Multiple View Geometry

Slides:

Advertisements

Similar presentations

The fundamental matrix F

Advertisements

Lecture 11: Two-view geometry

CSE473/573 – Stereo and Multiple View Geometry

Internet Vision - Lecture 3 Tamara Berg Sept 10. New Lecture Time Mondays 10:00am-12:30pm in 2311 Monday (9/15) we will have a general Computer Vision.

Silvina Rybnikov Supervisors: Prof. Ilan Shimshoni and Prof. Ehud Rivlin HomePage:

Dana Cobzas-PhD thesis Image-Based Models with Applications in Robot Navigation Dana Cobzas Supervisor: Hong Zhang.

Structure from motion.

Last Time Pinhole camera model, projection

Copyright  Philipp Slusallek Cs fall IBR: Model-based Methods Philipp Slusallek.

Multiple View Geometry : Computational Photography Alexei Efros, CMU, Fall 2005 © Martin Quinn …with a lot of slides stolen from Steve Seitz and.

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.

Multi-view stereo Many slides adapted from S. Seitz.

COMP322/S2000/L23/L24/L251 Camera Calibration The most general case is that we have no knowledge of the camera parameters, i.e., its orientation, position,

May 2004Stereo1 Introduction to Computer Vision CS / ECE 181B Tuesday, May 11, 2004  Multiple view geometry and stereo  Handout #6 available (check with.

CSE473/573 – Stereo Correspondence

Advanced Computer Vision Structure from Motion. Geometric structure-from-motion problem: using image matches to estimate: The 3D positions of the corresponding.

Scene planes and homographies. Homographies given the plane and vice versa.

CSCE 641 Computer Graphics: Image-based Modeling (Cont.) Jinxiang Chai.

55:148 Digital Image Processing Chapter 11 3D Vision, Geometry Topics: Basics of projective geometry Points and hyperplanes in projective space Homography.

David Luebke Modeling and Rendering Architecture from Photographs A hybrid geometry- and image-based approach Debevec, Taylor, and Malik SIGGRAPH.

55:148 Digital Image Processing Chapter 11 3D Vision, Geometry Topics: Basics of projective geometry Points and hyperplanes in projective space Homography.

CSE 6367 Computer Vision Stereo Reconstruction Camera Coordinate Transformations “Everything should be made as simple as possible, but not simpler.” Albert.

Multiple View Geometry in Computer Vision Slides modified from Marc Pollefeys’ online course materials Lecturer: Prof. Dezhen Song.

Automatic Camera Calibration

A Brief Overview of Computer Vision Jinxiang Chai.

1 Intelligent Robotics Research Centre (IRRC) Department of Electrical and Computer Systems Engineering Monash University, Australia Visual Perception.

Lecture 11 Stereo Reconstruction I Lecture 11 Stereo Reconstruction I Mata kuliah: T Computer Vision Tahun: 2010.

Final Exam Review CS485/685 Computer Vision Prof. Bebis.

Lighting-up geometry: accurate 3D modelling with a torch and a camera George Vogiatzis, Carlos Hernández, Roberto Cipolla University of Cambridge, Toshiba.

IMAGE MOSAICING Summer School on Document Image Processing

Y. Moses 11 Combining Photometric and Geometric Constraints Yael Moses IDC, Herzliya Joint work with Ilan Shimshoni and Michael Lindenbaum, the Technion.

CSCE 5013 Computer Vision Fall 2011 Prof. John Gauch

CSCE 643 Computer Vision: Structure from Motion

Metrology 1.Perspective distortion. 2.Depth is lost.

Correspondence-Free Determination of the Affine Fundamental Matrix (Tue) Young Ki Baik, Computer Vision Lab.

Computer Science Department Pacific University Artificial Intelligence -- Computer Vision.

1 Formation et Analyse d’Images Session 7 Daniela Hall 25 November 2004.

Acquiring 3D models of objects via a robotic stereo head David Virasinghe Department of Computer Science University of Adelaide Supervisors: Mike Brooks.

© 2005 Martin Bujňák, Martin Bujňák Supervisor : RNDr.

Lecture 03 15/11/2011 Shai Avidan הבהרה : החומר המחייב הוא החומר הנלמד בכיתה ולא זה המופיע / לא מופיע במצגת.

Single-view geometry Odilon Redon, Cyclops, 1914.

Autonomous Navigation Based on 2-Point Correspondence 2-Point Correspondence using ROS Submitted By: Li-tal Kupperman, Ran Breuer Advisor: Majd Srour,

Feature Matching. Feature Space Outlier Rejection.

Cherevatsky Boris Supervisors: Prof. Ilan Shimshoni and Prof. Ehud Rivlin

CSE 140: Computer Vision Camillo J. Taylor Assistant Professor CIS Dept, UPenn.

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.

Computer vision: models, learning and inference M Ahad Multiple Cameras

Visual Odometry David Nister, CVPR 2004

Computational Rephotography Soonmin Bae Aseem Agarwala Frédo Durand.

3D Reconstruction Using Image Sequence

Geometry Reconstruction March 22, Fundamental Matrix An important problem: Determine the epipolar geometry. That is, the correspondence between.

Model Refinement from Planar Parallax Anthony DickRoberto Cipolla Department of Engineering University of Cambridge.

MASKS © 2004 Invitation to 3D vision. MASKS © 2004 Invitation to 3D vision Lecture 1 Overview and Introduction.

Correspondence and Stereopsis. Introduction Disparity – Informally: difference between two pictures – Allows us to gain a strong sense of depth Stereopsis.

55:148 Digital Image Processing Chapter 11 3D Vision, Geometry

Advanced Computer Graphics

3D Single Image Scene Reconstruction For Video Surveillance Systems

Think-Pair-Share What visual or physiological cues help us to perceive 3D shape and depth?

Answering ‘Where am I?’ by Nonlinear Least Squares

Epipolar geometry.

دکتر سعید شیری قیداری & فصل 4 کتاب

Advanced Computer Vision

Multiple View Geometry in Computer Vision

Multiple View Geometry for Robotics

Course 6 Stereo.

Single-view geometry Odilon Redon, Cyclops, 1914.

Lecture 15: Structure from motion

Presentation transcript:

Multiple View Geometry Ilan Shimshoni Dept of Management Information Systems University of Haifa ishimshoni@mis.haifa.ac.il

Examples of Applications Visual robot navigation 3D reconstruction from video Was there a goal in a game from two video cameras?

What do we know? Camera parameters Camera position with respect to the scene Relative camera position (known or constrained) Objects in the scene Their positions Characteristics of objects in the scene Nothing!!!

The more we know the easier it is. The more we need to know the harder it is to perform the experiment. Example stereo reconstruction Camera parameters known Relative camera positions known and constrained Goal: reconstruct scene. Find for each point in the first image its corresponding point in the second image. How can we make the task easier?

Another example: scene reconstruction from a video sequence Camera parameters not known Camera positions not known Much easier for the user Something in between: Scene reconstruction from a set of images taken by a plane (geodesic dept) Position of plane known quite accurately High quality camera which has been accurately calibrated

How do we solve problems? Example: Pose estimation The algorithm is given: A model of a 3D object The geometric imaging parameters of the camera An image of the object Goal: Calculate accurately the position of the camera with respect to the object

Applications A robot would like to grab an object with its gripper using a camera which is positioned on the robot A robot would like to know where it is. The object is the room. A satellite would like to know its orientation in space from an image it took of the stars. The object is the universe

The input:

Steps in developing an algorithm Define f(X,x,) Find a method to compute  such that f(X,x,) = 0 What should we do if there is measurement noise in x? How do we match x to X? How do we deal with incorrect matches (outliers)? Build a full system.

Criminisi

MIT city scanning project Produce image hemispheres Localize hemispheres using GPS Match hemispheres using vision methods Find accurate positions of cameras Reconstruct buildings Reconstruct fine details

3D reconstruction from image sequence

Cipolla 2008

Elections November 2008