基于深度学习的三维形状特征提取方法

doi:10.11896/j.issn.1002-137X.2019.09.006

Abstract

Abstract: Research on extracting 3D shape features with low dimension and high discriminating ability can solve the problem such as classification,retrieval of 3D shape data.With the continuous development of deep learning,3D shape feature extraction method combineds with deep learning has become a research hotspot.Combining deep learning with traditional 3D shape feature extraction methods can not only break through the bottleneck of non-deep learningme-thods,but also improve the accuracy of 3D shape data classification,retrieval and other tasks,especially when 3D shape is non-rigid body.However,deep learning is still developing,and there are still problems that require a large number of training samples.Therefore,how to effectively extract 3D shape features by using deep learning methods has become the research focus and difficulty in the field of computer vision.At present,most researchers focus on improving the ability of neural network to extract features by improving network structure,training methods and other aspects.First,the re-levant deep learning model are introduced,and there are some new ideas about the network improvement and training methods.Second,the feature extraction methods of rigid body and non-rigid body based on deep learning are comprehensively expounded which combined with the development of deep learning and 3D shape feature extraction methods,and the current deep learning methods for the 3D shape feature extraction are described.And then,the current situation of the existing 3D shape retrieval system and the similarity calculation method are described.Finally,the current problems of 3D shape feature extraction methods are introduced,and its future development trend are explored.

Key words: 3D shape, Deep learning, Feature extraction, Neural networks

CLC Number:

TP391.4

ZHOU Yan, ZENG Fan-zhi, WU Chen, LUO Yue, LIU Zi-qin. 3D Shape Feature Extraction Method Based on Deep Learning[J].Computer Science, 2019, 46(9): 47-58.

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