GenSP: Consistent Spherical Parameterization via Learning Shape Generative Models

• URL: http://arxiv.org/abs/2607.00492v1
• Date: Wed, 01 Jul 2026 06:19:55 GMT
• Title: GenSP: Consistent Spherical Parameterization via Learning Shape Generative Models
• Abstract summary: We introduce GenSP, a data-driven framework that learns consistent spherical parameterizations across a collection of genus-0 shapes.<n>We address several key challenges in learning such a generative model.<n>Experiments on the ShapeNet dataset demonstrate that our approach significantly reduces geometric distortion.
• Score: 45.68408819044841
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce GenSP, a data-driven framework that learns consistent spherical parameterizations across a collection of genus-0 shapes. Instead of optimizing the parameterization of each shape independently, our method learns a neural generative model that predicts a continuous mapping from the unit sphere to shapes in a dataset. Under this formulation, spherical parameterizations are obtained through the inverse mappings of the learned generator, which encourages similar shapes to share consistent parameterizations. To make this formulation practical, we address several key challenges in learning such a generative model. First, we introduce a continuous neural deformation model that predicts surface points from sphere coordinates and latent shape codes, avoiding discretization artifacts common in mesh-based formulations. Second, we augment the training space with intermediate shapes that bridge the sphere and input shapes, allowing the model to learn meaningful deformations across a heterogeneous shape collection. Third, we compute reliable initial correspondences by propagating mappings along a spanning tree of training shapes in the latent space. Experiments on the ShapeNet dataset demonstrate that our approach significantly reduces geometric distortion and improves cross-shape consistency compared with state-of-the-art spherical parameterization methods.

Related papers

• Point-Based Shape Representation Generation with a Correspondence-Preserving Diffusion Model [1.5624421399300303]
  We propose a diffusion model designed to generate point-based shape representations with correspondences.<n>Using shape representation data with correspondences from Open Access Series of Imaging Studies 3 (OASIS-3), we demonstrate that our correspondence-preserving model effectively generates point-based hippocampal shape representations.
  arXiv  Detail & Related papers  (2025-08-05T21:36:26Z)
• SVarM: Linear Support Varifold Machines for Classification and Regression on Geometric Data [4.212663349859165]
  This work proposes textitSVarM to exploit varifold representations of shapes as measures and their duality with test functions $h:mathbbRn times Sn-1 rightarrow mathbbR$.<n>We develop classification and regression models on shape datasets by introducing a neural network-based representation of the trainable test function $h$.
  arXiv  Detail & Related papers  (2025-06-01T21:55:15Z)
• GenAnalysis: Joint Shape Analysis by Learning Man-Made Shape Generators with Deformation Regularizations [21.923143529947886]
  GenAnalysis is an implicit shape generation framework that allows joint analysis of man-made shapes.<n>We show how to extract shape variations by recovering piecewise affine vector fields in the tangent space of each shape.<n>We then derive shape correspondences by iteratively propagating AAAP deformations across a sequence of intermediate shapes.
  arXiv  Detail & Related papers  (2025-03-02T09:17:08Z)
• Level-Set Parameters: Novel Representation for 3D Shape Analysis [70.23417107911567]
  Recent development of neural fields brings in level-set parameters from signed distance functions as a novel, continuous, and numerical representation of 3D shapes.<n>We establish correlations across different shapes by formulating them as a pseudo-normal distribution, and learn the distribution prior to the respective dataset.<n>To further explore the level-set parameters with shape transformations, we propose to condition a subset of these parameters on rotations and translations, and generate them with a hypernetwork.
  arXiv  Detail & Related papers  (2024-12-18T04:50:19Z)
• 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)
• Geometric Neural Diffusion Processes [55.891428654434634]
  We extend the framework of diffusion models to incorporate a series of geometric priors in infinite-dimension modelling. We show that with these conditions, the generative functional model admits the same symmetry.
  arXiv  Detail & Related papers  (2023-07-11T16:51:38Z)
• Functional additive regression on shape and form manifolds of planar curves [0.0]
  We define shape and form as equivalence classes under translation, rotation and -- for shapes -- also scale. We extend generalized additive regression to models for the shape/form of planar curves or landmark.
  arXiv  Detail & Related papers  (2021-09-06T17:43:32Z)
• Representing Shape Collections with Alignment-Aware Linear Models [17.635846912560627]
  We revisit the classical representation of 3D point clouds as linear shape models. Our key insight is to leverage deep learning to represent a collection of shapes as affine transformations.
  arXiv  Detail & Related papers  (2021-09-03T16:28:34Z)
• NeuroMorph: Unsupervised Shape Interpolation and Correspondence in One Go [109.88509362837475]
  We present NeuroMorph, a new neural network architecture that takes as input two 3D shapes. NeuroMorph produces smooth and point-to-point correspondences between them. It works well for a large variety of input shapes, including non-isometric pairs from different object categories.
  arXiv  Detail & Related papers  (2021-06-17T12:25:44Z)
• SNARF: Differentiable Forward Skinning for Animating Non-Rigid Neural Implicit Shapes [117.76767853430243]
  We introduce SNARF, which combines the advantages of linear blend skinning for polygonal meshes with neural implicit surfaces. We propose a forward skinning model that finds all canonical correspondences of any deformed point using iterative root finding. Compared to state-of-the-art neural implicit representations, our approach generalizes better to unseen poses while preserving accuracy.
  arXiv  Detail & Related papers  (2021-04-08T17:54:59Z)

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.