Computer Vision News - October 2019

We Tried for You 20 standard deviations.Then the featuresare filtered toachieve robustness against illumination change, rotations and noise. Finally, descriptors across images are compared to find point correspondences across frames. When the matching is done, VSFM stores the SIFT key points as well as the raw matches in the directory of the images. This looks like this: In general, we can parse the SIFT file and use it in any programming language. For our purpose, we don't need to, since we will perform 3D reconstruction directly. This is done by pressing SfM-> Reconstruct Sparse. Sparse stands for the fact that we reconstruct only points that we have matched in the previous step. In the background, VSfM runs incremental structure from motion pipeline to recover the pose of each camera position. Then, given the set of camera matrices, each two (or more) points are triangulated and a 3D point is generated. At each addition of a camera, a bundle adjustment is performed to refine the solution. To visualize the results, the software plots it in a 3D graph. Generating 3D Reconstruction "It does not require programming knowledge, though it enables the user to export the model for further research, using any desired programming language".