Dataset and Pipeline for Multi-View Light-Field Video

Neus Sabater, Guillaume Boisson, Benoit Vandame, Paul Kerbiriou, Frederic Babon, Matthieu Hog, Remy Gendrot, Tristan Langlois, Olivier Bureller, Arno Schubert, Valerie Allie; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2017, pp. 30-40

The quantity and diversity of data in Light-Field videos makes this content valuable for many applications such as mixed and augmented reality or post-production in the movie industry. Some of such applications require a large parallax between the different views of the Light-Field, making the multi-view capture a better option than plenoptic cameras. In this paper we propose a dataset and a complete pipeline for Light-Field video. The proposed algorithms are specially tailored to process sparse and wide-baseline multi-view videos captured with a camera rig. Our pipeline includes algorithms such as geometric calibration, color homogenization, view pseudo-rectification and depth estimation. Such elemental algorithms are well known by the state-of-the-art but they must achieve high accuracy to guarantee the success of other algorithms using our data. Along this paper, we publish our Light-Field video dataset that we believe may be of special interest for the community. We provide the original sequences, the calibration parameters and the pseudo-rectified views. Finally, we propose a depth-based rendering algorithm for Dynamic Perspective Rendering.