# Beyond Planar Symmetry: Modeling human perception of reflection and rotation symmetries in the wild Source: [Beyond Planar Symmetry: Modeling human perception of reflection and rotation symmetries in the wild]() Collection index: [llms.txt]() Data updated: 2025-10-25T19:00:51.301Z Authors: Christopher Funk, Yanxi Liu Published: 2017-04-11T23:25:25.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Rotation Symmetry Detection on DENDI (test)](): Rotation Symmetry Detection; DENDI (test); F1 Score: 11.9. - [Reflection Symmetry Detection on DENDI (test)](): Reflection Symmetry Detection; DENDI (test); F1 Score: 30.7. ## Original sources and implementation [Original paper]() [PDF]()