3D Deep Learning for 3D Printing of Tooth Model Recently, with the development of both 3D sensors and 3D virtual network that bring the needs of interaction with the real world, many 3D applications burst out. However, it is difficult to understanding these threedimensional scenes with a fixed program. Then, a datadriven method is required to process these 3D data, which brings a strong demand of 3D Deep Learning in 3D data. Towards this goal, with an endtoend deep learning, the experiment is based on PointNet, a well proposed method for feature extraction. The experiment optimizes the network structure and parameters to improve the classification results. Finally, the network is applied to tooth model for classification and identification so that the dental model can be found from different perspectives.

EPN: EdgeAware PointNet for Object Recognition from MultiView 2.5D Point Clouds Performance of current 3D point based detectors is limited by the number of points they can process, consequently limiting their accuracy. In this paper we propose a novel architecture coined as EdgeAware PointNet, that incorporates geometric shape priors as binary maps, integrated in parallel with the PointNet framework, through convolutional neural networks (CNNs). The proposed architecture takes individual object instances as input and learns the task of object recognition for 3D shapes. To train the network, we present a dataset of 31k 2.5D synthetic point clouds rendered from ModelNet40. Through 2.5D representation, the network learns object recognition despite occlusion that enables improved performance on objects from real world, while 2D binary maps enable feature learning that is independent of number of points in the point cloud. Comprehensive experimentation shows that the proposed network is able to improve performance by 2.5% on ModelNet40 and 2.6% on ModelNet10 datasets, as compared to the baseline PointNet. We also show improved performance as compared to stateoftheart methods, on a real world RGBD dataset where our network improves results by 8%. Our code and dataset is publicly available at github.comMerium88EdgeAwarePointNet.