SKen: A Statistical Test for Removing Outliers in Optical Flow A 3D Reconstruction Case

摘要

The 3D reconstruction can be employed in several areas such as markerless augmented reality, manipulation of interactive virtual objects and to deal with the occlusion of virtual objects by real ones. However, many improvements into the 3D reconstruction pipeline in order to increase its efficiency may still be done. In such context, this paper proposes a filter for optimizing a 3D reconstruction pipeline. It is presented the SKen technique, a statistical hypothesis test that classifies the features by checking the smoothness of its trajectory. Although it was not mathematically proven that inliers features performed smooth camera paths, this work shows some evidence of a relationship between smoothness and inliers. By removing features that did not present smooth paths, the quality of the 3D reconstruction was enhanced.