Non-rigid 3D shape retrieval has become a research hotpot in communities of computer
graphics, computer vision, pattern recognition, etc. In this paper, we present the results of the …

We introduce a PDE-based node-to-element contact formulation as an alternative to
classical, purely geometrical formulations. It is challenging to devise solutions to nonsmooth …

Object skeleton is a useful geometric tool for shape analysis tasks. It encodes the topological
structure of the primitive shape and preserves a geometric cue as well. Skeletonization is a …

In the last few decades, significant advances in image matching are provided by rich local
descriptors that are defined through physical measurements such as reflectance. As such …

Shape complexity is a hard-to-quantify quality, mainly due to its relative nature. Biased by
Euclidean thinking, circles are commonly considered as the simplest. However, their …

Deep learning has recently emerged as one of the most popular and powerful paradigms for
learning tasks. In this paper, we present a deep learning approach to 3D shape …

In the past decade we have seen the emergence of many efficient algorithms for estimating
non-rigid deformations registering a template to target features. Registration of density …

Deep learning is a rapidly growing discipline that models high-level features in data as
multilayered neural networks. The recent trend toward deep neural networks has been …

Diffusion MRI (DMRI) provides us with the anatomy of the white matter fibers in the brain.
Extracting shape information is a necessary step to analyze the deformation in the white …

Local features are successfully used in 3D shape retrieval by encoding features descriptors
into global shape signatures. Previous 3D retrieval systems use different encoding methods …