Hamiltonian operator for spectral shape analysis
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Abstract (arXiv)
Many shape analysis methods treat the geometry of an object as a metric space that can be captured by the Laplace-Beltrami operator. In this paper, we propose to adapt the classical Hamiltonian operator from quantum mechanics to the field of shape analysis. To this end we study the addition of a potential function to the Laplacian as a generator for dual spaces in which shape processing is performed. We present a general optimization approach for solving variational problems involving the basis defined by the Hamiltonian using perturbation theory for its eigenvectors. The suggested operator is shown to produce better functional spaces to operate with, as demonstrated on different shape analysis tasks.
Key facts. Cited by 10 papers in the map (data through 14 Oct 2026). First posted on arXiv 7 Nov 2016.
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Citing papers per year: 2021: 3; 2022: 2; 2023: 0; 2024: 4; 2025: 0; 2026: 1.
10 papers.
- 2024SRIF: Semantic Shape Registration Empowered by Diffusion-based Image Morphing and Flow Estimationcited by 12
- 2022Non-Isometric Shape Matching via Functional Maps on Landmark-Adapted Basescited by 8
- 2021Disentangling Geometric Deformation Spaces in Generative Latent Shape Modelscited by 2
- 2024Hearing the shape of an arena with spectral swarm roboticscited by 2
- 2024DifAttack++: Query-Efficient Black-Box Adversarial Attack via Hierarchical Disentangled Feature Space in Cross-Domaincited by 2
- 2021Smoothness and effective regularizations in learned embeddings for shape matchingcited by 1
- 2022Partial Shape Similarity via Alignment of Multi-Metric Hamiltonian Spectracited by 1
- 2024LBONet: Supervised Spectral Descriptors for Shape Analysiscited by 1
- 2021Localized Shape Modelling with Global Coherence: An Inverse Spectral Approachcited by 0
- 2026NHO: A Neural Hamiltonian Operator for Anchor-based Region Localization and Dense Correspondencecited by 0
- 2026NHO: A Neural Hamiltonian Operator for Anchor-based Region Localization and Dense Correspondence29 Sep 2026
- 2024LBONet: Supervised Spectral Descriptors for Shape Analysis13 Nov 2024
- 2024SRIF: Semantic Shape Registration Empowered by Diffusion-based Image Morphing and Flow Estimation18 Sep 2024
- 2024DifAttack++: Query-Efficient Black-Box Adversarial Attack via Hierarchical Disentangled Feature Space in Cross-Domain5 Jun 2024
- 2024Hearing the shape of an arena with spectral swarm robotics25 Mar 2024
- 2022Partial Shape Similarity via Alignment of Multi-Metric Hamiltonian Spectra7 Jul 2022
- 2022Non-Isometric Shape Matching via Functional Maps on Landmark-Adapted Bases10 May 2022
- 2021Smoothness and effective regularizations in learned embeddings for shape matching14 Dec 2021
- 2021Localized Shape Modelling with Global Coherence: An Inverse Spectral Approach4 Aug 2021
- 2021Disentangling Geometric Deformation Spaces in Generative Latent Shape Models27 Feb 2021