Open Access System for Information Sharing

Login Library

 

Conference
Cited 0 time in webofscience Cited 0 time in scopus
Metadata Downloads

Reflection and Rotation Symmetry Detection via Equivariant Learning

Title
Reflection and Rotation Symmetry Detection via Equivariant Learning
Authors
서아현김병진곽수하조민수
Date Issued
2022-06-22
Publisher
IEEE Computer Society
Abstract
The inherent challenge of detecting symmetries stems from arbitrary orientations of symmetry patterns; a reflection symmetry mirrors itself against an axis with a specific orientation while a rotation symmetry matches its rotated copy with a specific orientation. Discovering such symmetry patterns from an image thus benefits from an equivariant feature representation, which varies consistently with reflection and rotation of the image. In this work, we introduce a group-equivariant convolutional network for symmetry detection, dubbed EquiSym, which leverages equivariant feature maps with respect to a dihedral group of reflection and rotation. The proposed network is built end-to-end with dihedrally-equivariant layers and trained to output a spatial map for reflection axes or rotation centers. We also present a new dataset, DENse and DIverse symmetry (DENDI), which mitigates limitations of existing benchmarks for reflection and rotation symmetry detection. Experiments show that our method achieves the state of the arts in symmetry detection on LDRS and DENDI datasets.
URI
https://oasis.postech.ac.kr/handle/2014.oak/122830
Article Type
Conference
Citation
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, page. 9529 - 9538, 2022-06-22
Files in This Item:
There are no files associated with this item.

qr_code

  • mendeley

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Related Researcher

Views & Downloads

Browse