Image processing and computer VISIon Chapter 8: Stereo vision Stereo vO. a
Image processing and computer vision Chapter 8: Stereo vision Stereo v0.a 1
3-D computer vision, an overview Find 3-d structure using 2-D images One camera: Possible but limited results, e.g. by identify vanishing lines Two cameras: Stereo: use Epipolar geometry( this chapter) Three cameras Trifocal tensor method (very advanced not discussed here cameras Factorization(Chapter 9), fast method, moderate accuracy Bundle adjustment chapter 11), slow but accurate Stereo vO. a
3-D computer vision, an overview • Find 3-D structure using 2-D images • One camera: – Possible but limited results, e.g. by identify vanishing lines • Two cameras: – Stereo: use Epipolar geometry (This chapter) • Three cameras: – Trifocal tensor method (very advanced not discussed here) • N cameras: – Factorization (Chapter 9), fast method, moderate accuracy – Bundle adjustment (chapter 11), slow but accurate. Stereo v0.a 2
Intro Essential Mat. I Fundamental Mat. I Epipolar Geom. I Corresp. I Reconst In this chapter you will learn Stereo camera setup Essential matrix (E)for describing the geometry between two cameras of known intrinsic parameters Fundamental matrix (f) for describing the geometry between two cameras of unknown intrinsic parameters Epipolar geometry parameters and characteristics The correspondent problem 3D Reconstruction using stereo vision Stereo vO. a
Intro. | Essential Mat. | Fundamental Mat. | Epipolar Geom. | Corresp. | Reconst. In this chapter you will learn • Stereo camera setup • Essential matrix (E)for describing the geometry between two cameras of known intrinsic parameters • Fundamental matrix (F) for describing the geometry between two cameras of unknown intrinsic parameters • Epipolar geometry: parameters and characteristics • The Correspondent problem • 3D Reconstruction using stereo vision Stereo v0.a 3
Intro. Essential Mat. Fundamental Mat. Epipolar Geom. Corresp. Reconst Part 1: A simple approach 3-D reconstruction from stereo mages Assumption: the cameras are paralle/ (2 principal axes are parallel) and the camera shift is only in the horizontal direction Stereo vO. a
Intro. | Essential Mat. | Fundamental Mat. | Epipolar Geom. | Corresp. | Reconst. Part 1: A simple approach 3-D reconstruction from stereo images Assumption : the cameras are parallel (2 principal axes are parallel) and the camera shift is only in the horizontal direction Stereo v0.a 4
Intro Essential Mat. I Fundamental Mat. I Epipolar Geom. I Corresp. I Reconst Introduction to stereo vision Objectives: Basic idea of stereo vision Stereo reconstruction by epipolar geometry Stereo camera pair calibration find Fundamental matriX F) Construct the 3d ( graphic) model from 2 images Inside a computer e.g. Graphic 3-Dobⅰect model in a game Stereo vO. a
Intro. | Essential Mat. | Fundamental Mat. | Epipolar Geom. | Corresp. | Reconst. Introduction to Stereo Vision • Objectives: – Basic idea of stereo vision – Stereo reconstruction by epipolar geometry • Stereo camera pair calibration (find Fundamental matrix F) • Construct the 3D (graphic) model from 2 images Stereo v0.a 5 e.g. Graphic model in a game Inside a computer 3-D object