This project has been written in Python and its aim is to reconstruct 3d maps of an enviroment starting from pairs of 2d stereo images.

The code is able to perform camera calibration for radial and tangential distortion (by capturing images of a checkerboard or by using a stored set of chessboard images), stereo rectification and image capture. Once a pair of stero images has been undistorted, rectified and pre-processed using a set of operations (sharpening filters, histogram equalization, Brightness Control, Gaussian Pyramiding), a disparity map is calculated using our variant of the SAD (Sum of Absolute Differences) algorithm. From there, a depth map is retrieved using some triangulations in order to correctly locate in the 3d space each pixel of the map (respecting the correct real-world size and distances of the objects). Eventually, a .ply coloured point cloud is generated, which can be interpolated to obtain a uniform 3d model of the environment.

Here is shown more in detail the entire operative pipeline of the program:

Detailed documentation about the whole pipeline of the program can be found in this Paper (pdf format): https://github.com/ArtyZiff35/3D_Reconstruction_From_Stereo_Images/blob/master/documentation/3D_Model_Reconstruction_from_Stereo_2D_Images.pdf

Also, a set of brief presentation slides can be found here: https://docs.google.com/presentation/d/1Q8aFab3yFyF7yvGsJnriGsj5vfLVJpKg9J2PLjyWOMM/edit?usp=sharing

The following is an example result of the calibration and rectification process. Before: After:

Next step is the disparity map generation using our custom algorithm derived from an optimized version of SAD:

Eventually, after some other processing, we end up with a depth map, and its corresponding 3d representation: