1 code implementation • 12 Oct 2022 • Lei LI, Nicolas Donati, Maks Ovsjanikov
Our approach is not only accurate with near-isometric input, for which a high spectral resolution is typically preferred, but also robust and able to produce reasonable matching even in the presence of significant non-isometric distortion, which poses great challenges to existing methods.
1 code implementation • CVPR 2022 • Nicolas Donati, Etienne Corman, Maks Ovsjanikov
State-of-the-art fully intrinsic networks for non-rigid shape matching often struggle to disambiguate the symmetries of the shapes leading to unstable correspondence predictions.
2 code implementations • 17 Dec 2021 • Nicolas Donati, Etienne Corman, Simone Melzi, Maks Ovsjanikov
In this paper, we introduce complex functional maps, which extend the functional map framework to conformal maps between tangent vector fields on surfaces.
3 code implementations • CVPR 2020 • Nicolas Donati, Abhishek Sharma, Maks Ovsjanikov
We present a novel learning-based approach for computing correspondences between non-rigid 3D shapes.
