Day 1 how do we represent the shape around us? course outline motivation for gathering geometry from multiple images –our eyes are two views –structure.

Slides:



Advertisements
Similar presentations
Projective 3D geometry class 4
Advertisements

Computing 3-view Geometry Class 18
MASKS © 2004 Invitation to 3D vision Lecture 7 Step-by-Step Model Buidling.
Multiple View Reconstruction Class 24 Multiple View Geometry Comp Marc Pollefeys.
Self-calibration.
More single view geometry Describes the images of planes, lines,conics and quadrics under perspective projection and their forward and backward properties.
Epipolar Geometry class 11 Multiple View Geometry Comp Marc Pollefeys.
Structure from motion.
Self-calibration and multi-view geometry Class 10 Read Chapter 6 and 3.2.
Scene Planes and Homographies class 16 Multiple View Geometry Comp Marc Pollefeys.
More on single-view geometry class 10 Multiple View Geometry Comp Marc Pollefeys.
3D reconstruction class 11
Projective 2D geometry (cont’) course 3
Parameter estimation class 5 Multiple View Geometry Comp Marc Pollefeys.
Epipolar geometry. (i)Correspondence geometry: Given an image point x in the first view, how does this constrain the position of the corresponding point.
Structure from motion. Multiple-view geometry questions Scene geometry (structure): Given 2D point matches in two or more images, where are the corresponding.
Uncalibrated Geometry & Stratification Sastry and Yang
Multiple View Geometry Marc Pollefeys University of North Carolina at Chapel Hill Modified by Philippos Mordohai.
3D reconstruction of cameras and structure x i = PX i x’ i = P’X i.
Many slides and illustrations from J. Ponce
Self-calibration Class 21 Multiple View Geometry Comp Marc Pollefeys.
Multiple View Geometry
Multiple View Geometry
Multiple View Geometry in Computer Vision
 -Linearities and Multiple View Tensors Class 19 Multiple View Geometry Comp Marc Pollefeys.
More on single-view geometry class 10 Multiple View Geometry Comp Marc Pollefeys.
Lec 21: Fundamental Matrix
3D photography Marc Pollefeys Fall 2004 / Comp Tue & Thu 9:30-10:45
1 Three-view geometry 3-view constraint along F Minimal algebraic sol The content described in these slides is not required in the final exam!
Projective 2D geometry course 2 Multiple View Geometry Comp Marc Pollefeys.
Multiple View Geometry
The Trifocal Tensor Class 17 Multiple View Geometry Comp Marc Pollefeys.
Multiple View Geometry. THE GEOMETRY OF MULTIPLE VIEWS Reading: Chapter 10. Epipolar Geometry The Essential Matrix The Fundamental Matrix The Trifocal.
55:148 Digital Image Processing Chapter 11 3D Vision, Geometry Topics: Basics of projective geometry Points and hyperplanes in projective space Homography.
Projective 2D geometry course 2 Multiple View Geometry Comp Marc Pollefeys.
Multiple View Geometry in Computer Vision Slides modified from Marc Pollefeys’ online course materials Lecturer: Prof. Dezhen Song.
Automatic Camera Calibration
Computer vision: models, learning and inference
1 Preview At least two views are required to access the depth of a scene point and in turn to reconstruct scene structure Multiple views can be obtained.
Projective cameras Motivation Elements of Projective Geometry Projective structure from motion Planches : –
Pole/polar consider projective geometry again we may want to compute the tangents of a curve that pass through a point (e.g., visibility) let C be a conic.
Projective cameras Trifocal tensors Euclidean/projective SFM Self calibration Line geometry Purely projective cameras Je ne suis pas la la semaine prochaine.
Structure from Motion Course web page: vision.cis.udel.edu/~cv April 25, 2003  Lecture 26.
Day x: Infinity DLT alg HZ 4.1 Rectification HZ 2.7 Hierarchy of maps Invariants HZ 2.4 Projective transform HZ 2.3 Behaviour at infinity Primitives pt/line/conic.
Two-view geometry Epipolar geometry F-matrix comp. 3D reconstruction
HONGIK UNIVERSITY School of Radio Science & Communication Engineering Visual Information Processing Lab Hong-Ik University School of Radio Science & Communication.
1 Camera calibration based on arbitrary parallelograms 授課教授:連震杰 學生:鄭光位.
Mixing Catadioptric and Perspective Cameras
3D reconstruction from uncalibrated images
55:148 Digital Image Processing Chapter 11 3D Vision, Geometry Topics: Basics of projective geometry Points and hyperplanes in projective space Homography.
55:148 Digital Image Processing Chapter 11 3D Vision, Geometry Topics: Basics of projective geometry Points and hyperplanes in projective space Homography.
776 Computer Vision Jan-Michael Frahm & Enrique Dunn Spring 2013.
Auto-calibration we have just calibrated using a calibration object –another calibration object is the Tsai grid of Figure 7.1 on HZ182, which can be used.
MASKS © 2004 Invitation to 3D vision Uncalibrated Camera Chapter 6 Reconstruction from Two Uncalibrated Views Modified by L A Rønningen Oct 2008.
Uncalibrated reconstruction Calibration with a rig Uncalibrated epipolar geometry Ambiguities in image formation Stratified reconstruction Autocalibration.
Structure from motion Multi-view geometry Affine structure from motion Projective structure from motion Planches : –
Projective cameras The end of projective SFM Euclidean upgrades Line geometry Purely projective cameras Présentations mercredi 26 mai de 9h30 à midi, salle.
EECS 274 Computer Vision Projective Structure from Motion.
MASKS © 2004 Invitation to 3D vision. MASKS © 2004 Invitation to 3D vision Lecture 1 Overview and Introduction.
Lec 26: Fundamental Matrix CS4670 / 5670: Computer Vision Kavita Bala.
Projective 2D geometry course 2 Multiple View Geometry Comp Marc Pollefeys.
55:148 Digital Image Processing Chapter 11 3D Vision, Geometry
L-infinity minimization in geometric vision problems.
Parameter estimation class 5
Two-view geometry Computer Vision Spring 2018, Lecture 10
Epipolar Geometry class 11
More on single-view geometry class 10
3D reconstruction class 11
Multiple View Geometry in Computer Vision
Uncalibrated Geometry & Stratification
Presentation transcript:

Day 1 how do we represent the shape around us? course outline motivation for gathering geometry from multiple images –our eyes are two views –structure from motion: Pollefeys Medusa mask video –sfm and camera positions: UW phototourism video –(Boujou) Reading: HZ Chapter 1; Pollefeys and Van Gool ‘From Images to 3D Models’, CACM 2002 (see website) pop quiz (for projective geometry): what is the implicit equation of the line through the points (1,2) and (3,4)? what is the intersection point of the two lines 5x+y=2 and x-2y=1?

Day 1 diagram Projective geometry quiz UW/Szeliski Phototourism video Pollefeys Medusa video Course outline Reading: HZ Ch 1 + Polle CACM

Major topics everything is about matrices projective geometry (2d and 3d) –how are points and lines encoded for simplicity of computation? what aspects of shape are preserved under imaging? how are conics and quadrics represented? how are tangents represented? how is shape at infinity encoded, for consistency of implementation? behaviour at infinity is crucial for calibration fundamental matrix –encodes epipolar geometry and 2-view geometry trifocal tensor –encodes three-view geometry camera matrix –encodes camera geometry auto-calibration with image of absolute conic –for metric reconstruction bundle adjustment –for more than 3 images and removing noise in some of the images