||No required textbook, though the following book may be useful for many parts of the course:
Biasotti, S.; Falcidieno, B.; Giorgi, D.; Spagnuolo, M., Mathematical Tools for Shape Analysis and Description, Morgan & Claypool, 2014, doi: 10.2200/S00588ED1V01Y201407CGR016
Each lecture will have handouts, notes and references for the specific material. Discussion of research topics will refer to research papers as background reading
||3D shape representations: meshes, implicit surfaces, volumetric representations
Basic geometric analysis: dimensions, geodesics, curvature, moments etc
Advanced geometric analysis: discrete differential geometry, medial axis transform, spectral decomposition
Geometric features (local and global): distance histograms, spherical harmonics, light field descriptors, heat kernel signatures
Shape search and classification: nearest-neighbor lookup, statistical classifiers
Shape segmentation: unsupervised (single and multi-shape), supervised
Shape correspondences: sparse and dense
Shape parametrizations (local and global): minimizing various metric distortions
Statistical models of shape structure: templates, grammars, part-based graphical models
Shape manipulation: editing and deformations
Research topics and trends: review of current state of the art and area trends – models of semantics, function, aesthetics, manufacturability, interaction etc. Reading and discussion of significant selected papers. Applications to design, acquisition, vision and robotics.
Small programming assignments for selected subset of above topics, plus final project (ideally a small research project, but can also be a development-based one).