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

Slides:



Advertisements
Similar presentations
Epipolar Geometry.
Advertisements

3D reconstruction.
Jan-Michael Frahm, Enrique Dunn Spring 2012
Two-view geometry.
Recovering metric and affine properties from images
Lecture 8: Stereo.
Epipolar Geometry class 11 Multiple View Geometry Comp Marc Pollefeys.
Camera calibration and epipolar geometry
Recovering metric and affine properties from images
Computer Vision : CISC 4/689
The 2D Projective Plane Points and Lines.
Geometry of Images Pinhole camera, projection A taste of projective geometry Two view geometry:  Homography  Epipolar geometry, the essential matrix.
Robot Vision SS 2008 Matthias Rüther 1 ROBOT VISION Lesson 6: Shape from Stereo Matthias Rüther Slides partial courtesy of Marc Pollefeys Department of.
Epipolar Geometry Class 7 Read notes Feature tracking run iterative L-K warp & upsample Tracking Good features Multi-scale Transl. Affine.
3D reconstruction class 11
Epipolar geometry. (i)Correspondence geometry: Given an image point x in the first view, how does this constrain the position of the corresponding point.
Uncalibrated Geometry & Stratification Sastry and Yang
Epipolar Geometry and the Fundamental Matrix F
Multiple View Geometry Marc Pollefeys University of North Carolina at Chapel Hill Modified by Philippos Mordohai.
Multiple-view Reconstruction from Points and Lines
3D reconstruction of cameras and structure x i = PX i x’ i = P’X i.
CAU Kiel DAGM 2001-Tutorial on Visual-Geometric 3-D Scene Reconstruction 1 The plan for today Leftovers and from last time Camera matrix Part A) Notation,
Multiple View Geometry
Projective 2D geometry Appunti basati sulla parte iniziale del testo
Two-view geometry Epipolar geometry F-matrix comp. 3D reconstruction Structure comp.
Multiple View Geometry Marc Pollefeys University of North Carolina at Chapel Hill Modified by Philippos Mordohai.
Multiple View Geometry Marc Pollefeys University of North Carolina at Chapel Hill Modified by Philippos Mordohai.
Projected image of a cube. Classical Calibration.
May 2004Stereo1 Introduction to Computer Vision CS / ECE 181B Tuesday, May 11, 2004  Multiple view geometry and stereo  Handout #6 available (check with.
Lec 21: Fundamental Matrix
Epipolar geometry Class 5
Multiple View Geometry Marc Pollefeys University of North Carolina at Chapel Hill Modified by Philippos Mordohai.
3-D Scene u u’u’ Study the mathematical relations between corresponding image points. “Corresponding” means originated from the same 3D point. Objective.
Epipolar geometry Class 5. Geometric Computer Vision course schedule (tentative) LectureExercise Sept 16Introduction- Sept 23Geometry & Camera modelCamera.
Multi-view geometry. Multi-view geometry problems Structure: Given projections of the same 3D point in two or more images, compute the 3D coordinates.
776 Computer Vision Jan-Michael Frahm, Enrique Dunn Spring 2013.
Lecture 11 Stereo Reconstruction I Lecture 11 Stereo Reconstruction I Mata kuliah: T Computer Vision Tahun: 2010.
Multi-view geometry.
Epipolar geometry The fundamental matrix and the tensor
Projective cameras Motivation Elements of Projective Geometry Projective structure from motion Planches : –
Lecture 04 22/11/2011 Shai Avidan הבהרה : החומר המחייב הוא החומר הנלמד בכיתה ולא זה המופיע / לא מופיע במצגת.
Stereo Course web page: vision.cis.udel.edu/~cv April 11, 2003  Lecture 21.
Robot Vision SS 2007 Matthias Rüther 1 ROBOT VISION Lesson 6a: Shape from Stereo, short summary Matthias Rüther Slides partial courtesy of Marc Pollefeys.
1 Formation et Analyse d’Images Session 7 Daniela Hall 25 November 2004.
Two-view geometry Epipolar geometry F-matrix comp. 3D reconstruction
Geometry of Multiple Views
Multi-linear Systems and Invariant Theory in the Context of Computer Vision and Graphics CS329 Amnon Shashua.
Two-view geometry. Epipolar Plane – plane containing baseline (1D family) Epipoles = intersections of baseline with image planes = projections of the.
Feature Matching. Feature Space Outlier Rejection.
Computer vision: models, learning and inference M Ahad Multiple Cameras
MASKS © 2004 Invitation to 3D vision Uncalibrated Camera Chapter 6 Reconstruction from Two Uncalibrated Views Modified by L A Rønningen Oct 2008.
Reconstruction from Two Calibrated Views Two-View Geometry
Uncalibrated reconstruction Calibration with a rig Uncalibrated epipolar geometry Ambiguities in image formation Stratified reconstruction Autocalibration.
Lec 26: Fundamental Matrix CS4670 / 5670: Computer Vision Kavita Bala.
Projective 2D geometry course 2 Multiple View Geometry Comp Marc Pollefeys.
Multiview geometry ECE 847: Digital Image Processing Stan Birchfield Clemson University.
Multi-view geometry. Multi-view geometry problems Structure: Given projections of the same 3D point in two or more images, compute the 3D coordinates.
55:148 Digital Image Processing Chapter 11 3D Vision, Geometry
Parameter estimation class 5
Two-view geometry Computer Vision Spring 2018, Lecture 10
Epipolar geometry.
Epipolar Geometry class 11
CS Visual Recognition Projective Geometry Projective Geometry is a mathematical framework describing image formation by perspective camera. Under.
3D Photography: Epipolar geometry
Computer Graphics Recitation 12.
3D reconstruction class 11
Uncalibrated Geometry & Stratification
Two-view geometry.
Two-view geometry.
Multi-view geometry.
Presentation transcript:

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

Three questions: Correspondence geometry: Given an image point x in the first view, how does this constrain the position of the corresponding point x’ in the second image? (ii) Camera geometry (motion): Given a set of corresponding image points {xi ↔x’i}, i=1,…,n, what are the cameras P and P’ for the two views? (iii) Scene geometry (structure): Given corresponding image points xi ↔x’i and cameras P, P’, what is the position of (their pre-image) X in space?

The epipolar geometry C,C’,x,x’ and X are coplanar

The epipolar geometry What if only C,C’,x are known?

The epipolar geometry All points on p project on l and l’

The epipolar geometry Family of planes p and lines l and l’ Intersection in e and e’

The epipolar geometry epipoles e,e’ = intersection of baseline with image plane = projection of projection center in other image = vanishing point of camera motion direction an epipolar plane = plane containing baseline (1-D family) an epipolar line = intersection of epipolar plane with image (always come in corresponding pairs)

Example: converging cameras

Example: motion parallel with image plane

Example: forward motion

The fundamental matrix F algebraic representation of epipolar geometry we will see that mapping is (singular) correlation (i.e. projective mapping from points to lines) represented by the fundamental matrix F

The fundamental matrix F geometric derivation mapping from 2-D to 1-D family (rank 2)

The fundamental matrix F algebraic derivation (note: doesn’t work for C=C’  F=0)

The fundamental matrix F correspondence condition The fundamental matrix satisfies the condition that for any pair of corresponding points x↔x’ in the two images

The fundamental matrix F F is the unique 3x3 rank 2 matrix that satisfies x’TFx=0 for all x↔x’ Transpose: if F is fundamental matrix for (P,P’), then FT is fundamental matrix for (P’,P) Epipolar lines: l’=Fx & l=FTx’ Epipoles: on all epipolar lines, thus e’TFx=0, x e’TF=0, similarly Fe=0 F has 7 d.o.f. , i.e. 3x3-1(homogeneous)-1(rank2) F is a correlation, projective mapping from a point x to a line l’=Fx (not a proper correlation, i.e. not invertible)

The epipolar line geometry l,l’ epipolar lines, k line not through e  l’=F[k]xl and symmetrically l=FT[k’]xl’ (pick k=e, since eTe≠0)

Fundamental matrix for pure translation

Fundamental matrix for pure translation

Fundamental matrix for pure translation example: motion starts at x and moves towards e, faster depending on Z pure translation: F only 2 d.o.f., xT[e]xx=0  auto-epipolar

General motion

Geometric representation of F Fs: Steiner conic, 5 d.o.f. Fa=[xa]x: pole of line ee’ w.r.t. Fs, 2 d.o.f.

Geometric representation of F

Pure planar motion Steiner conic Fs is degenerate (two lines)

Projective transformation and invariance Derivation based purely on projective concepts F invariant to transformations of projective 3-space unique not unique canonical form

Projective ambiguity of cameras given F previous slide: at least projective ambiguity this slide: not more! Show that if F is same for (P,P’) and (P,P’), there exists a projective transformation H so that P=HP and P’=HP’ ~ ~ ~ lemma: (22-15=7, ok)

Canonical cameras given F F matrix corresponds to P,P’ iff P’TFP is skew-symmetric F matrix, S skew-symmetric matrix (fund.matrix=F) Possible choice: Canonical representation:

The essential matrix ~fundamental matrix for calibrated cameras (remove K) 5 d.o.f. (3 for R; 2 for t up to scale) E is essential matrix if and only if two singularvalues are equal (and third=0)

(only one solution where points is in front of both cameras) Four possible reconstructions from E (only one solution where points is in front of both cameras)