StereoVision_1 - Nanyang Technological University

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Relation of Baseline to Accuracy As cameras become closer together, baseline distance T is reduced As T → Estimation for Z is unstable as cameras get infinitesimally close together Larger baseline is better for estimation accuracy School of Computer Engineering Nanyang Technological University 0, δZ = lim T →0 2 Z fT SC437 Computer Vision and Image Processing δd → ∞ TJ Cham 2002-03 / S2
Recap on Parallax and 1D Triangulation Parallax / no parallax School of Computer Engineering Nanyang Technological University Parallax when projection centers are displaced Depth from 2D triangulation Z = f T / (x l -x r ) Disparity map depth map Depth estimation accuracy Relation of Z estimation to baseline distance T Larger T gives more accurate Z estimation SC437 Computer Vision and Image Processing TJ Cham 2002-03 / S2
Page 1 and 2: School of Computer Engineering Nany
Page 3 and 4: Range Sensing Radars and Sonars Tim
Page 5 and 6: Representation of Raw Range Data Cl
Page 7 and 8: Fitting 3D Models to Range Data We
Page 9 and 10: Parallax Parallax refers to the obs
Page 11 and 12: Parallax from Z Translation Paralla
Page 13 and 14: No Parallax from Pure Rotation No p
Page 15 and 16: Simple 2D Triangulation Assume 1D c
Page 17 and 18: Simple 2D Triangulation Depth from
Page 19: Relation of Baseline to Accuracy Le
Page 23 and 24: Matching Image Points Appearance-ba
Page 25 and 26: Appearance-based Matching Assumptio
Page 27 and 28: Minimize Sum-of-Squares Difference
Page 29 and 30: Appearance Matching Example Point G
Page 31 and 32: Matching Accuracy Examples SSD Imag
Page 33 and 34: S( x 0 Quantifying Matching Localiz
Page 35 and 36: Cornerness Cornerness is defined as
Page 37 and 38: Recap on Appearance-Based Matching
Page 39 and 40: Feature-based Matching Want to matc
Page 41 and 42: Feature-based Matching Example Exam
Page 43 and 44: Feature Matching Heuristics Example
Page 45 and 46: Geometric Search Constraint A very
Page 47 and 48: Stereo Rig for Epipolar Geometry Sc
Page 49 and 50: Epipoles and Epipolar Lines Example
Page 51 and 52: Epipolar Geometry Derivation P l,
Page 53 and 54: Epipolar Geometry Derivation Subst
Page 55 and 56: Fundamental Matrix Equation Affin
Page 57 and 58: Using Epipolar Geometry If we know
Page 59 and 60: Estimating Fundamental Matrix The F
Page 61 and 62: Estimating Fundamental Matrix We ca
Page 63 and 64: 3D Reconstruction from Perspective
Page 65 and 66: 3D Reconstruction from Perspective
Page 67 and 68: 3D Reconstruction from Weak- Perspe
Page 69 and 70: Recap on 3D Reconstruction 3D recon
Page 71 and 72:
Example Application of 3D Reconstru
Page 73:
Summary of 3D Stereo Vision Represe
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StereoVision_1 - Nanyang Technological University

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