Hide/Show Apps

Robust extraction of sparse 3d points from image sequences

Vural, Elif
In this thesis, the extraction of sparse 3D points from calibrated image sequences is studied. The presented method for sparse 3D reconstruction is examined in two steps, where the first part addresses the problem of two-view reconstruction, and the second part is the extension of the two-view reconstruction algorithm to include multiple views. The examined two-view reconstruction method consists of some basic building blocks, such as feature detection and matching, epipolar geometry estimation, and the reconstruction of cameras and scene structure. Feature detection and matching is achieved by Scale Invariant Feature Transform (SIFT) method. For the estimation of epipolar geometry, the 7-point and 8-point algorithms are examined for Fundamental matrix (F-matrix) computation, while RANSAC and PROSAC are utilized for the robustness and accuracy for model estimation. In the final stage of two-view reconstruction, the camera projection matrices are computed from the F-matrix, and the locations of 3D scene points are estimated by triangulation; hence, determining the scene structure and cameras up to a projective transformation. The extension of the two-view reconstruction to multiple views is achieved by estimating the camera projection matrix of each additional view from the already reconstructed matches, and then adding new points to the scene structure by triangulating the unreconstructed matches. Finally, the reconstruction is upgraded from projective to metric by a rectifying homography computed from the camera calibration information. In order to obtain a refined reconstruction, two different methods are suggested for the removal of erroneous points from the scene structure. In addition to the examination of the solution to the reconstruction problem, experiments have been conducted that compare the performances of competing algorithms used in various stages of reconstruction. In connection with sparse reconstruction, a rate-distortion efficient piecewise planar scene representation algorithm that generates mesh models of scenes from reconstructed point clouds is examined, and its performance is evaluated through experiments.