mlsanityData to 14 Oct 2026

VariGrad: A Novel Feature Vector Architecture for Geometric Deep Learning on Unregistered Data

arXiv 2307.03553 ↗First posted 7 Jul 2023cs.CVcited by 4

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Abstract (arXiv)

We present a novel geometric deep learning layer that leverages the varifold gradient (VariGrad) to compute feature vector representations of 3D geometric data. These feature vectors can be used in a variety of downstream learning tasks such as classification, registration, and shape reconstruction. Our model's use of parameterization independent varifold representations of geometric data allows our model to be both trained and tested on data independent of the given sampling or parameterization. We demonstrate the efficiency, generalizability, and robustness to resampling demonstrated by the proposed VariGrad layer.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 18 of its 31 references that are in the map. First posted on arXiv 7 Jul 2023.

Builds on

18 of 31 references.

References · 18

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
  3. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,223
  4. 2016Geometric deep learning: going beyond Euclidean datacited by 1,048
  5. 2017A Neural Representation of Sketch Drawingscited by 313
  6. 2018Generating 3D faces using Convolutional Mesh Autoencoderscited by 268
  7. 2020DiffusionNet: Discretization Agnostic Learning on Surfacescited by 164
  8. 2019Multi-Graph Transformer for Free-Hand Sketch Recognitioncited by 35
  9. 2013The varifold representation of non-oriented shapes for diffeomorphic registrationcited by 24
  10. 2022Elastic shape analysis of surfaces with second-order Sobolev metrics: a comprehensive numerical frameworkcited by 19
  11. 2021Field Convolutions for Surface CNNscited by 14
  12. 2022SHREC'22 Track: Sketch-Based 3D Shape Retrieval in the Wildcited by 9
6 more
  1. 2023Toward Mesh-Invariant 3D Generative Deep Learning with Geometric Measurescited by 6
  2. 2021A new variational model for shape graph registration with partial matching constraintscited by 5
  3. 2021Supervised deep learning of elastic SRV distances on the shape space of curvescited by 4
  4. 2016Reconstruction of convex bodies from momentscited by 3
  5. 2020An inexact matching approach for the comparison of plane curves with general elastic metricscited by 2
  6. 2022SketchCleanNet -- A deep learning approach to the enhancement and correction of query sketches for a 3D CAD model retrieval systemcited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2024: 2; 2025: 1; 2026: 1.

4 papers.

  1. 2024Self Supervised Networks for Learning Latent Space Representations of Human Body Scans and Motionscited by 3
  2. 2024Explainable Binary Classification of Separable Shape Ensemblescited by 1
  3. 2025SVarM: Linear Support Varifold Machines for Classification and Regression on Geometric Datacited by 1
  4. 2026Feature-based morphological analysis of shape graph datacited by 0