This paper proposes a novel approach for the classification of 3D shapes exploiting deep learning techniques. The proposed algorithm starts by constructing a set of depth maps by rendering the input 3D shape from different viewpoints. Then the depth maps are fed to a multi-branch Convolutional Neural Network. Each branch of the network takes in input one of the depth maps and produces a classification vector by using 5 convolutional layers of progressively reduced resolution. The various classification vectors are finally fed to a linear classifier that combines the outputs of the various branches and produces the final classification. Experimental results on the Princeton ModelNet database show how the proposed approach allows to obtain a high classification accuracy and outperforms several state-of-the-art approaches.
Deep Learning For 3D Shape Classification from Multiple Depth Maps
Pietro Zanuttigh;Ludovico Minto
2017
Abstract
This paper proposes a novel approach for the classification of 3D shapes exploiting deep learning techniques. The proposed algorithm starts by constructing a set of depth maps by rendering the input 3D shape from different viewpoints. Then the depth maps are fed to a multi-branch Convolutional Neural Network. Each branch of the network takes in input one of the depth maps and produces a classification vector by using 5 convolutional layers of progressively reduced resolution. The various classification vectors are finally fed to a linear classifier that combines the outputs of the various branches and produces the final classification. Experimental results on the Princeton ModelNet database show how the proposed approach allows to obtain a high classification accuracy and outperforms several state-of-the-art approaches.Pubblicazioni consigliate
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