Unsupervised Scale-Invariant Multispectral Shape Matching

• URL: http://arxiv.org/abs/2012.10685v1
• Date: Sat, 19 Dec 2020 13:44:45 GMT
• Title: Unsupervised Scale-Invariant Multispectral Shape Matching
• Authors: Idan Pazi, Dvir Ginzburg, Dan Raviv
• Abstract summary: Alignment between non-rigid stretchable structures is one of the hardest tasks in computer vision. We present unsupervised neural network architecture based upon the spectrum of scale-invariant geometry. Our method is agnostic to local-scale deformations and shows superior performance for matching shapes from different domains.
• Score: 7.04719493717788
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Alignment between non-rigid stretchable structures is one of the hardest tasks in computer vision, as the invariant properties are hard to define on one hand, and on the other hand no labelled data exists for real datasets. We present unsupervised neural network architecture based upon the spectrum of scale-invariant geometry. We build ontop the functional maps architecture, but show that learning local features, as done until now, is not enough once the isometric assumption breaks but can be solved using scale-invariant geometry. Our method is agnostic to local-scale deformations and shows superior performance for matching shapes from different domains when compared to existing spectral state-of-the-art solutions.

Related papers

• Hybrid Functional Maps for Crease-Aware Non-Isometric Shape Matching [42.0728900164228]
  We propose a novel approach of combining the non-orthogonal extrinsic basis of eigenfunctions of the elastic thin-shell hessian with the intrinsic ones of the Laplace-Beltrami operator (LBO) eigenmodes. We show extensive evaluations across various supervised and unsupervised settings and demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-12-06T18:41:01Z)
• Deformation-Guided Unsupervised Non-Rigid Shape Matching [7.327850781641328]
  We present an unsupervised data-driven approach for non-rigid shape matching. Our approach is particularly robust when matching digitized shapes using 3D scanners.
  arXiv  Detail & Related papers  (2023-11-27T09:55:55Z)
• Bending Graphs: Hierarchical Shape Matching using Gated Optimal Transport [80.64516377977183]
  Shape matching has been a long-studied problem for the computer graphics and vision community. We investigate a hierarchical learning design, to which we incorporate local patch-level information and global shape-level structures. We propose a novel optimal transport solver by recurrently updating features on non-confident nodes to learn globally consistent correspondences between the shapes.
  arXiv  Detail & Related papers  (2022-02-03T11:41:46Z)
• 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)
• 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)
• Spatial-spectral Hyperspectral Image Classification via Multiple Random Anchor Graphs Ensemble Learning [88.60285937702304]
  This paper proposes a novel spatial-spectral HSI classification method via multiple random anchor graphs ensemble learning (RAGE) Firstly, the local binary pattern is adopted to extract the more descriptive features on each selected band, which preserves local structures and subtle changes of a region. Secondly, the adaptive neighbors assignment is introduced in the construction of anchor graph, to reduce the computational complexity.
  arXiv  Detail & Related papers  (2021-03-25T09:31:41Z)
• Dual Geometric Graph Network (DG2N) -- Iterative network for deformable shape alignment [8.325327265120283]
  We provide a novel new approach for aligning geometric models using a dual graph structure where local features are mapping probabilities. We report state of the art results on stretchable domains alignment in a rapid and stable solution for meshes and cloud of points.
  arXiv  Detail & Related papers  (2020-11-30T12:03:28Z)
• DSG-Net: Learning Disentangled Structure and Geometry for 3D Shape Generation [98.96086261213578]
  We introduce DSG-Net, a deep neural network that learns a disentangled structured and geometric mesh representation for 3D shapes. This supports a range of novel shape generation applications with disentangled control, such as of structure (geometry) while keeping geometry (structure) unchanged. Our method not only supports controllable generation applications but also produces high-quality synthesized shapes, outperforming state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-08-12T17:06:51Z)
• Dense Non-Rigid Structure from Motion: A Manifold Viewpoint [162.88686222340962]
  Non-Rigid Structure-from-Motion (NRSfM) problem aims to recover 3D geometry of a deforming object from its 2D feature correspondences across multiple frames. We show that our approach significantly improves accuracy, scalability, and robustness against noise.
  arXiv  Detail & Related papers  (2020-06-15T09:15:54Z)
• Equivariant Maps for Hierarchical Structures [17.931059591895984]
  We show that symmetry of a hierarchical structure is the "wreath product" of symmetries of the building blocks. By voxelizing the point cloud, we impose a hierarchy of translation and permutation symmetries on the data. We report state-of-the-art on Semantic3D, S3DIS, and vKITTI, that include some of the largest real-world point-cloud benchmarks.
  arXiv  Detail & Related papers  (2020-06-05T18:42:12Z)
• ProAlignNet : Unsupervised Learning for Progressively Aligning Noisy Contours [12.791313859673187]
  "ProAlignNet" accounts for large scale misalignments and complex transformations between the contour shapes. It learns by training with a novel loss function which is derived an upperbound of a proximity-sensitive and local shape-dependent similarity metric. In two real-world applications, the proposed models consistently perform superior to state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-05-23T14:56:14Z)

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.