Abstract
With the development of science and technology, the requirements for 3D point cloud classification are increasing. Methods that can directly process point cloud has the advantages of small calculation amount and high real-time performance. Hence, we proposed a novel convolutional neural network(CNN) method to directly extract features from point cloud for 3D object classification. We firstly train a pre-training model with ModelNet40 dataset. Then, we freeze the first five layers of our CNN model and adjust the learning rate to fine tune our CNN model. Finally, we evaluate our methods by ModelNet40 and the classification accuracy of our model can achieve 87.8% which is better than other traditional approaches. We also design some experiments to research the effect of T-Net proposed by Charles R. Qi et al. on 3D object classification. In the end, we find that T-Net has little effect on classification task and it is not necessary to apply in our CNN.
This work was supported partially by National Natural Science Foundation of China (Grant No. 61801144, 61971156), Shandong Provincial Natural Science Foundation, China (Grant No. ZR2019QF003, ZR2019MF035), and the Fundamental Research Funds for the Central Universities, China (Grant No. HIT.NSRIF.2019081).
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Lu, J., Kang, W., Ma, R., Qin, Z. (2022). 3D Point Cloud Classification Based on Convolutional Neural Network. In: Shi, S., Ma, R., Lu, W. (eds) 6GN for Future Wireless Networks. 6GN 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 439. Springer, Cham. https://doi.org/10.1007/978-3-031-04245-4_29
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