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.

Related Material

author = {Sabater, Neus and Boisson, Guillaume and Vandame, Benoit and Kerbiriou, Paul and Babon, Frederic and Hog, Matthieu and Gendrot, Remy and Langlois, Tristan and Bureller, Olivier and Schubert, Arno and Allie, Valerie},
title = {Dataset and Pipeline for Multi-View Light-Field Video},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
month = {July},
year = {2017}