A novel particle filtering framework for 2D-TO-3D conversion from a monoscopic 2D image sequence

Abstract

This paper presents a novel 2D-TO-3D conversion approach from a monoscopic 2D image sequence. We propose a particle filter framework for recursive recovery of point-wise depth from feature correspondences matched through image sequences. We formulate a novel 2D dynamics model for recursive depth estimation with the combination of camera model, structure model and translation model. The proposed method utilizes edge-detection-assisted scale-invariant features to avoid lack of edge features in scale-invariant features (SIFT). Furthermore, the depths in the depth map are computed and interpolated using 2D Delaunay triangulation. Finally, a stereo-view generation algorithm is presented for multiple users that uses proposed dynamics model and particle filter framework. Experimental results show that our proposed framework yields superior results.