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Ornament Analysis with the Help of Screened Poisson Shape Fields Read chapter Read first chapter

2016 | OriginalPaper | Chapter

11. Applying Random Forests to the Problem of Dense Non-rigid Shape Correspondence

Authors : Matthias Vestner, Emanuele Rodolà, Thomas Windheuser, Samuel Rota Bulò, Daniel Cremers

Published in: Perspectives in Shape Analysis

Publisher: Springer International Publishing

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Abstract

We introduce a novel dense shape matching method for deformable, three-dimensional shapes. Differently from most existing techniques, our approach is general in that it allows the shapes to undergo deformations that are far from being isometric. We do this in a supervised learning framework which makes use of training data as represented by a small set of example shapes. From this set, we learn an implicit representation of a shape descriptor capturing the variability of the deformations in the given class. The learning paradigm we choose for this task is a random forest classifier. With the additional help of a spatial regularizer, the proposed method achieves significant improvements over the baseline approach and obtains state-of-the-art results while keeping a low computational cost.

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previous chapter Sparse Models for Intrinsic Shape Correspondence
next chapter Accelerating Deformable Part Models with Branch-and-Bound
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Metadata
Title
Applying Random Forests to the Problem of Dense Non-rigid Shape Correspondence
Authors
Matthias Vestner
Emanuele Rodolà
Thomas Windheuser
Samuel Rota Bulò
Daniel Cremers
Copyright Year
2016
Book
Perspectives in Shape Analysis

Print ISBN: 978-3-319-24724-3

Electronic ISBN: 978-3-319-24726-7

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-24726-7_11

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