Cover Image

Roto-Translation Invariant Metrics on Position-Orientation Space

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Bellaard, Gijs, Smets, Bart M. N.
Format:	Preprint
Published:	2025
Subjects:	Differential Geometry Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Universal Collection of Euclidean Invariants between Pairs of Position-Orientations
by: Bellaard, Gijs, et al.
Published: (2025)

Flow Matching on Lie Groups
by: Sherry, Finn M., et al.
Published: (2025)

Analysis and Computation of Geodesic Distances on Reductive Homogeneous Spaces
by: Duits, Remco, et al.
Published: (2025)

Optimal Transport on the Lie Group of Roto-translations
by: Bon, Daan, et al.
Published: (2024)

Diffusion-Shock Filtering on the Space of Positions and Orientations
by: Sherry, Finn M., et al.
Published: (2025)

PDE-CNNs: Axiomatic Derivations and Applications
by: Bellaard, Gijs, et al.
Published: (2024)

Interpretable Analytic Calabi-Yau Metrics via Symbolic Distillation
by: Eng, D Yang
Published: (2026)

Geometric Learning with Positively Decomposable Kernels
by: Da Costa, Nathael, et al.
Published: (2023)

Diffusion-Shock PDEs for Deep Learning on Position-Orientation Space
by: Sherry, Finn M., et al.
Published: (2025)

Geometry-Aware Generative Autoencoders for Warped Riemannian Metric Learning and Generative Modeling on Data Manifolds
by: Sun, Xingzhi, et al.
Published: (2024)

Steerable Neural ODEs on Homogeneous Spaces
by: Andersdotter, Emma, et al.
Published: (2026)

Discretized Gradient Flow for Manifold Learning in the Space of Embeddings
by: Gold, Dara, et al.
Published: (2019)

Robust Tangent Space Estimation via Laplacian Eigenvector Gradient Orthogonalization
by: Kohli, Dhruv, et al.
Published: (2025)

Thinner Latent Spaces: Detecting Dimension and Imposing Invariance with Conformal Autoencoders
by: Kevrekidis, George A., et al.
Published: (2024)

Distance Measure Based on an Embedding of the Manifold of K-Component Gaussian Mixture Models into the Manifold of Symmetric Positive Definite Matrices
by: Vishwakarma, Amit, et al.
Published: (2025)

Sub-Riemannian Snakes on the Projective Line Bundle with Applications to Segmentation of SEM Images
by: Vis, Leanne, et al.
Published: (2026)

Minimising Willmore Energy via Neural Flow
by: Hirst, Edward, et al.
Published: (2026)

Principal Component Analysis in Space Forms
by: Tabaghi, Puoya, et al.
Published: (2023)

Sasaki Metric for Spline Models of Manifold-Valued Trajectories
by: Nava-Yazdani, Esfandiar, et al.
Published: (2023)

Machine Learned Calabi-Yau Metrics and Curvature
by: Berglund, Per, et al.
Published: (2022)

Space filling positionality and the Spiroformer
by: Maurin, M., et al.
Published: (2025)

Revealing Decurve Flows for Generalized Graph Propagation
by: Lin, Chen, et al.
Published: (2024)

Metric Flows with Neural Networks
by: Halverson, James, et al.
Published: (2023)

Symbolic Approximations to Ricci-flat Metrics Via Extrinsic Symmetries of Calabi-Yau Hypersurfaces
by: Mirjanić, Viktor, et al.
Published: (2024)

Jet Functors and Weil Algebras in Automatic Differentiation: A Geometric Analysis
by: Sangha, Amandip
Published: (2025)

Barycentric subspace analysis of network-valued data
by: Maignant, Elodie, et al.
Published: (2025)

Learning Beyond Euclid: Curvature-Adaptive Generalization for Neural Networks on Manifolds
by: Sarkar, Krisanu
Published: (2025)

Deep Generative Models: Complexity, Dimensionality, and Approximation
by: Wang, Kevin, et al.
Published: (2025)

Riemannian Integrated Gradients: A Geometric View of Explainable AI
by: Costanza, Federico, et al.
Published: (2025)

Fiber Bundle Networks: A Geometric Machine Learning Paradigm
by: Liu, Dong
Published: (2025)

Universal kernels via harmonic analysis on Riemannian symmetric spaces
by: Steinert, Franziskus, et al.
Published: (2025)

Tangentially Aligned Integrated Gradients for User-Friendly Explanations
by: Simpson, Lachlan, et al.
Published: (2025)

Simultaneous Optimization of Geodesics and Fréchet Means
by: Rygaard, Frederik Möbius, et al.
Published: (2025)

Staying on the Manifold: Geometry-Aware Noise Injection
by: Jacobsen, Albert Kjøller, et al.
Published: (2025)

Manifold Learning with Normalizing Flows: Towards Regularity, Expressivity and Iso-Riemannian Geometry
by: Diepeveen, Willem, et al.
Published: (2025)

Riemannian Variational Flow Matching for Material and Protein Design
by: Zaghen, Olga, et al.
Published: (2025)

Hardness of Learning Neural Networks under the Manifold Hypothesis
by: Kiani, Bobak T., et al.
Published: (2024)

Cartan moving frames and the data manifolds
by: Tron, Eliot, et al.
Published: (2024)

Kernel-Gradient Drifting Models
by: Esteban-Casadevall, Maria, et al.
Published: (2026)

Complex normalizing flows can almost be information Kähler-Ricci flows
by: Gracyk, Andrew
Published: (2026)