Building a 3D model with point cloud and color images

In the field of artificial recognition, computer vision and robotics there are usually different means of getting representation of surrounding place. One of a kind and unique source of data is a point cloud representation which gives us 3D description of the surroundings. Use cases of point cloud far surpass robotics as it can be used to reconstruct a 3D model from the real world. With post processing we can obtain accurate models that can be later on printed with a 3D printer. The goal of this thesis has been to improve ICP algorithm with the use of 2D data from colored images, to approximate camera positions. Approximate camera position is the end result of SfM algorithm