An evaluation of canonical forms for non-rigid 3D shape retrieval


Pickup D., Liu J., Sun X., Rosin P. L. , Martin R. R. , Cheng Z., ...Daha Fazla

GRAPHICAL MODELS, cilt.97, ss.17-29, 2018 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 97
  • Basım Tarihi: 2018
  • Doi Numarası: 10.1016/j.gmod.2018.02.002
  • Dergi Adı: GRAPHICAL MODELS
  • Sayfa Sayıları: ss.17-29

Özet

Canonical forms attempt to factor out a non-rigid shape's pose, giving a pose-neutral shape. This opens up the possibility of using methods originally designed for rigid shape retrieval for the task of non-rigid shape retrieval. We extend our recent benchmark for testing canonical form algorithms. Our new benchmark is used to evaluate a greater number of state-of-the-art canonical forms, on five recent non-rigid retrieval datasets, within two different retrieval frameworks. A total of fifteen different canonical form methods are compared. We find that the difference in retrieval accuracy between different canonical form methods is small, but varies significantly across different datasets. We also find that efficiency is the main difference between the methods.