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Erschienen in:
Back to Basics: Unsupervised Learning of Optical Flow via Brightness Constancy and Motion Smoothness Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

How Useful Is Photo-Realistic Rendering for Visual Learning?

verfasst von : Yair Movshovitz-Attias, Takeo Kanade, Yaser Sheikh

Erschienen in: Computer Vision – ECCV 2016 Workshops

Verlag: Springer International Publishing

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Abstract

Data seems cheap to get, and in many ways it is, but the process of creating a high quality labeled dataset from a mass of data is time-consuming and expensive.
With the advent of rich 3D repositories, photo-realistic rendering systems offer the opportunity to provide nearly limitless data. Yet, their primary value for visual learning may be the quality of the data they can provide rather than the quantity. Rendering engines offer the promise of perfect labels in addition to the data: what the precise camera pose is; what the precise lighting location, temperature, and distribution is; what the geometry of the object is.
In this work we focus on semi-automating dataset creation through use of synthetic data and apply this method to an important task – object viewpoint estimation. Using state-of-the-art rendering software we generate a large labeled dataset of cars rendered densely in viewpoint space. We investigate the effect of rendering parameters on estimation performance and show realism is important. We show that generalizing from synthetic data is not harder than the domain adaptation required between two real-image datasets and that combining synthetic images with a small amount of real data improves estimation accuracy.

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Vorheriges Kapitel Deep Kinematic Pose Regression
Nächstes Kapitel Learning the Structure of Objects from Web Supervision
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Metadaten
Titel
How Useful Is Photo-Realistic Rendering for Visual Learning?
verfasst von
Yair Movshovitz-Attias
Takeo Kanade
Yaser Sheikh
Copyright-Jahr
2016
Buch
Computer Vision – ECCV 2016 Workshops

Print ISBN: 978-3-319-49408-1

Electronic ISBN: 978-3-319-49409-8

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-49409-8_18

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