US 8,015,125 B2Granted

Multi-scale segmentation and partial matching 3D models

AssigneeDrexel University

InventorsWilliam C. Regli, Ali Shokoufandeh, Dmitriy Bespalov

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Abstract

A scale-Space feature extraction technique is based on recursive decomposition of polyhedral surfaces into surface patches. The experimental results show that this technique can be used to perform matching based on local model structure. Scale-space techniques can be parameterized to generate decompositions that correspond to manufacturing, assembly or surface features relevant to mechanical design. One application of these techniques is to support matching and content-based retrieval of solid models. Scale-space technique can extract features that are invariant with respect to the global structure of the model as well as small perturbations that 3D laser scanning may introduce. A new distance function defined on triangles instead of points is introduced. This technique offers a new way to control the feature decomposition process, which results in extraction of features that are more meaningful from an engineering viewpoint. The technique is computationally practical for use in indexing large models.

Claims20

Dependent claims

A method of searching a database for a solid model comprising the steps of:

providing a query model;
determining a first set of values based on predetermined properties of said query model;
wherein said first set of values includes at least a first set of distances between points on triangular faces of said query model, said distances being determined by determining an angular shortest path between two triangular faces on said query model, wherein the angular shortest path on the query model is the shortest path on the surface of the training model which is computed in terms of an angular difference between the two triangular faces on the query model;
comparing said first set of values to a second set of values that is determined by predetermined properties for a group of models for training, said predetermined properties of said models for training including at least a second set of distances between points on triangular faces of the models for training, said second set of distances being determined by determining an angular shortest path between two triangular faces on said models for training, wherein the angular shortest path on the model for training is the shortest path on the surface of the model for training which is computed in terms of an angular difference between the two triangular faces on the model for training;
and
indexing, classifying and partially matching said query model with one or more models for training based on said comparing step.

3 more elements

Dependencies are inferred from the claim text.

Facts

Application: 11/847,942Filed 30 Aug 2007 · us provisional application
Priority: 31 Aug 2006
Published: US 2008/0215510 A14 Sep 2008
Granted: 6 Sep 2011
Term ends: Not estimatedThe source records don’t support an estimate.

Classification6 codes

G06F 16/2468
Special types of queries, e.g. statistical queries, fuzzy queries or distributed queries: fuzzy queries
G06N 20/00
Machine learning
G06T 19/00
Manipulating three-dimensional [3d] models or images for computer graphics
G06F
Computing and data processing