@inproceedings{18268,
  abstract     = {The registration of surfaces with non-rigid deformation, especially non-isometric deformations, is a challenging problem. When applying such techniques to real scans, the problem is compounded by topological and geometric inconsistencies between shapes. In this paper, we capture a benchmark dataset of scanned 3D shapes undergoing various controlled deformations (articulating, bending, stretching and topologically changing), along with ground truth correspondences. With the aid of this tiered benchmark of increasingly challenging real scans, we explore this problem and investigate how robust current state-of- the-art methods perform in different challenging registration and correspondence scenarios. We discover that changes in topology is a challenging problem for some methods and that machine learning-based approaches prove to be more capable of handling non-isometric deformations on shapes that are moderately similar to the training set.},
  author       = {Dyke, R.M. and Stride, C. and Lai, Y.-K. and Rosin, P.L. and Aubry, M. and Boyarski, A. and Bronstein, Alexander and Bronstein, M.M. and Cremers, D. and Fisher, M. and Groueix, T. and Guo, D. and Kim, V.G. and Kimmel, R. and Lähner, Z. and Li, K. and Litany, O. and Remez, T. and Rodola, E. and Russell, B.C. and Sahillioglu, Y. and Slossberg, R. and Tam, G.K.L. and Vestner, M. and Wu, Z. and Yang, J.},
  booktitle    = {Eurographics Workshop on 3D Object Retrieval},
  issn         = {1997-0471},
  publisher    = {The Eurographics Association},
  title        = {{Shape correspondence with isometric and non-isometric deformations}},
  doi          = {10.2312/3DOR.20191069},
  year         = {2019},
}