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Found 15 papers, 5 papers with code
Hybrid Functional Maps for Crease-Aware Non-Isometric Shape Matching
Non-isometric shape correspondence remains a fundamental challenge in computer vision.
SIGMA: Scale-Invariant Global Sparse Shape Matching
We propose a novel mixed-integer programming (MIP) formulation for generating precise sparse correspondences for highly non-rigid shapes.
CCuantuMM: Cycle-Consistent Quantum-Hybrid Matching of Multiple Shapes
Jointly matching multiple, non-rigidly deformed 3D shapes is a challenging, $\mathcal{NP}$-hard problem.
Conjugate Product Graphs for Globally Optimal 2D-3D Shape Matching
We consider the problem of finding a continuous and non-rigid matching between a 2D contour and a 3D mesh.
QuAnt: Quantum Annealing with Learnt Couplings
As a result, the solution encodings can be chosen flexibly and compactly.
A Simple Strategy to Provable Invariance via Orbit Mapping
Many applications require robustness, or ideally invariance, of neural networks to certain transformations of input data.
Intrinsic Neural Fields: Learning Functions on Manifolds
Neural fields have gained significant attention in the computer vision community due to their excellent performance in novel view synthesis, geometry reconstruction, and generative modeling.
Training or Architecture? How to Incorporate Invariance in Neural Networks
Many applications require the robustness, or ideally the invariance, of a neural network to certain transformations of input data.
Q-Match: Iterative Shape Matching via Quantum Annealing
Finding shape correspondences can be formulated as an NP-hard quadratic assignment problem (QAP) that becomes infeasible for shapes with high sampling density.
Isometric Multi-Shape Matching
Finding correspondences between shapes is a fundamental problem in computer vision and graphics, which is relevant for many applications, including 3D reconstruction, object tracking, and style transfer.
Unsupervised Dense Shape Correspondence using Heat Kernels
In this work, we propose an unsupervised method for learning dense correspondences between shapes using a recent deep functional map framework.
Smooth Shells: Multi-Scale Shape Registration with Functional Maps
Smooth shells define a series of coarse-to-fine shape approximations designed to work well with multiscale algorithms.
Divergence-Free Shape Interpolation and Correspondence
We present a novel method to model and calculate deformation fields between shapes embedded in $\mathbb{R}^D$.
Efficient Deformable Shape Correspondence via Kernel Matching
We present a method to match three dimensional shapes under non-isometric deformations, topology changes and partiality.
Efficient Globally Optimal 2D-to-3D Deformable Shape Matching
We propose the first algorithm for non-rigid 2D-to-3D shape matching, where the input is a 2D shape represented as a planar curve and a 3D shape represented as a surface; the output is a continuous curve on the surface.
