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Found 9 papers, 4 papers with code
Localized Shape Modelling with Global Coherence: An Inverse Spectral Approach
In this work, we address this problem by defining a data-driven model upon a family of linear operators (variants of the mesh Laplacian), whose spectra capture global and local geometric properties of the shape at hand.
High-Resolution Augmentation for Automatic Template-Based Matching of Human Models
This augmentation provides an effective workaround for the resolution limitations imposed by the adopted morphable model.
Infinite Feature Selection: A Graph-based Feature Filtering Approach
We propose a filtering feature selection framework that considers subsets of features as paths in a graph, where a node is a feature and an edge indicates pairwise (customizable) relations among features, dealing with relevance and redundancy principles.
Instant recovery of shape from spectrum via latent space connections
We introduce the first learning-based method for recovering shapes from Laplacian spectra.
Shape retrieval of non-rigid 3d human models
In addition, further participants have also taken part, and we provide extra analysis of the retrieval results.
FARM: Functional Automatic Registration Method for 3D Human Bodies
We introduce a new method for non-rigid registration of 3D human shapes.
Region-Based Correspondence Between 3D Shapes via Spatially Smooth Biclustering
This problem statement is similar to that of "biclustering", implying that RBC can be cast as a biclustering problem.
Infinite Latent Feature Selection: A Probabilistic Latent Graph-Based Ranking Approach
An appealing characteristic of the approach is that it aims to discover an abstraction behind low-level sensory data, that is, relevancy.
Free energy score space
Score functions induced by generative models extract fixed-dimension feature vectors from different-length data observations by subsuming the process of data generation, projecting them in highly informative spaces called score spaces.
