Divergence-Free Shape Interpolation and Correspondence
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Abstract (arXiv)
We present a novel method to model and calculate deformation fields between shapes embedded in ℝD. Our framework combines naturally interpolating the two input shapes and calculating correspondences at the same time. The key idea is to compute a divergence-free deformation field represented in a coarse-to-fine basis using the Karhunen-Lo`eve expansion. The advantages are that there is no need to discretize the embedding space and the deformation is volume-preserving. Furthermore, the optimization is done on downsampled versions of the shapes but the morphing can be applied to any resolution without a heavy increase in complexity. We show results for shape correspondence, registration, inter- and extrapolation on the TOSCA and FAUST data sets.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 27 Jun 2018.
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Citing papers per year: 2022: 1; 2023: 1; 2024: 0; 2025: 3; 2026: 1.
6 papers.
- 2022Neural Conservation Laws: A Divergence-Free Perspectivecited by 70
- 2023Dynamic Point Fieldscited by 37
- 20254Deform: Neural Surface Deformation for Robust Shape Interpolationcited by 7
- 2025Spline Deformation Fieldcited by 3
- 2025Simulating Refractive Distortions and Weather-Induced Artifacts for Resource-Constrained Autonomous Perceptioncited by 1
- 2026On the Structural Failure of Chamfer Distance in 3D Shape Optimizationcited by 0
- 2026On the Structural Failure of Chamfer Distance in 3D Shape Optimization10 Mar 2026
- 2025Spline Deformation Field10 Jul 2025
- 2025Simulating Refractive Distortions and Weather-Induced Artifacts for Resource-Constrained Autonomous Perception7 Jul 2025
- 20254Deform: Neural Surface Deformation for Robust Shape Interpolation27 Feb 2025
- 2023Dynamic Point Fields5 Apr 2023
- 2022Neural Conservation Laws: A Divergence-Free Perspective4 Oct 2022