Goal 4Calibration Camera Calibration Camera matrix
Reconstruction get_mapped.m (WP3)
Slide 5
Camera Calibration (Camera resectioning) 5Calibration = the
process of finding the true parameters of the camera that produced
a given photograph or video Pinhole camera model Camera matrix
Represents the camera parameters
Slide 6
Pinhole camera model 6Calibration Describes the mathematical
relationship between the coordinates of a 3D point and its
projection onto the image plane of an ideal pinhole camera
Slide 7
Camera matrix 7Calibration Describes the mapping of a pinhole
camera from 3D points in the world to 2D points in an image.
Intrinsic parametersExtrinsic parameters
Camera matrix 10Calibration Describes the mapping of a pinhole
camera from 3D points in the world to 2D points in an image.
Intrinsic parametersExtrinsic parameters
Slide 11
Camera matrix 11Calibration Describes the mapping of a pinhole
camera from 3D points in the world to 2D points in an image.
Intrinsic parametersExtrinsic parameters
Slide 12
Camera matrix 12Calibration Describes the mapping of a pinhole
camera from 3D points in the world to 2D points in an image.
Intrinsic parametersExtrinsic parameters
Slide 13
Camera matrix 13Calibration Describes the mapping of a pinhole
camera from 3D points in the world to 2D points in an image.
Intrinsic parametersExtrinsic parameters (Bouguet) (POSIT)
Slide 14
Camera Calibration Toolbox for Matlab (Bouguet) Input: 20+
images of a planar checkerboard Output: Intrinsic parameters Corner
extraction Automatic: RADOCC Camera Calibration Toolbox
(CornerFinder.m) 14Calibration
Slide 15
Camera Calibration Toolbox for Matlab (Bouguet) Input: 30
images of each camera (squares = 28mm) Camera 1: Camera 2:
15Calibration
Slide 16
Camera Calibration Toolbox for Matlab (Bouguet) 16Calibration
Input: 30 images of each camera (squares = 28mm) Camera 1: Camera
2:
Slide 17
Camera Calibration Toolbox for Matlab (Bouguet) Output:
intrinsic parameters of each camera Camera 1: Camera 2:
17Calibration
Slide 18
POSIT Description: POSIT is a fast iterative algorithm for
finding the pose (rotation and translation) of an object or scene
with respect to a camera when points of the object are given in
some object coordinate system and these points are visible in the
camera image and recognizable, so that corresponding image points
and object points can be listed in the same order. [rotation,
translation] = Posit(imagePoints, objectPoints, focalLength,
center) 18Calibration
Slide 19
POSIT Input: nbPts : 4+ noncoplanar feature points of the
object imagePoints : matrix of size nbPts x 2 objectPoints : matrix
of size nbPts x 3 focalLength : focal length of the camera in
pixels Center : row vector with the elements of the image center
19Calibration [rotation, translation] = Posit(imagePoints,
objectPoints, focalLength, center) Camera 2
Slide 20
POSIT Input: nbPts : 4+ noncoplanar feature points of the
object imagePoints : matrix of size nbPts x 2 objectPoints : matrix
of size nbPts x 3 focalLength : focal length of the camera in
pixels Center : row vector with the elements of the image center
Output: rotation : 3 x 3 rotation matrix of scene with respect to
camera translation : 3 x 1 translation vector from projection
center of camera to FIRST POINT in list of object points
20Calibration [rotation, translation] = Posit(imagePoints,
objectPoints, focalLength, center)
Slide 21
Output: extrinsic parameters of each camera Camera 1: Camera 2:
21Calibration POSIT
Slide 22
Camera matrix 22Calibration Describes the mapping of a pinhole
camera from 3D points in the world to 2D points in an image.
Intrinsic parametersExtrinsic parameters (Bouguet) (POSIT)
Slide 23
Camera matrix 23Calibration Describes the mapping of a pinhole
camera from 3D points in the world to 2D points in an image. Camera
1: Camera 2:
Slide 24
Goal 24Calibration Camera Calibration Camera matrix
Reconstruction get_mapped.m (WP3)
3D-reconstruction (Triangulation) 26Calibration Epipolar
geometry: When two cameras view a 3D scene from two distinct
positions, there are a number of geometric relations between the 3D
points and their projections onto the 2D images that lead to
constraints between the image points.
Slide 27
3D-reconstruction (Triangulation) 27Calibration Triangulation:
The process of determining a point in 3D space given its
projections onto two, or more, images. In order to solve this
problem it is necessary to know the parameters of the camera
projection function from 3D to 2D for the cameras involved,
represented by the camera matrices
Slide 28
2D-reconstruction (Calibration matrix) 28Calibration 3
dimensions ? Camera 2
Slide 29
2D-reconstruction (Calibration matrix) 29Calibration 2
dimensions ! Camera 2
Slide 30
2D-reconstruction (Calibration matrix) 30Calibration 2
dimensions ! Camera 2
Slide 31
2D-reconstruction (Calibration matrix) 31Calibration 2
dimensions ! Camera 2
Slide 32
2D-reconstruction (Calibration matrix) 32Calibration 2
dimensions ! Function get_mapped.m WP3 function [ x_world, y_world
] = get_mapped(camera_nr, x_pixel, y_pixel)