Point cloud classification9/18/2023 ![]() In: 2014 IEEE Conference on Computer Vision and Pattern Recognition, pp. Girshick, R., Donahue, J., Darrell, T., Malik, J.: Rich feature hierarchies for accurate object detection and semantic segmentation. In the end, we find that T-Net has little effect on classification task and it is not necessary to apply in our CNN. We also design some experiments to research the effect of T-Net proposed by Charles R. 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. Then, we freeze the first five layers of our CNN model and adjust the learning rate to fine tune our CNN model. We firstly train a pre-training model with ModelNet40 dataset. Hence, we proposed a novel convolutional neural network(CNN) method to directly extract features from point cloud for 3D object classification. Methods that can directly process point cloud has the advantages of small calculation amount and high real-time performance. ![]() With the development of science and technology, the requirements for 3D point cloud classification are increasing. ![]()
0 Comments
Leave a Reply.AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |