Estimating a 3D representation of a real-world object using partially overlapping images is the main goal of a photogrammetry technique known as Structure from Motion.
Whereas the structure from motion pipeline has been well established, the algorithms in practice vary significantly in the implementation of the different stages. In search of effective and reliable techniques we have described several algorithms for feature detection, feature description, feature matching, pose estimation, point cloud extraction and 3D reconstruction. Each algorithm has been evaluated in terms of efficiency, speed and robustness.
Methods presented in this thesis are tested on a dataset consisting of reconstructed 3D models and consecutive images of objects.
For each model similarity between the reconstructed mesh and the original 3D model has been measured using the MeshLab toolbox. In summary the best performing methods in the reconstruction pipeline proved to be: SURF for feature detection, Fast approximate nearest-neighbour search for feature matching, PROSAC for fundamental matrix estimation, Incremental Structure from Motion for camera pose estimation and Dense stero matching for point cloud reconstruction.
|
