14 citations · 21 across the 4 of their papers we have counts for
5 papers
Deformation Recovery: Localized Learning for Detail-Preserving Deformations
Ramana Sundararaman, Nicolas Donati, Simone Melzi +2
We introduce a novel data-driven approach aimed at designing high-quality shape deformations based on a coarse localized input signal. Unlike previous data-driven methods that requ…
Learning Multi-resolution Functional Maps with Spectral Attention for Robust Shape Matching
Lei Li, Nicolas Donati, Maks Ovsjanikov
In this work, we present a novel non-rigid shape matching framework based on multi-resolution functional maps with spectral attention. Existing functional map learning methods all…
Deep Orientation-Aware Functional Maps: Tackling Symmetry Issues in Shape Matching
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. M…
Complex Functional Maps : a Conformal Link Between Tangent Bundles
Nicolas Donati, Etienne Corman, Simone Melzi +1
In this paper, we introduce complex functional maps, which extend the functional map framework to conformal maps between tangent vector fields on surfaces. A key property of these…
Deep Geometric Functional Maps: Robust Feature Learning for Shape Correspondence
Nicolas Donati, Abhishek Sharma, Maks Ovsjanikov
We present a novel learning-based approach for computing correspondences between non-rigid 3D shapes. Unlike previous methods that either require extensive training data or operate…