In shape analysis and matching, it is often important to encode informa tion about the relation between a given point and other points on a shape, namely, its context. To this aim, we propose a theoretically sound and effi cient approach for the simulation of a discrete time evolution process that runs through all possible paths between pairs of points on a surface repre sented as a triangle mesh in the discrete setting.We demonstrate how this construction can be used to efficiently construct amultiscale point descrip tor, called the Discrete Time Evolution Process Descriptor, which robustly en codes the structure of neighborhoods of a point across multiple scales. Our work is similar in spirit to the methods based on diffusion geometry, and derived signatures such as the HKS or the WKS, but provides information that is complementary to these descriptors and can be computed without solving an eigenvalue problem.We demonstrate through extensive experi mental evaluation that our descriptor can be used to obtain accurate results in shape matching in different scenarios. Our approach outperforms simi lar methods and is especially robust in the presence of large nonisometric deformations, including missing parts.
Discrete time evolution process descriptor for shape analysis and matching
Melzi, S.;Roffo, G.;Cristani Marco;Castellani Umberto
2018-01-01
Abstract
In shape analysis and matching, it is often important to encode informa tion about the relation between a given point and other points on a shape, namely, its context. To this aim, we propose a theoretically sound and effi cient approach for the simulation of a discrete time evolution process that runs through all possible paths between pairs of points on a surface repre sented as a triangle mesh in the discrete setting.We demonstrate how this construction can be used to efficiently construct amultiscale point descrip tor, called the Discrete Time Evolution Process Descriptor, which robustly en codes the structure of neighborhoods of a point across multiple scales. Our work is similar in spirit to the methods based on diffusion geometry, and derived signatures such as the HKS or the WKS, but provides information that is complementary to these descriptors and can be computed without solving an eigenvalue problem.We demonstrate through extensive experi mental evaluation that our descriptor can be used to obtain accurate results in shape matching in different scenarios. Our approach outperforms simi lar methods and is especially robust in the presence of large nonisometric deformations, including missing parts.File | Dimensione | Formato | |
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