Symmetry Detection of Occluded Point Cloud Using Deep Learning
This addresses a classical computer graphics problem for applications like 3D modeling, but it is incremental as it applies deep learning to a specific scenario.
The paper tackles symmetry detection in occluded point clouds using a deep learning framework with double supervisions for points and normal vectors, achieving efficacy as demonstrated on the YCB-video dataset.
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.