Spatially and spectrally consistent deep functional maps
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 …
within a collection of shapes. In this paper, we investigate its utility in the approaches of …
Unsupervised deep multi-shape matching
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 …
graphics. While deep neural networks were shown to lead to state-of-the-art results in shape …
Self-supervised learning for multimodal non-rigid 3d shape matching
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 …
meshes, as well as for shapes represented as point clouds. While point clouds are a …
Q-FW: A hybrid classical-quantum Frank-Wolfe for quadratic binary optimization
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 …
FW, for solving quadratic, linearly-constrained, binary optimization problems on quantum …
Kissing to find a match: efficient low-rank permutation representation
Permutation matrices play a key role in matching and assignment problems across the
fields, especially in computer vision and robotics. However, memory for explicitly …
fields, especially in computer vision and robotics. However, memory for explicitly …
G-msm: Unsupervised multi-shape matching with graph-based affinity priors
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 …
learning approach for non-rigid shape correspondence. Rather than treating a collection of …