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 …

Abstract 3D shape matching is a long-standing problem in computer vision and computer
graphics. While deep neural networks were shown to lead to state-of-the-art results in shape …

The matching of 3D shapes has been extensively studied for shapes represented as surface
meshes, as well as for shapes represented as point clouds. While point clouds are a …

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 …

We present SyNoRiM, a novel way to jointly register multiple non-rigid shapes by
synchronizing the maps that relate learned functions defined on the point clouds. Even …

Jointly matching multiple, non-rigidly deformed 3D shapes is a challenging, NP-hard
problem. A perfect matching is necessarily cycle-consistent: Following the pairwise point …

We present a scalable combinatorial algorithm for globally optimizing over the space of
geometrically consistent mappings between 3D shapes. We use the mathematically elegant …

We present a hybrid classical-quantum framework based on the Frank-Wolfe algorithm, Q-
FW, for solving quadratic, linearly-constrained, binary optimization problems on quantum …

Permutation matrices play a key role in matching and assignment problems across the
fields, especially in computer vision and robotics. However, memory for explicitly …

Abstract We present G-MSM (Graph-based Multi-Shape Matching), a novel unsupervised
learning approach for non-rigid shape correspondence. Rather than treating a collection of …