these correspondences are projectively equivalent allows reconstruction from pair of uncalibrated images!

Objective Given two uncalibrated images compute (PM,P‘M,{XMi}) (i.e. within similarity of original scene and cameras) Algorithm Compute projective reconstruction (P,P‘,{Xi}) Compute F from xi↔x‘i Compute P,P‘ from F Triangulate Xi from xi↔x‘i Rectify reconstruction from projective to metric Direct method: compute H from control points Stratified method: Affine reconstruction: compute p∞ Metric reconstruction: compute IAC w

F F,H∞ p∞ w,w’ W∞ Image information provided View relations and projective objects 3-space objects reconstruction ambiguity point correspondences F projective point correspondences including vanishing points F,H∞ p∞ affine Points correspondences and internal camera calibration w,w’ W∞ metric

separate known from unknown Epipolar geometry: basic equation separate known from unknown (data) (unknowns) (linear)

the singularity constraint SVD from linearly computed F matrix (rank 3) Compute closest rank-2 approximation

the minimum case – 7 point correspondences one parameter family of solutions but F1+lF2 not automatically rank 2

the minimum case – impose rank 2 F1 F2 F 3 F7pts (obtain 1 or 3 solutions) (cubic equation) Compute possible l as eigenvalues of (only real solutions are potential solutions)

! the NOT normalized 8-point algorithm Orders of magnitude difference ~10000 ~100 1 ! Orders of magnitude difference Between column of data matrix  least-squares yields poor results

Transform image to ~[-1,1]x[-1,1] the normalized 8-point algorithm Transform image to ~[-1,1]x[-1,1] (0,0) (700,500) (700,0) (0,500) (1,-1) (0,0) (1,1) (-1,1) (-1,-1) Least squares yields good results (Hartley, PAMI´97)

algebraic minimization possible to iteratively minimize algebraic distance subject to det F=0 (see book if interested)

Geometric distance Gold standard Sampson error Symmetric epipolar distance

Gold standard Maximum Likelihood Estimation (= least-squares for Gaussian noise) Initialize: normalized 8-point, (P,P‘) from F, reconstruct Xi Parameterize: (overparametrized) Minimize cost using Levenberg-Marquardt (preferably sparse LM, see book)

Gold standard Alternative, minimal parametrization (with a=1) (note (x,y,1) and (x‘,y‘,1) are epipoles) problems: a=0  pick largest of a,b,c,d to fix epipole at infinity  pick largest of x,y,w and of x’,y’,w’ 4x3x3=36 parametrizations! reparametrize at every iteration, to be sure

Zhang&Loop’s approach CVIU’01

First-order geometric error (Sampson error) (one eq./point JJT scalar) (problem if some x is located at epipole) advantage: no subsidiary variables required

Symmetric epipolar error

Some experiments:

Some experiments:

Some experiments:

Some experiments: Residual error: (for all points!)

Recommendations: Do not use unnormalized algorithms Quick and easy to implement: 8-point normalized Better: enforce rank-2 constraint during minimization Best: Maximum Likelihood Estimation (minimal parameterization, sparse implementation)

Special case: Enforce constraints for optimal results: Pure translation (2dof), Planar motion (6dof), Calibrated case (5dof)

The envelope of epipolar lines What happens to an epipolar line if there is noise? Monte Carlo n=10 n=15 n=25 n=50

Other entities? Lines give no constraint for two view geometry (but will for three and more views) Curves and surfaces yield some constraints related to tangency

Automatic computation of F Interest points Putative correspondences RANSAC (iv) Non-linear re-estimation of F Guided matching (repeat (iv) and (v) until stable)

Extract feature points to relate images Required properties: Well-defined (i.e. neigboring points should all be different) Stable across views (i.e. same 3D point should be extracted as feature for neighboring viewpoints)

(e.g.Harris&Stephens´88; Shi&Tomasi´94) Feature points (e.g.Harris&Stephens´88; Shi&Tomasi´94) Find points that differ as much as possible from all neighboring points homogeneous edge corner M should have large eigenvalues Feature = local maxima (subpixel) of F(1,  2)

Select strongest features (e.g. 1000/image) Feature points Select strongest features (e.g. 1000/image)

? Evaluate NCC for all features with similar coordinates Feature matching Evaluate NCC for all features with similar coordinates Keep mutual best matches Still many wrong matches! ?

Feature example 1 5 2 4 3 1 5 2 4 3 Gives satisfying results 0.96 -0.40 -0.16 -0.39 0.19 -0.05 0.75 -0.47 0.51 0.72 -0.18 0.73 0.15 -0.75 -0.27 0.49 0.16 0.79 0.21 0.08 0.50 -0.45 0.28 0.99 1 5 2 4 3 Gives satisfying results for small image motions

Requirement to cope with larger variations between images Wide-baseline matching… Requirement to cope with larger variations between images Translation, rotation, scaling Foreshortening Non-diffuse reflections Illumination geometric transformations photometric changes

(Tuytelaars and Van Gool BMVC 2000) Wide-baseline matching… (Tuytelaars and Van Gool BMVC 2000) Wide baseline matching for two different region types

RANSAC Step 1. Extract features Step 2. Compute a set of potential matches Step 3. do Step 3.1 select minimal sample (i.e. 7 matches) Step 3.2 compute solution(s) for F Step 3.3 determine inliers until (#inliers,#samples)<95% (generate hypothesis) (verify hypothesis) Step 4. Compute F based on all inliers Step 5. Look for additional matches Step 6. Refine F based on all correct matches #inliers 90% 80% 70% 60% 50% #samples 5 13 35 106 382

restrict search range to neighborhood of epipolar line Finding more matches restrict search range to neighborhood of epipolar line (1.5 pixels) relax disparity restriction (along epipolar line)

Model selection (Torr et al., ICCV´98, Kanatani, Akaike) Degenerate cases: Degenerate cases Planar scene Pure rotation No unique solution Remaining DOF filled by noise Use simpler model (e.g. homography) Model selection (Torr et al., ICCV´98, Kanatani, Akaike) Compare H and F according to expected residual error (compensate for model complexity)

Absence of sufficient features (no texture) More problems: Absence of sufficient features (no texture) Repeated structure ambiguity Robust matcher also finds support for wrong hypothesis solution: detect repetition (Schaffalitzky and Zisserman, BMVC‘98)

geometric relations between two views is fully two-view geometry geometric relations between two views is fully described by recovered 3x3 matrix F

Next class: image pair rectification reconstructing points and lines