Non-rigid 3D Faces Registration using Geodesic Distance Maps

The matching score is based on the residual distance between two fragments after their rigid registration, which is performed using a three-level coarse-to-fine search strategy that

We decided to use plush toys to test the robustness of shape retrieval methods against articulated and local deformations, as they can be easily posed in different ways, present a

For the competition, four teams submitted results which were evaluated using commonly used measures for retrieval systems, as well as the time required to process the dataset

In the 2015 SHREC track on Scalability of 3D Shape Retrieval we provide a benchmark with more than 96 thousand shapes.. The data set is based on a non-rigid retrieval benchmark

Based on the analyses in [LGX13], existing non-rigid 3D shape retrieval methods can be roughly classified into al- gorithms employing local features, topological

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

A novel method using non-rigid image registration was reported which is obtained through two steps: (i) au- tomated rigid or affine registration, and (ii) a non-rigid in- tensity

1) A new 3D mesh unfolding method is proposed without calculat- ing geodesic distances; 2) We formulate the unfolding problem as a form of semidefinite programming and deduce