Deformable Voxel Grids for Shape Comparisons
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Abstract (arXiv)
We present Deformable Voxel Grids (DVGs) for 3D shapes comparison and processing. It consists of a voxel grid which is deformed to approximate the silhouette of a shape, via energy-minimization. By interpreting the DVG as a local coordinates system, it provides a better embedding space than a regular voxel grid, since it is adapted to the geometry of the shape. It also allows to deform the shape by moving the control points of the DVG, in a similar manner to the Free Form Deformation, but with easier interpretability of the control points positions. After proposing a computation scheme of the energies compatible with meshes and pointclouds, we demonstrate the use of DVGs in a variety of applications: correspondences via cubification, style transfer, shape retrieval and PCA deformations. The first two require no learning and can be readily run on any shapes in a matter of minutes on modest hardware. As for the last two, they require to first optimize DVGs on a collection of shapes, which amounts to a pre-processing step. Then, determining PCA coordinates is straightforward and brings a few parameters to deform a shape.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 9 of its 26 references that are in the map. First posted on arXiv 21 Nov 2022.
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References · 9
- 2022Generative Adversarial Networkscited by 5,627
- 2016PointNet: Deep Learning on Point Sets for 3D Classification and Segmentationcited by 5,248
- 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,401
- 2013Auto-Encoding Variational Bayescited by 4,053
- 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,897
- 2016A Point Set Generation Network for 3D Object Reconstruction from a Single Imagecited by 979
- 2019Composite Shape Modeling via Latent Space Factorizationcited by 31
- 2017DeformNet: Free-Form Deformation Network for 3D Shape Reconstruction from a Single Imagecited by 25
- 2018ALIGNet: Partial-Shape Agnostic Alignment via Unsupervised Learningcited by 11
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