Improving 3D reconstruction tracks using denoised euclidean distance matrices

The reconstruction of 3D point cloud models from unordered and uncalibrated sets of images has recently been a hot topic in the computer vision world. Most of the proposed solutions rely on the Structure-From-Motion algorithms, and their performances are significantly affected by the processing order (called track) of the considered images. This is computed according to a distance (or similarity) metric between couples of images, which is usually highly noisy. The paper proposes an image ordering strategy that models the distances between images as an Euclidean distance matrix and applies a rank-based denoising algorithm in order to refine the metric values. Experimental results prove that the accuracy of the final 3D model is sensibly improved.