NHO: A Neural Hamiltonian Operator for Anchor-based Region Localization and Dense Correspondance

NHO introduces a neural Hamiltonian operator that uses sparse anchors and the intrinsic geometry of a partial shape to learn a localized eigenspace encoding both region support and intrinsic coordinates for dense correspondence. The method parameterizes the Hamiltonian potential as an intrinsic neural field and optimizes it with anchor evidence, spectral, and geometric constraints, employing reciprocal refinement between operator estimation and correspondence recovery. After iterative refinement, aggregated eigenfunction energy yields final localization and initializes dense correspondence refinement, achieving competitive accuracy and robustness to scaling and rotation.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.