Abstract
Recognizing 3D point cloud shapes under isometric deformation is an interesting and challenging issue in the geometry field. This paper proposes a novel feature learning approach by using both the model-based intrinsic descriptor and the deep learning technique. Instead of directly applying deep convolutional neural networks (CNN) on point clouds, we first represent the isometric deformation by using a set of local intrinsic functions to grasp the invariant properties of the shape. Then, an effective point CNN network is developed to learn the parameters and perform semantic feature learning in an end-to-end fashion to link the local and global information together for discriminative shape representation and classification. To reduce the computational costs of our CNN network, some simple operations, like downsampling and fusion, are applied to decrease the number of points and the intrinsic dimensions based on our average heat function. The experimental results on multiple standard benchmarks have demonstrated that our proposed algorithm can achieve very competitive results on both the accuracy rates and the computational efficiency.
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Acknowledgements
This work is partly supported by National Natural Science Foundation of China under Grants 61806063, 61772161, 61622205, 61472110, 61602136 and the Zhejiang Provincial Natural Science Foundation of China under Grant LR15F020002. The authors would like to thank the reviewers and editors for their insightful comments and valuable suggestions which have led to substantial improvements of the paper.
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Kuang, Z., Yu, J., Zhu, S., Li, Z., Fan, J. (2018). Deformable Point Cloud Recognition Using Intrinsic Function and Deep Learning. In: Hong, R., Cheng, WH., Yamasaki, T., Wang, M., Ngo, CW. (eds) Advances in Multimedia Information Processing – PCM 2018. PCM 2018. Lecture Notes in Computer Science(), vol 11165. Springer, Cham. https://doi.org/10.1007/978-3-030-00767-6_9
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