Algorithm 1. The overall steps: image to point cloud generation.
Input: Image pair input
Output: The 3D point cloud of the environment
1:	Initialize the encoder and decoder model
2:	Initialize the proper model and input size
3:	Initialize the calibration parameter, such as intrinsic and projection matrix.
4:	while image frames are available, do
5:	Read image pairs
6:	Convert to torch tensor
7:	Concatenate the image pairs
8:	Extract the features using the encoder network
9:	Depth output using decoder network
10:	Functional interpolation of result if the size is different
11:	Squeeze the output to the array
12:	Project the disparity to points
13:	Convert to point field for visualization
14:	end