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Quick answers:

• For 1), you should look at the equation, a 3D point in the world coordinate is projected into the image plane using the extrinsic and intrinsic matrices
• For 2), you should look for a course on this topic (homogeneous transformation), maybe this or this
• For 3), you use it everytime when you capture the world in 3D dimension into a 2D image

Depends on how many frames you have. If you have one object, you can define the same frame for the object / world frame. If you have multiples objects, you can define an object frame for each object and a reference world frame somewhere. The extrinsic matrix relates the pose of a frame with respect to the camara frame.

• For 4), look at the equation: (u, v, 1)^T = K . [R | t] . (X, Y, Z, 1). Which frame is multiplied by the extrinsic matrix?

If you want to experiment, print a chessboard pattern and calibrate your camera. You will have the intrinsic and extrinsic matrices. Look into the OpenCV sample code:

• here is constructed the list of the 3D points for the chessboard
• if you multiply one 3D point in the object frame with the corresponding extrinsic matrix, you will have the 3D coordinate in the camera frame. Also if you look at t_x, t_y, t_z in the extrinsic matrix, you will have the translation between the camera frame and the object frame.
• For each image, you will a different extrinsic matrix.

I have found the following courses (among others):

• Position & motion in 2D and 3D, Robot Vision by Peter Corke
• Projective Geometry and Camera Models by Derek Hoiem
• Computer Vision: Algorithms and Applications by Richard Szeliski for a general book on the computer vision field
• etc.

Quick answers:

• For 1), you should look at the equation, a 3D point in the world coordinate is projected into the image plane using the extrinsic and intrinsic matrices
• For 2), you should look for a course on this topic (homogeneous transformation), maybe this or this
• For 3), you use it everytime when you capture the world in 3D dimension 3 dimensions into a 2D image

Depends on how many frames you have. If you have one object, you can define the same frame for the object / world frame. If you have multiples objects, you can define an object frame for each object and a reference world frame somewhere. The extrinsic matrix relates the pose of a frame with respect to the camara frame. In fact, the extrinsic matrix is just the name given of the homogeneous transformation that transforms one frame to the camera frame.

• For 4), look at the equation: (u, v, 1)^T = K . [R | t] . (X, Y, Z, 1). 1)^T. Which frame is multiplied by the extrinsic matrix?

If you want to experiment, print a chessboard pattern and calibrate your camera. You will have the intrinsic and extrinsic matrices. Look into the OpenCV sample code:

• here is constructed the list of the 3D points for the chessboard
• if you multiply one 3D point in the object frame with the corresponding extrinsic matrix, you will have the 3D coordinate in the camera frame. Also if you look at t_x, t_y, t_z in the extrinsic matrix, you will have the translation between the camera frame and the object frame.
• For each image, you will a different extrinsic matrix.

I have found the following courses (among others):

• Position & motion in 2D and 3D, Robot Vision by Peter Corke
• Projective Geometry and Camera Models by Derek Hoiem
• Computer Vision: Algorithms and Applications by Richard Szeliski for a general book on the computer vision field
• etc.