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Found 6 papers, 2 papers with code
Make-A-Shape: a Ten-Million-scale 3D Shape Model
We then make the representation generatable by a diffusion model by devising the subband coefficients packing scheme to layout the representation in a low-resolution grid.
Sketch-A-Shape: Zero-Shot Sketch-to-3D Shape Generation
Significant progress has recently been made in creative applications of large pre-trained models for downstream tasks in 3D vision, such as text-to-shape generation.
Spectral Maps for Learning on Subgraphs
We demonstrate the benefits of incorporating spectral maps in graph learning pipelines, addressing scenarios where a node-to-node map is not well defined, or in the absence of exact isomorphism.
Universal Spectral Adversarial Attacks for Deformable Shapes
Our attacks are universal, in that they transfer across different shapes, different representations (meshes and point clouds), and generalize to previously unseen data.
Instant recovery of shape from spectrum via latent space connections
We introduce the first learning-based method for recovering shapes from Laplacian spectra.
Isospectralization, or how to hear shape, style, and correspondence
The question whether one can recover the shape of a geometric object from its Laplacian spectrum ('hear the shape of the drum') is a classical problem in spectral geometry with a broad range of implications and applications.
