Geometric Camera Models

TermDefinitionNotes
Backward Camera ModelA Geometric Camera Model that transforms 2D image points into 3D rays. 
Camera-Relative World PointA World Point in the reference frame of the camera. 
Central Camera ModelA Geometric Camera Model for which all rays go through a single point. 
ExtrinsicsA portion of a Geometric Camera Model that describes the position and orientation of an object within a world coordinate system.See also: Pose.
Forward Camera ModelA Geometric Camera Model that transforms 3D world points into 2D image points. 
Geometric Camera ModelA model that describes the geometric properties of a camera.This is sometimes referred to as a "geometric calibration model". A Geometric Camera Model is composed of Extrinsics, Intrinsics, and Distortion.
Global World PointA World Point in the global reference frame. 
Image Coordinate SystemThe basis for describing image points.See Image Coordinate System documentation.
Image PointAn ordered pair (x, y) describing a 2D location on the focal plane.An Image Point is defined in an Image Coordinate System.
IntrinsicsA portion of a Geometric Camera Model that describes the internal geometric properties of the camera. 
PoseThe position and orientation of an object relative to some world coordinate system.See also: Extrinsics.
Principal PointThe point on the focal plane that intersects the optical axis. 
World PointAn ordered triple (X, Y, Z) describing a 3D location in a world coordinate system. 

Notation

All vectors are column vectors. Uppercase , , and refer to 3D world points. Lowercase Uppercase and refer to 2D image points.

Homogeneous Coordinates

Homogeneous coordinates are a set of coordinates with useful properties for perspective geometry [1, 2]:

A "standard" coordinate is referred to as inhomogeneous. 

Properties 

Transforms

Inhomogeneous to Homogeneous

The simplest way to convert from an inhomogeneous coordinate to a homogeneous one is to append a 1 to the end of the coordinate. 

The general conversion is to append 1 and multiply by any non-zero real number.

Homogeneous to Inhomogeneous

To convert from a homogeneous coordinate to an inhomogeneous one, divide all of the components by the last one, which is discarded.

Intrinsics

The intrinsic matrix, , is an upper-triangular matrix that transforms a world coordinate relative to the camera into a homogeneous image coordinate. There are two general and equivalent forms of the intrinsic matrix:

where

VariableDescription
The x-focal length
The y-focal length
The focal length ratios 
The skew
The principal point (intersection of the optical axis with the focal plane)

The intrinsic matrix of the th camera is applied to the th camera-relative 3D point to produce a homogeneous image point.

Notes:

Camera Models

Summary

ModelForward OrderMaximum Field of ViewDistortion OptionsCentralityReferences
Simple PinholeExtrinsics → Intrinsics<180°NoneCentral[1, 2]
OpenCVExtrinsics → Distortion → Intrinsics<180°Radial, Tangential, Thin Prism, TiltCentral[3]

Simple Pinhole

The forward direction of the simple pinhole model is: Extrinsics → Intrinsics.

The forward direction of the OpenCV model is given by:

  1. Transform a camera-relative world point through the intrinsics camera matrix:
  2. Convert to an inhomogeneous image point

OpenCV

The OpenCV camera model [3] is defined by the OpenCV library. The version used in Imatest is that of OpenCV 4.12.0.

The forward direction of the OpenCV model is: Extrinsics → Distortion → Intrinsics.

The forward direction of the OpenCV model is given by:

  1. Transform a camera-relative world point into an undistorted image point :
  2. Compute the radius, :
  3. Apply the radial distortion:
  4. Apply the tangential distortion:
  5. Apply the thin-prism distortion:
  6. Apply the tilt-distortion (and convert back to an inhomogeneous image coordinate):
  7. Apply the intrinsics:

References

[1] Hartley, R., & Zisserman, A. (2003). Multiple view geometry in computer vision. Cambridge University Press.

[2] Ma, Y., Soatto, S., Košecká, J., & Sastry, S. (2004). An invitation to 3-d vision: from images to geometric models (Vol. 26). New York: Springer.

[3] OpenCV. Camera Calibration and 3D Reconstruction. https://docs.opencv.org/4.12.0/d9/d0c/group__calib3d.html [Accessed 2025-08-18]