  Humans take advantage of real world symmetries for various tasks, yet
capturing their superb symmetry perception mechanism with a computational model
remains elusive. Motivated by a new study demonstrating the extremely high
inter-person accuracy of human perceived symmetries in the wild, we have
constructed the first deep-learning neural network for reflection and rotation
symmetry detection (Sym-NET), trained on photos from MS-COCO (Microsoft-Common
Object in COntext) dataset with nearly 11K consistent symmetry-labels from more
than 400 human observers. We employ novel methods to convert discrete human
labels into symmetry heatmaps, capture symmetry densely in an image and
quantitatively evaluate Sym-NET against multiple existing computer vision
algorithms. On CVPR 2013 symmetry competition testsets and unseen MS-COCO
photos, Sym-NET significantly outperforms all other competitors. Beyond
mathematically well-defined symmetries on a plane, Sym-NET demonstrates
abilities to identify viewpoint-varied 3D symmetries, partially occluded
symmetrical objects, and symmetries at a semantic level.
