Geometric Deep Learning for Shape Analysis
| dc.contributor.author | Boscaini, Davide | |
| dc.date.accessioned | 2017-12-15T17:55:08Z | |
| dc.date.available | 2017-12-15T17:55:08Z | |
| dc.date.issued | 2017-08-16 | |
| dc.description.abstract | The past decade in computer vision research has witnessed the re-emergence of artificial neural networks (ANN), and in particular convolutional neural net- work (CNN) techniques, allowing to learn powerful feature representations from large collections of data. Nowadays these techniques are better known under the umbrella term deep learning and have achieved a breakthrough in perfor- mance in a wide range of image analysis applications such as image classification, segmentation, and annotation. Nevertheless, when attempting to apply deep learning paradigms to 3D shapes one has to face fundamental differences between images and geometric objects. The main difference between images and 3D shapes is the non-Euclidean nature of the latter. This implies that basic operations, such as linear combination or convolution, that are taken for granted in the Euclidean case, are not even well defined on non-Euclidean domains. This happens to be the major obstacle that so far has precluded the successful application of deep learning methods on non-Euclidean geometric data. The goal of this thesis is to overcome this obstacle by extending deep learning tecniques (including, but not limiting to CNNs) to non-Euclidean domains. We present different approaches providing such extension and test their effectiveness in the context of shape similarity and correspondence applications. The proposed approaches are evaluated on several challenging experiments, achieving state-of- the-art results significantly outperforming other methods. To the best of our knowledge, this thesis presents different original contributions. First, this work pioneers the generalization of CNNs to discrete manifolds. Second, it provides an alternative formulation of the spectral convolution operation in terms of the windowed Fourier transform to overcome the drawbacks of the Fourier one. Third, it introduces a spatial domain formulation of convolution operation using patch operators and several ways of their construction (geodesic, anisotropic diffusion, mixture of Gaussians). Fourth, at the moment of publication the proposed approaches achieved state-of-the-art results in different computer graphics and vision applications such as shape descriptors and correspondence. | en_US |
| dc.identifier.uri | https://diglib.eg.org:443/handle/10.2312/2631996 | |
| dc.language.iso | en | en_US |
| dc.publisher | Università della Svizzera italiana | en_US |
| dc.subject | Geometric Deep Learning | en_US |
| dc.subject | Shape Analysis | en_US |
| dc.subject | Local shape descriptors | en_US |
| dc.subject | Shape correspondence | en_US |
| dc.subject | Shape retrieval | en_US |
| dc.title | Geometric Deep Learning for Shape Analysis | en_US |
| dc.type | Thesis | en_US |
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