Contextual Part Analogies in 3D Objects

Lior Shapira, Shy Shalom, Ariel Shamir, Daniel Cohen-Or, and Richard Hao Zhang


In this paper we address the problem of finding analogies between parts of 3D

objects. By partitioning an object into meaningful parts and finding analogous

parts in other objects, not necessarily of the same type, many analysis and

modeling tasks could be enhanced. For instance, partial match queries can be

formulated, annotation of parts in objects can be utilized, and

modeling-by-parts applications could be supported. We define a similarity

measure between two parts based not only on their local signatures and

geometry, but also on their context within the shape to which they belong.

In our approach, all objects are hierarchically segmented (e.g. using the shape

diameter function), and each part is given a local signature. However, to find

corresponding parts in other objects we use a context enhanced part-in-whole

matching. Our matching function is based on bi-partite graph matching and is

computed using a flow algorithm which takes into account both local geometrical

features and the partitioning hierarchy. We present results on finding part

analogies among numerous objects from shape repositories, and demonstrate

sub-part queries using an implementation of a simple search and retrieval

application. We also demonstrate a simple annotation tool that carries textual

tags of object parts from one model to many others using analogies, laying a

basis for semantic text based search.


Publication typeArticle
Published inInternational Journal of Computer Vision
AddressSecaucus, NJ, USA
PublisherSpringer-Verlag New York, Inc.
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