Deformable voxel grids for shape comparisons

Raphaël Groscot; Laurent D. Cohen

doi:10.1117/12.2645961

12 October 2022 Deformable voxel grids for shape comparisons

Raphaël Groscot, Laurent D. Cohen

Proceedings Volume 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022); 123423G (2022) https://doi.org/10.1117/12.2645961
Event: Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 2022, Wuhan, China

Abstract

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.

Citation Download Citation

Raphaël Groscot and Laurent D. Cohen "Deformable voxel grids for shape comparisons", Proc. SPIE 12342, Fourteenth International Conference on Digital Image Processing (ICDIP 2022), 123423G (12 October 2022); https://doi.org/10.1117/12.2645961

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KEYWORDS

Principal component analysis

3D modeling

Neural networks

Shape analysis

Image segmentation

Statistical analysis

Visualization

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