Paper Info
Title
A Bayesian Approach To Perceptual 3d Object-Part Decomposition Using Skeleton-Based Representations
Abstract
We present a probabilistic approach to shape decomposition that creates a skeleton-based shape representation of a 3D object while simultaneously decomposing it into constituent parts. Our approach probabilistically combines two prominent threads from the shape literature: skeleton-based (medial axis) representations of shape, and part-based representations of shape, in which shapes are combinations of primitive parts. Our approach recasts skeleton-based shape representation as a mixture estimation problem, allowing us to apply probabilistic estimation techniques to the problem of 3D shape decomposition, extending earlier work on the 2D case. The estimated 3D shape decompositions approximate human shape decomposition judgments. We present a tractable implementation of the framework, which begins by over-segmenting objects at concavities, and then probabilistically merges them to create a distribution over possible decompositions. This results in a hierarchy of decompositions at different structural scales, again closely matching known properties of human shape representation. The probabilistic estimation procedures that arise naturally in the model allow effective prediction of missing parts. We present results on shapes from a standard database illustrating the effectiveness of the approach.
Year
Venue
Field
2015
PROCEEDINGS OF THE TWENTY-NINTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE
Active shape model,Computer science,Medial axis,Topological skeleton,Thread (computing),Artificial intelligence,Probabilistic logic,Hierarchy,Machine learning,Bayesian probability,Shape analysis (digital geometry)
DocType
Citations 
PageRank 
Conference
2
0.36
References 
Authors
11
5
Name
Order
Citations
PageRank
Tarek El-Gaaly1394.98
Froyen, Vicky240.74
Ahmed Elgammal32553168.71
Jacob Feldman491.85
Manish K. Singh571.86