Abstract
Edge features in point clouds are prominent due to the capability of describing an abstract shape of a set of points. Point clouds obtained by 3D scanner devices are often immense in terms of size. Edges are essential features in large scale point clouds since they are capable of describing the shapes in down-sampled point clouds while maintaining the principal information. In this paper, we tackle challenges of edge detection tasks in 3D point clouds. To this end, we propose a novel technique to detect edges of point clouds based on a capsule network architecture. In this approach, we define the edge detection task of point clouds as a semantic segmentation problem. We built a classifier through the capsules to predict edge and non-edge points in 3D point clouds. We applied a weakly-supervised learning approach in order to improve the performance of our proposed method and built in the capability of testing the technique in wider range of shapes. We provide several quantitative and qualitative experimental results to demonstrate the robustness of our proposed EDC-Net for edge detection in 3D point clouds. We performed a statistical analysis over the ABC and ShapeNet datasets. Our numerical results demonstrate the robust and efficient performance of EDC-Net.
Highlights
Point cloud data is a fundamental class of 3D data—a type of raw data from various 3D sensor devices
We set a threshold (τ) as 0.1 (τ = 0.1); if the distance (d) of a point to the closest feature lines of point clouds was smaller than the threshold (d < τ), the point was assigned as an edge point; otherwise, (d > τ) the point was assigned as a non-edge point
+ FP, Recall = TP+ FN where TP stands for true positives representing the number of correctly detected points; FP stands for false positives representing the number of wrongly detected points; FN stands for false negatives, representing the number of false rejections, i.e., edge points in the ground-truth that are not detected as edges by EDC-Net model
Summary
Point cloud data is a fundamental class of 3D data—a type of raw data from various 3D sensor devices. Point clouds are playing an important role in the computer vision community due to their rich geometric information and the proliferation of 3D sensors. The importance of 3D point cloud data stems from their depth information and geometric structure; Edges in 3D point clouds are considered as remarkably meaningful features due to their capability of representing the topological shape of a set of points. Extracting edges from 3D point clouds is one of the fundamental shape understanding methods which is able to describe the abstract features of a point set. Extracting edges from point clouds provides a smaller chunk of data while preserving the feature information of shapes in point clouds. The proliferation of algorithms for reconstructing 3D objects, e.g., [4,5], is an affirmation of the importance of the 3D information in the unreachable cases to the 3D sensors
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
