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2016 | OriginalPaper | Chapter

Creating Feasible Reflectance Data for Synthetic Optical Flow Datasets

Authors : Burkhard Güssefeld, Katrin Honauer, Daniel Kondermann

Published in: Advances in Visual Computing

Publisher: Springer International Publishing

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Abstract

Optical flow ground truth generated by computer graphics has many advantages. For example, we can systematically vary scene parameters to understand algorithm sensitivities. But is synthetic ground truth realistic enough? Appropriate material models have been established as one of the major challenges for the creation of synthetic datasets: previous research has shown that highly sophisticated reflectance field acquisition methods yield results, which various optical flow methods cannot distinguish from real scenes. However, such methods are costly both in acquisition and rendering time and thus infeasible for large datasets. In this paper we find the simplest reflectance models (RM) for different groups of materials which still provide sufficient accuracy for optical flow performance analysis. It turns out that a spatially varying Phong RM is sufficient for simple materials. Normal estimation combined with Anisotropic RM can handle even very complex materials.

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Metadata
Title
Creating Feasible Reflectance Data for Synthetic Optical Flow Datasets
Authors
Burkhard Güssefeld
Katrin Honauer
Daniel Kondermann
Copyright Year
2016
Book
Advances in Visual Computing

Print ISBN: 978-3-319-50834-4

Electronic ISBN: 978-3-319-50835-1

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-50835-1_8

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