3D Shape Matching based on Geodesic Distance Distributions

A notable drawback of MDS-based canonical forms is their sensitivity to topological noise: different shape connectivity can affect dramatically the geodesic distances, resulting in

Elad and Kimmel [EK03] proposed computing a canoni- cal form of a mesh by mapping the geodesic distance be- tween all pairs of vertices to three-dimensional Euclidean distances.. As

The shape of a molecular strand is shown as a 3D visualisation floating above a 2D triangular matrix representing distance constraints, contact maps or other features of residue

Over-segmentation of the vertices on mesh can be generated by minimizing a new energy function defined by weighted geodesic distance which can be used for measuring the similarity

Perfect isometric mapping which preserves angles, areas, and the geodesic distance on the surface edges is usually not possible with the data dealt with in medical visualization

Based on the parametrization of the Bézier curves, the distribution of geometric features (e.g. stroke eccentricity) in the query sketch can be modeled and its SAR distance [SRG15]

Veltkamp (Editors).. DeepGM, to 3D shape retrieval. The proposed technique leverages recent developments in machine learning and geometry processing to effectively represent and

[Ezuz et al.. *) DEEP LEARNING 3D SHAPE SURFACES USING GEOMETRY IMAGES [SINHA ET AL.. *) GEODESIC CONVOLUTIONAL NEURAL NETWORKS ON RIEMANNIAN MANIFOLDS [MASCI ET AL.. *)