Non‐rigid registration computes an alignment between a source surface with a target
surface in a non‐rigid manner. In the past decade, with the advances in 3D sensing …

Deep learning approach has been used extensively in image analysis tasks. However,
implementing the methods in 3D data is a bit complex because most of the previously …

Geometric deep learning is an umbrella term for emerging techniques attempting to
generalize (structured) deep neural models to non-Euclidean domains, such as graphs and …

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 …

We present a simple and efficient method for refining maps or correspondences by iterative
upsampling in the spectral domain that can be implemented in a few lines of code. Our main …

3D data is a valuable asset the computer vision filed as it provides rich information about the
full geometry of sensed objects and scenes. Recently, with the availability of both large 3D …

We propose a method for efficiently computing orientation-preserving and approximately
continuous correspondences between non-rigid shapes, using the functional maps …

We consider the problem of computing dense correspondences between non-rigid shapes
with potentially significant partiality. Existing formulations tackle this problem through heavy …

Cycle consistency has long been exploited as a powerful prior for jointly optimizing maps
within a collection of shapes. In this paper, we investigate its utility in the approaches of …

We present a novel method for computing correspondences across 3D shapes using
unsupervised learning. Our method computes a non-linear transformation of given …