Integral spin images usage in deep learning algorithms for 3D model classification

Abstract We are investigating a problem of 3D model classification using deep learning algorithms. We propose integral spin images usage as 3D model representation. A number of computational experiments were made to build spin images for 3D models of Princeton Shape Benchmark and use them to train L...

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Bibliographic Details
Published inJournal of physics. Conference series Vol. 1902; no. 1; pp. 12114 - 12122
Main Authors Denisenko, A I, Krylovetsky, A A, Chernikov, I S
Format Journal Article
LanguageEnglish
Published Bristol IOP Publishing 01.05.2021
Subjects
Accuracy
Algorithms
Artificial neural networks
Classification
Computer architecture
Deep learning
Image classification
Integrals
Machine learning
Neural networks
Physics
Three dimensional models
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Summary:Abstract We are investigating a problem of 3D model classification using deep learning algorithms. We propose integral spin images usage as 3D model representation. A number of computational experiments were made to build spin images for 3D models of Princeton Shape Benchmark and use them to train LeNet-5, AlexNet and ResNet deep neural networks. The results showed that integral spin images can be used in conjunction with deep learning algorithms in 3D model classification problem. However, with greater number of classes classification accuracy tends to decrease. It is expected that designing a more complex neural network architecture and expanding number of data characteristics can increase accuracy.
ISSN:1742-6588
1742-6596
DOI:10.1088/1742-6596/1902/1/012114