Abstract
Point cloud has become increasingly prevalent in 3D computer vision tasks owing to its simplicity, flexibility and powerful representation capability. However, for 3D shapes classification, how to obtain discriminative and effective feature descriptors from point clouds is still a challenging task. In this paper, we implement a point convolutional neural network combining multi-geometric feature to classify 3D shapes. We firstly obtain hidden geometric information from raw input point. And then a set of point information that consists of three-dimensional coordinates, normal vectors and curvatures is input to network. The extracted feature combining local geometric information can represent 3D shapes more effectively. Experiments on the Modelnet40 dataset show the effectiveness of the proposed method.
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Acknowledgments
This work is partially supported by National Natural Science Foundation of China (61877002, 61702020), Beijing Natural Science Foundation grant number (4172013), Beijing Natural Science Foundation-Haidian Primitive Innovation Joint Fund grant number (L182007) and Beijing Municipal Commission of Education PXM2019-014213-000007.
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Zeng, G., Wu, Y., Li, H., Tan, L. (2020). 3D Shape Classification Based on Point Convolutional Neural Network Combining Multi-geometric Features. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2019 Chinese Intelligent Systems Conference. CISC 2019. Lecture Notes in Electrical Engineering, vol 594. Springer, Singapore. https://doi.org/10.1007/978-981-32-9698-5_49
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DOI: https://doi.org/10.1007/978-981-32-9698-5_49
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