# EDC-Net: Edge Detection Capsule Network for 3D Point Clouds
## 1 Introduction
In our proposed capsule network for the edge detection, instead of applying the primary capsule activation directly to the routing process, we apply an attention module to the primary capsules and concatenate them with the features of previous convolution layers in order to combine the local and global features for a point-wise segmentation process. Another challenging issue of training a CNN-based model for edge detection from point clouds is the lack of annotated data. In this paper, we address this problem to train edge detection models for 3D point clouds with a weakly-supervised transfer learning approach. Weakly-supervised learning is an approach of using noisy or imprecise sources as labels for large amounts of training data. Afterwards, these imprecise labels are fed into a supervised learning approach. Various methods have been proposed to extract such imperfect labels purposefully from the training data [30]. Furthermore, transfer learning has contributed to the wide range of applications [31]. It allows exploiting the knowledge from high-quality pre-trained networks when there is relatively little labeled data for training networks. A combination of weakly-supervised learning and transfer learning was proposed in [32; 33]. Given the success of these networks in different applications, it might be surprising that this approach has not been applied to edge detection problems of point clouds, whereas there is a lack of diverse label data for edge detection of point clouds. To tackle this challenge, we generated the imprecise labels of ShapeNet [34] samples for edge detection based on the method of [12], while using the fact that ABC [35] is the only publicly available dataset with edge detection labels for 3D point clouds. In a nutshell the novelties and contributions of this paper can be summarized as follows:  
1. We introduce EDC-Net: the edge detection capsule network for 3D point clouds, a novel architecture of capsule networks which is designed for the purpose of edge detection from 3D point clouds. 2. We design a weakly-supervised transfer learning approach for edge detection of point clouds in order to tackle the challenge of lack of the diversity of annotated data. 3. We formulate a loss function assigned to the edge detection problem by combining two formats of ground-truths as edge extraction and segmentation. This combination in the loss function emphasizes the prediction of edge points and boosts the training process. 4. Our model is able to improve incrementally the proposed weakly-supervised transfer learning for edge detection from 3D point clouds. This aspect of our proposed method brings the capability of applying EDC-Net to any target data. This attribute of EDCNet is remarkable for industrial applications and is currently lacking in other edge detection techniques. The robustness of this work stems from three aspects: (1) designing capsule networks for edge detection from point clouds to disentangle geometrical features of point clouds; (2) applying weakly-supervised transfer learning to tackle the lack of annotated data; (3) formulating a loss function to improve the training process by emphasizing the prediction of edges. The contribution of this paper does not lie in merely surpassing the previous edge detection techniques. The significant aspects of our work are introducing a novel learning solution to this community, and tackling the challenges of the lack of annotated data for edge detection tasks of point clouds. The results of the proposed method are promising and constitute the first steps towards reliable point cloud edge detection based on capsule network and weakly-supervised transfer learning approaches. Our work opens a new research path in point cloud edge detection and poses novel research questions with available trends for further improvement. The remainder of the article is organized as follows: the next section reviews the related work; Section 3 describes our proposed EDC-Net; the experimental results and conclusions are presented in Sections 4 and 6, respectively.