In this talk, I will introduce a method called `Consistent ZoomOut' for efficiently refining correspondences among deformable 3D shape collections, while promoting the resulting map consistency. Our formulation is closely related to a recent unidirectional spectral refinement framework, but naturally integrates map consistency constraints into the refinement. Beyond that, our formulation can be adapted to recover the underlying isometry among near-isometric shape collections with a theoretical guarantee, which is absent in the other spectral map synchronization frameworks. In the end, I will demonstrate that our method improves the accuracy compared to the competing methods when synchronizing correspondences in both near-isometric and heterogeneous shape collections, but also significantly outperforms the baselines in terms of map consistency.
Ruqi Huang is going to join TBSI as an assistant professor this fall. Prior to that, he obtained his PhD degree from the University of Paris-Saclay in 2016, and had been a postdoctoral researcher in Ecole Polytechnique from 2017 to 2019. Ruqi’s research interest lies in the areas of geometry processing, operator-based shape analysis, and 3D computer vision.