Beyond Planar Symmetry: Modeling Human Perception of Reflection and Rotation Symmetries in the Wild

Christopher Funk, Yanxi Liu; The IEEE International Conference on Computer Vision (ICCV), 2017, pp. 793-803

Abstract


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.

Related Material


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[bibtex]
@InProceedings{Funk_2017_ICCV,
author = {Funk, Christopher and Liu, Yanxi},
title = {Beyond Planar Symmetry: Modeling Human Perception of Reflection and Rotation Symmetries in the Wild},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}