A novel method for three-dimensional (3-D) shape retrieval using bag-of-feature techniques (BoF) is proposed. This method is based on vector quantization of invariant descriptors of 3-D object patches. Firstly, it starts by selecting and then describing a set of points from the 3-D object. Such descriptors have the advantage of being invariant to different transformations that a shape can undergo. Based on vector quantization, we cluster those descriptors to form a shape vocabulary. Then, each point selected in the object is associated to a cluster (word) in that vocabulary. Finally, a weighted vector counting the occurrences of every word is computed. These results clearly demonstrate that the method is robust to nonrigid and deformable shapes, in which the class of transformations may be very wide due to the capability of such shapes to bend and assume different forms.
3D-model analysis plays an important role in numerous applications. In this paper, we present an approach for
Reeb graph extraction using a novel mapping function. Our mapping function computes a real value for each
vertex which provides interesting insights to describe topology structure of the 3D-model. We perform discrete
contour for each vertex according to our mapping function. Topology changes can be detected by discrete
contours analysis to construct the Reeb graph.
Our mapping function has some important properties. It is invariant to rigid and non rigid transformations,
it is insensitive to noise, it is robust to small topology changes, and it does not depend on parameters. From the
extracted graph, these properties show the significant parts of a 3D-model. We retain the evaluation criteria to
the properties of the mapping function, and compared them to those used in the state of the art. In the end, we
present extracted Reeb graph on various models with different positions.
We present a novel method for 3D-shape matching using Bag-of-Feature techniques (BoF). The method starts
by selecting and then describing a set of points from the 3D-object. Such descriptors have the advantage of
being invariant to different transformations that a shape can undergo. Based on vector quantization, we cluster
those descriptors to form a shape vocabulary. Then, each point selected in the object is associated to a cluster
(word) in that vocabulary. Finally, a BoF histogram counting the occurrences of every word is computed. These
results clearly demonstrate that the method is robust to non-rigid and deformable shapes, in which the class of
transformations may be very wide due to the capability of such shapes to bend and assume different forms.
KEYWORDS: 3D modeling, Databases, Data modeling, Systems modeling, Performance modeling, Visualization, Genetic algorithms, Internet, 3D scanning, Data processing
In this paper we present a three-dimensional model retrieval system. A three-dimensional model is described by two invariant descriptors : a shape index and a histogram of distances between meshes. This work focuses on extracting invariant descriptors that well represent a three-dimensional model, and on combining theses descriptors in order to get a better retrieval performance. An experimental evaluation demonstrates the good performance of the approach.
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