Non-rigid 3D shape classification based on convolutional neural networks

Jan Franco Llerena Quenaya; Cristian José Lopez Del Alamo

doi:10.1109/LA-CCI.2017.8285693

Non-rigid 3D shape classification based on convolutional neural networks

Jan Franco Llerena Quenaya, Cristian José Lopez Del Alamo

Resultado de la investigación: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

Resumen

Over the years, the scientific interest towards 3D models analysis has become more popular. Problems such as classification, retrieval and matching are studied with the idea to offer robust solutions. This paper introduces a 3D object classification method for non-rigid shapes, based on the detection of key points, the use of spectral descriptors and deep learning techniques. We adopt an approach of converting the models into a "spectral image". By extracting interest points and calculating three types of spectral descriptors (HKS, WKS and GISIF), we generate a three-channel input to a convolutional neural network. This CNN is trained to automatically learn features such as topology of 3D models. The results are evaluated and analyzed using the Non-Rigid Classification Benchmark SHREC 2011. Our proposal shows promising results in classification tasks compared to other methods, and also it is robust under several types of transformations.

Idioma original	Inglés
Título de la publicación alojada	2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings
Editorial	Institute of Electrical and Electronics Engineers Inc.
Páginas	1-6
Número de páginas	6
ISBN (versión digital)	9781538637340
DOI	https://doi.org/10.1109/LA-CCI.2017.8285693
Estado	Publicada - 7 feb. 2018
Publicado de forma externa	Sí
Evento	2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Arequipa, Perú Duración: 8 nov. 2017 → 10 nov. 2017

Serie de la publicación

Nombre	2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings
Volumen	2017-November

Conferencia

Conferencia	2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017
País/Territorio	Perú
Ciudad	Arequipa
Período	8/11/17 → 10/11/17

Nota bibliográfica

Publisher Copyright:
© 2017 IEEE.

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10.1109/LA-CCI.2017.8285693

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Quenaya, J. F. L., & Lopez Del Alamo, C. J. (2018). Non-rigid 3D shape classification based on convolutional neural networks. En 2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings (pp. 1-6). (2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings; Vol. 2017-November). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/LA-CCI.2017.8285693

Quenaya, Jan Franco Llerena ; Lopez Del Alamo, Cristian José. / Non-rigid 3D shape classification based on convolutional neural networks. 2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 1-6 (2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings).

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Quenaya, JFL & Lopez Del Alamo, CJ 2018, Non-rigid 3D shape classification based on convolutional neural networks. En 2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings. 2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings, vol. 2017-November, Institute of Electrical and Electronics Engineers Inc., pp. 1-6, 2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017, Arequipa, Perú, 8/11/17. https://doi.org/10.1109/LA-CCI.2017.8285693

Non-rigid 3D shape classification based on convolutional neural networks. / Quenaya, Jan Franco Llerena; Lopez Del Alamo, Cristian José.

2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc., 2018. p. 1-6 (2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings; Vol. 2017-November).

Resultado de la investigación: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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AB - Over the years, the scientific interest towards 3D models analysis has become more popular. Problems such as classification, retrieval and matching are studied with the idea to offer robust solutions. This paper introduces a 3D object classification method for non-rigid shapes, based on the detection of key points, the use of spectral descriptors and deep learning techniques. We adopt an approach of converting the models into a "spectral image". By extracting interest points and calculating three types of spectral descriptors (HKS, WKS and GISIF), we generate a three-channel input to a convolutional neural network. This CNN is trained to automatically learn features such as topology of 3D models. The results are evaluated and analyzed using the Non-Rigid Classification Benchmark SHREC 2011. Our proposal shows promising results in classification tasks compared to other methods, and also it is robust under several types of transformations.

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Quenaya JFL, Lopez Del Alamo CJ. Non-rigid 3D shape classification based on convolutional neural networks. En 2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings. Institute of Electrical and Electronics Engineers Inc. 2018. p. 1-6. (2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings). doi: 10.1109/LA-CCI.2017.8285693

Non-rigid 3D shape classification based on convolutional neural networks

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