Landmark shape spaces with induced metrics
- URL: http://arxiv.org/abs/2607.28064v1
- Date: Thu, 30 Jul 2026 11:41:19 GMT
- Title: Landmark shape spaces with induced metrics
- Abstract summary: We present a unification of Kendall's landmark shape spaces, where rigid motions are factored out and scale fixed on landmark configurations equipped with Euclidean geometry.<n>The regularity of the descending metric prevents landmarks from colliding, the metric is defined in the ambient space independent of the number of landmarks, local rigid transformations are preserved, global rigid motions are removed, and scale fixed.
- Score: 2.1665689529884697
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: We present a unification of Kendall's landmark shape spaces, where rigid motions are factored out and scale fixed on landmark configurations equipped with Euclidean geometry, with landmark configuration spaces carrying Riemannian metrics descending from right-invariant Sobolev metrics on the diffeomorphism group. The resulting new landmark shape spaces achieve the defining properties of both approaches: The regularity of the descending metric prevents landmarks from colliding, the metric is defined in the ambient space independent of the number of landmarks, local rigid transformations are preserved, global rigid motions are removed, and scale fixed. To achieve this, we define a particular Sobolev-type operator, the screened elasticity operator, whose null-space consists exactly of the rigid motions, we show how this operator descends to achieve the desired geometry, and we present approaches to solving matching problems and computing geodesics numerically. The resulting construction allows the use of landmark configuration spaces with sufficiently regular metrics in applications while retaining the shape invariances that are a hallmark of Kendall's shape spaces.
Related papers
- Group-Equivariant Poincaré Convolutional Networks [2.1092185983505396]
Poincaré ResNets have demonstrated the potential of learning visual representations directly in hyperbolic space.<n>We propose Equivariant Poincaré ResNets, combining hyperbolic geometry with discrete symmetry groups.<n>We propose geometrically safe tensor reshaping, left-regular permutations for hyperbolic group convolutions, and joint-orientation Poincaré Midpoint Batch normalisation.
arXiv Detail & Related papers (2026-07-01T07:44:28Z) - LAST: Bridging Vision-Language and Action Manifolds via Gromov-Wasserstein Alignment [71.3712075841632]
LAST reconstructs the action space to establish local metric compatibility with the Vision-Language-Action modality.<n> LAST enables VLA models with superior convergence and generalizability.
arXiv Detail & Related papers (2026-05-27T03:48:10Z) - General Covariant Action Modeling: Constructing Generalized Manifolds via Spatio-Temporal Decoupling [48.559681359374146]
Generalized Action Manifold (GAM) framework enforces general covariance through structural disentanglement.<n>GAM enables superior transfer and robustness capabilities, outperforming geometry-agnostic baselines.
arXiv Detail & Related papers (2026-05-27T03:38:15Z) - Riemannian-Manifold Steering: Geometry-Aware Generative Autoencoders for Label-Free Steering [3.854000879223449]
We recast manifold steering as textbfRiemannian geodesic computation on activation space.<n>We approximate this with a learned encoder trained on output distances over a small concept-token schema.
arXiv Detail & Related papers (2026-05-24T08:41:56Z) - SGMatch: Semantic-Guided Non-Rigid Shape Matching with Flow Regularization [43.12848612518533]
We introduce SGMatch, a learning-based framework for semantic-guided non-rigid shape matching.<n> Specifically, we design a Semantic-Guided Local Cross-Attention module that integrates semantic features from vision foundation models into geometric descriptors.<n>We introduce a regularization objective based on conditional flow matching, which supervises a time-varying velocity field to encourage spatial smoothness of the recovered correspondences.
arXiv Detail & Related papers (2026-03-13T12:31:43Z) - Riemannian Flow Matching for Disentangled Graph Domain Adaptation [51.98961391065951]
Graph Domain Adaptation (GDA) typically uses adversarial learning to align graph embeddings in Euclidean space.<n>DisRFM is a geometry-aware GDA framework that unifies embedding and flow-based transport.
arXiv Detail & Related papers (2026-01-31T11:05:35Z) - Riemannian Zeroth-Order Gradient Estimation with Structure-Preserving Metrics for Geodesically Incomplete Manifolds [57.179679246370114]
We construct metrics that are geodesically complete while ensuring that every stationary point under the new metric remains stationary under the original one.<n>An $$-stationary point under the constructed metric $g'$ also corresponds to an $$-stationary point under the original metric $g'$.<n>Experiments on a practical mesh optimization task demonstrate that our framework maintains stable convergence even in the absence of geodesic completeness.
arXiv Detail & Related papers (2026-01-12T22:08:03Z) - ARGUS: Adaptive Rotation-Invariant Geometric Unsupervised System [0.0]
This paper introduces Argus, a framework that reconceptualizes drift detection as tracking local statistics over a fixed spatial partition of the data manifold.<n> Voronoi tessellations over canonical orthonormal frames yield drift metrics that are invariant to transformations.<n>A graph-theoretic characterization of drift propagation is developed that distinguishes coherent distributional shifts from isolated perturbations.
arXiv Detail & Related papers (2026-01-03T22:39:20Z) - Basis restricted elastic shape analysis on the space of unregistered
surfaces [10.543359560247847]
This paper introduces a new mathematical and numerical framework for surface analysis.
The specificity of the approach we develop is to restrict the space of allowable transformations to predefined finite dimensional bases of deformation fields.
We specifically validate our approach on human body shape and pose data as well as human face scans, and show how it generally outperforms state-of-the-art methods on problems such as shape registration, motion transfer or random pose generation.
arXiv Detail & Related papers (2023-11-07T23:06:22Z) - Shape And Structure Preserving Differential Privacy [70.08490462870144]
We show how the gradient of the squared distance function offers better control over sensitivity than the Laplace mechanism.
We also show how using the gradient of the squared distance function offers better control over sensitivity than the Laplace mechanism.
arXiv Detail & Related papers (2022-09-21T18:14:38Z) - Frame Averaging for Equivariant Shape Space Learning [85.42901997467754]
A natural way to incorporate symmetries in shape space learning is to ask that the mapping to the shape space (encoder) and mapping from the shape space (decoder) are equivariant to the relevant symmetries.
We present a framework for incorporating equivariance in encoders and decoders by introducing two contributions.
arXiv Detail & Related papers (2021-12-03T06:41:19Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.