Advanced point cloud estimation based on multiple view geometry

In this paper we present a method based on Multiple View Geometry (MVG) for the accurate 3D point clouds estimation. This method identifies features in all the images from the dataset, like edges with gradients in multiple directions, and tries to match these features between all the images and then computing the relative motion. It builds a 3D model with the correlated features. It then creates a 3D point cloud with color information of the scanned object. This designed method provides simple access to the classic problem solvers in Multiple View Geometry (MVG). The experimental results show that the best results using combination of SIFT (Scale-invariant feature transform) descriptor and ANN (Approximate Nearest Neighbor) matcher were obtained (43 matching images and 8942 found corresponding points).