论文标题
使用深度学习对遮挡点云的对称检测
Symmetry Detection of Occluded Point Cloud Using Deep Learning
论文作者
论文摘要
对称检测一直是计算机图形中的经典问题，其中许多使用传统的几何方法。但是，近年来，我们目睹了出现的深度学习改变了计算机图形的景观。在本文中，我们旨在以深度学习方式解决对遮挡点云的对称性检测。据我们所知，我们是第一个利用深度学习来解决这样一个问题的人。在这样的深度学习框架中，双重监督：使用对称平面和正常向量的点来帮助我们查明对称平面。我们在YCB-视频数据集上进行了实验，并证明了我们方法的功效。
Symmetry detection has been a classical problem in computer graphics, many of which using traditional geometric methods. In recent years, however, we have witnessed the arising deep learning changed the landscape of computer graphics. In this paper, we aim to solve the symmetry detection of the occluded point cloud in a deep-learning fashion. To the best of our knowledge, we are the first to utilize deep learning to tackle such a problem. In such a deep learning framework, double supervisions: points on the symmetry plane and normal vectors are employed to help us pinpoint the symmetry plane. We conducted experiments on the YCB- video dataset and demonstrate the efficacy of our method.
