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2017 | OriginalPaper | Buchkapitel

A Dataset and Evaluation Methodology for Depth Estimation on 4D Light Fields

verfasst von : Katrin Honauer, Ole Johannsen, Daniel Kondermann, Bastian Goldluecke

Erschienen in: Computer Vision – ACCV 2016

Verlag: Springer International Publishing

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Abstract

In computer vision communities such as stereo, optical flow, or visual tracking, commonly accepted and widely used benchmarks have enabled objective comparison and boosted scientific progress.

In the emergent light field community, a comparable benchmark and evaluation methodology is still missing. The performance of newly proposed methods is often demonstrated qualitatively on a handful of images, making quantitative comparison and targeted progress very difficult. To overcome these difficulties, we propose a novel light field benchmark. We provide 24 carefully designed synthetic, densely sampled 4D light fields with highly accurate disparity ground truth. We thoroughly evaluate four state-of-the-art light field algorithms and one multi-view stereo algorithm using existing and novel error measures.

This consolidated state-of-the art may serve as a baseline to stimulate and guide further scientific progress. We publish the benchmark website http://www.lightfield-analysis.net, an evaluation toolkit, and our rendering setup to encourage submissions of both algorithms and further datasets.

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Vorheriges Kapitel Layered Scene Reconstruction from Multiple Light Field Camera Views

Nächstes Kapitel Radial Lens Distortion Correction Using Convolutional Neural Networks Trained with Synthesized Images

Nur mit Berechtigung zugänglich

http://www.votchallenge.net/.

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Titel: A Dataset and Evaluation Methodology for Depth Estimation on 4D Light Fields
verfasst von: Katrin Honauer
Ole Johannsen
Daniel Kondermann
Bastian Goldluecke
Verlag: Springer International Publishing
Buch: Computer Vision – ACCV 2016
Print ISBN: 978-3-319-54186-0

Electronic ISBN: 978-3-319-54187-7

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-54187-7_2

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