activity
20172026
most citedDeformation Recovery: Localized Learning for Detail-Preserving Deformations

7 citations · 7 across the 3 of their papers we have counts for

collaborators

5 papers

cs.GR2026

Implicit Minimal Surfaces for Bijective Correspondences

Etienne Corman, Yousuf Soliman, Robin Magnet +1

We introduce an implicit representation of continuous, bijective, orientation-preserving maps between genus zero surfaces with or without boundary. The distortion of these maps can…

cs.GR2024★ 7 cited

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…

cs.CV2022

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…

cs.CV2021

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…

cs.GR2017

Functional Characterization of Deformation Fields

Etienne Corman, Maks Ovsjanikov

In this paper we present a novel representation for deformation fields of 3D shapes, by considering the induced changes in the underlying metric. In particular, our approach allows…