Globally solving the Gromov-Wasserstein problem for point clouds in low dimensional Euclidean spaces.
Martin RynerJan KronqvistJohan KarlssonPublished in: NeurIPS (2023)
Keyphrases
- point cloud
- euclidean space
- low dimensional
- high dimensional
- higher dimensional
- data points
- pairwise distances
- dimensionality reduction
- principal component analysis
- structure from motion
- high dimensional data
- riemannian manifolds
- point sets
- shape analysis
- embedding space
- manifold learning
- vector space
- wide class
- point cloud data
- dimension reduction
- laser scanner
- input space
- euclidean distance
- geodesic distance
- feature space
- metric space
- reproducing kernel hilbert space
- laplace beltrami
- hough transform
- nearest neighbor
- d objects
- computer vision
- neural network
- data sets