Inverse tensor transfer for novel view synthesis

Abstract

This paper provides a new transfer based novel view synthesis method. This method does not need a pre-computed dense depth map, therefore overcomes most common problems associated with conventional dense correspondence algorithms, yet still produce very photo-realistic novel images. The power of the method comes from the introducing and using of a novel inverse tensor transfer technique, which offers a simple mechanism to exploit both photometric constraints and geometric constraints across multiple input images. Our method works equally well for both calibrated images and un-calibrated images. Experiments on real sequences show promising results.