Symmetric Volume Maps
- URL: http://arxiv.org/abs/2202.02568v1
- Date: Sat, 5 Feb 2022 14:44:37 GMT
- Title: Symmetric Volume Maps
- Authors: S. Mazdak Abulnaga, Oded Stein, Polina Golland, Justin Solomon
- Abstract summary: We propose a method for mapping between volumes represented as tetrahedral volumetric meshes.
Our formulation minimizes a distortion energy designed to extract maps symmetrically, without dependence on the ordering of the source and target domains.
We demonstrate our method on a diverse geometric dataset, producing low-distortion matchings that align to the boundary.
- Score: 26.715010694637243
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Although shape correspondence is a central problem in geometry processing,
most methods for this task apply only to two-dimensional surfaces. The
neglected task of volumetric correspondence--a natural extension relevant to
shapes extracted from simulation, medical imaging, volume rendering, and even
improving surface maps of boundary representations--presents unique challenges
that do not appear in the two-dimensional case. In this work, we propose a
method for mapping between volumes represented as tetrahedral meshes. Our
formulation minimizes a distortion energy designed to extract maps
symmetrically, i.e., without dependence on the ordering of the source and
target domains. We accompany our method with theoretical discussion describing
the consequences of this symmetry assumption, leading us to select a
symmetrized ARAP energy that favors isometric correspondences. Our final
formulation optimizes for near-isometry while matching the boundary. We
demonstrate our method on a diverse geometric dataset, producing low-distortion
matchings that align to the boundary.
Related papers
- Spectral Meets Spatial: Harmonising 3D Shape Matching and Interpolation [50.376243444909136]
We present a unified framework to predict both point-wise correspondences and shape between 3D shapes.
We combine the deep functional map framework with classical surface deformation models to map shapes in both spectral and spatial domains.
arXiv Detail & Related papers (2024-02-29T07:26:23Z) - Adaptive Surface Normal Constraint for Geometric Estimation from Monocular Images [56.86175251327466]
We introduce a novel approach to learn geometries such as depth and surface normal from images while incorporating geometric context.
Our approach extracts geometric context that encodes the geometric variations present in the input image and correlates depth estimation with geometric constraints.
Our method unifies depth and surface normal estimations within a cohesive framework, which enables the generation of high-quality 3D geometry from images.
arXiv Detail & Related papers (2024-02-08T17:57:59Z) - Partial Symmetry Detection for 3D Geometry using Contrastive Learning
with Geodesic Point Cloud Patches [10.48309709793733]
We propose to learn rotation, reflection, translation and scale invariant local shape features for geodesic point cloud patches.
We show that our approach is able to extract multiple valid solutions for this ambiguous problem.
We incorporate the detected symmetries together with a region growing algorithm to demonstrate a downstream task.
arXiv Detail & Related papers (2023-12-13T15:48:50Z) - 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) - Geometrically Consistent Partial Shape Matching [50.29468769172704]
Finding correspondences between 3D shapes is a crucial problem in computer vision and graphics.
An often neglected but essential property of matching geometrics is consistency.
We propose a novel integer linear programming partial shape matching formulation.
arXiv Detail & Related papers (2023-09-10T12:21:42Z) - Towards classification of holographic multi-partite entanglement
measures [0.0]
We classify and count general measures as invariants of local unitary transformations.
We derive their holographic dual with the assumption that the replica symmetry is unbroken in the bulk.
We discuss the replica symmetry assumption and also how the already known entanglement measures, such as entanglement negativity and reflected entropy fit in our framework.
arXiv Detail & Related papers (2023-04-12T18:04:11Z) - A Level Set Theory for Neural Implicit Evolution under Explicit Flows [102.18622466770114]
Coordinate-based neural networks parameterizing implicit surfaces have emerged as efficient representations of geometry.
We present a framework that allows applying deformation operations defined for triangle meshes onto such implicit surfaces.
We show that our approach exhibits improvements for applications like surface smoothing, mean-curvature flow, inverse rendering and user-defined editing on implicit geometry.
arXiv Detail & Related papers (2022-04-14T17:59:39Z) - Elastic shape analysis of surfaces with second-order Sobolev metrics: a
comprehensive numerical framework [11.523323270411959]
This paper introduces a set of numerical methods for shape analysis of 3D surfaces.
We address the computation of geodesics and geodesic distances between parametrized or unparametrized surfaces represented as 3D meshes.
arXiv Detail & Related papers (2022-04-08T18:19:05Z) - Improving Metric Dimensionality Reduction with Distributed Topology [68.8204255655161]
DIPOLE is a dimensionality-reduction post-processing step that corrects an initial embedding by minimizing a loss functional with both a local, metric term and a global, topological term.
We observe that DIPOLE outperforms popular methods like UMAP, t-SNE, and Isomap on a number of popular datasets.
arXiv Detail & Related papers (2021-06-14T17:19:44Z) - Shape analysis via inconsistent surface registration [4.367664806447789]
We develop a framework for shape analysis using inconsistent surface mapping.
Our method is capable of solving this problem using inconsistent surface registration based on quasi-conformal theory.
arXiv Detail & Related papers (2020-03-03T06:58:16Z) - Inverse Learning of Symmetries [71.62109774068064]
We learn the symmetry transformation with a model consisting of two latent subspaces.
Our approach is based on the deep information bottleneck in combination with a continuous mutual information regulariser.
Our model outperforms state-of-the-art methods on artificial and molecular datasets.
arXiv Detail & Related papers (2020-02-07T13:48:52Z)
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.