We propose a novel learning-based approach for robust 3D shape matching. Our method
builds upon deep functional maps and can be trained in a fully unsupervised manner …

In this paper, we propose a novel method, which we call Consistent ZoomOut, for efficiently
refining correspondences among deformable 3D shape collections, while promoting the …

Finding correspondences between shapes is a central task in geometry processing with
applications such as texture or deformation transfer and shape interpolation. We develop a …

We propose a novel approach for the approximation and transfer of signals across 3D
shapes. The proposed solution is based on taking pointwise polynomials of the Fourier‐like …

We propose a fully-automatic method that computes from scratch point-to-point dense
correspondences between isometric shapes under topological noise. While relying on …

Alignment between non-rigid stretchable structures is one of the most challenging tasks in
computer vision, as the invariant properties are hard to define, and there is no labeled data …

COMPUTATIONAL ANALYSIS OF DEFORMABLE MANIFOLDS: FROM GEOMETRIC
MODELING TO DEEP LEARNING Stefan C. Schonsheck Page 1 COMPUTATIONAL ANALYSIS …