TY - GEN
T1 - Non-rigid 3D shape classification based on convolutional neural networks
AU - Quenaya, Jan Franco Llerena
AU - López Del Alamo, Cristian Jose
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/7/2
Y1 - 2017/7/2
N2 - 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.
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.
UR - http://www.scopus.com/inward/record.url?scp=85050367614&partnerID=8YFLogxK
U2 - 10.1109/LA-CCI.2017.8285693
DO - 10.1109/LA-CCI.2017.8285693
M3 - Conference contribution
AN - SCOPUS:85050367614
T3 - 2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings
SP - 1
EP - 6
BT - 2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 IEEE Latin American Conference on Computational Intelligence, LA-CCI 2017
Y2 - 8 November 2017 through 10 November 2017
ER -