Beyond Planar Symmetry: Modeling Human Perception of Reflection and Rotation Symmetries in the Wild
2017 IEEE International Conference on Computer Vision (ICCV)(2017)
摘要
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 onphotos 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.
更多查看译文
关键词
discrete human labels,symmetry heatmaps,capture symmetry,Sym-NET,CVPR 2013 symmetry competition testsets,planar symmetry,human perception,rotation symmetries,superb symmetry perception mechanism,computational model,human perceived symmetries,deep-learning neural network,rotation symmetry detection,computer vision algorithms,MS-COCO photos,temperature 11.0 K
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要