Calculating camera extrincs

Before we talk about the projection matrix of the depth correspondces, we need to know two things:

- Camera extrinsics
- Camera intrinsics

Camera extrinsics maps the world coorinates to the camera coordinates. For the simplicity of the camera, it is a pinhole camera without lenses.  I'll talk about the lenses, the focal length, the lense aberation, the pixel sensor dimension, etc in Camera intrincs.

So, locating an object in two images and projecting in the camera space is not that straight. But, it will be a straight process with the application of Machine Learning.

I'll talk about the next part of the series in applying the deep neural network to optimizing the homographic projection and have it robust in low texture settings including low light.

Deep Neural Network - Estimating Homography

to address:
- low texture environment
- outside light conditions ( gamma > 2kLs)
- robust as or better than SfM or other SLAM techniquese


First, we need to locate the camera and its translation and rotation relative to the object scene. Using the normalized coodinates, so that we can convert it to and from a common frame coordinates.

Using the geomeometric calibration, we will keep an extrinsic matrix converision.