Fast View Synthesis with Deep Stereo Vision

Tewodros Habtegebrial, Kiran Varanasi, Christian Bailer, Didier Stricker

2019

Abstract

Novel view synthesis is an important problem in computer vision and graphics. Over the years a large number of solutions have been put forward to solve the problem. However, the large-baseline novel view synthesis problem is far from being ”solved”. Recent works have attempted to use Convolutional Neural Networks (CNNs) to solve view synthesis tasks. Due to the difficulty of learning scene geometry and interpreting camera motion, CNNs are often unable to generate realistic novel views. In this paper, we present a novel view synthesis approach based on stereo-vision and CNNs that decomposes the problem into two sub-tasks: view dependent geometry estimation and texture inpainting. Both tasks are structured prediction problems that could be effectively learned with CNNs. Experiments on the KITTI Odometry dataset show that our approach is more accurate and significantly faster than the current state-of-the-art.

Paper Citation

in Harvard Style

Habtegebrial T., Varanasi K., Bailer C. and Stricker D. (2019). Fast View Synthesis with Deep Stereo Vision. In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP; ISBN 978-989-758-354-4, SciTePress, pages 792-799. DOI: 10.5220/0007360107920799

in Bibtex Style

@conference{visapp19,
author={Tewodros Habtegebrial and Kiran Varanasi and Christian Bailer and Didier Stricker},
title={Fast View Synthesis with Deep Stereo Vision},
booktitle={Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP},
year={2019},
pages={792-799},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007360107920799},
isbn={978-989-758-354-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019) - Volume 5: VISAPP
TI - Fast View Synthesis with Deep Stereo Vision
SN - 978-989-758-354-4
AU - Habtegebrial T.
AU - Varanasi K.
AU - Bailer C.
AU - Stricker D.
PY - 2019
SP - 792
EP - 799
DO - 10.5220/0007360107920799
PB - SciTePress