PRS-Net: Planar Reflective Symmetry Detection Net for 3D Models

IEEE Transactions on Visualization and Computer Graphics, vol. 27, pp. 3007–3018

Abstract

In geometry processing, symmetry is a universal type of high-level structural information of 3D models and benefits many geometry processing tasks including shape segmentation, alignment, matching, and completion. Thus it is an important problem to analyze various symmetry forms of 3D shapes. Planar reflective symmetry is the most fundamental one. Traditional methods based on spatial sampling can be time-consuming and may not be able to identify all the symmetry planes. In this article, we present a novel learning framework to automatically discover global planar reflective symmetry of a 3D shape. Our framework trains an unsupervised 3D convolutional neural network to extract global model features and then outputs possible global symmetry parameters, where input shapes are represented using voxels. We introduce a dedicated symmetry distance loss along with a regularization loss to avoid generating duplicated symmetry planes. Our network can also identify generalized cylinders by predicting their rotation axes. We further provide a method to remove invalid and duplicated planes and axes. We demonstrate that our method is able to produce reliable and accurate results. Our neural network based method is hundreds of times faster than the state-of-the-art methods, which are based on sampling. Our method is also robust even with noisy or incomplete input surfaces.

Authors 6

1. Lin Gao

   Chinese Academy of Sciences · Institute of Computing Technology · University of Chinese Academy of Sciences

   Affiliation as printed

   Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

   University of Chinese Academy of Sciences, Beijing, China

2. Lingxiao Zhang

   Chinese Academy of Sciences · Institute of Computing Technology · University of Chinese Academy of Sciences

   Affiliation as printed

   Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

   University of Chinese Academy of Sciences, Beijing, China

3. Hsien-Yu Meng

   University of Maryland, College Park

   Affiliation as printed

   University of Maryland, College Park, MD, USA

4. Yihui Ren

   Chinese Academy of Sciences · Institute of Computing Technology · University of Chinese Academy of Sciences

   Affiliation as printed

   Institute of Computing Technology, Chinese Academy of Sciences, Beijing, China

   University of Chinese Academy of Sciences, Beijing, China

5. Yu‐Kun Lai

   Cardiff University

   Affiliation as printed

   School of Computer Science and Informatics, Cardiff University, Wales, United Kingdom

6. Leif P. Kobbelt Aachen Institute for Computer Graphics and Multimedia

   RWTH Aachen University

   Affiliation as printed

   Institute for Computer Graphics and Multimedia, RWTH Aachen University, Aachen, Germany