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Abstract A Fast Algorithm for ICP-Based 3D Shape

A Fast Algorithm for ICP-Based3D Shape

Biometrics

Ping Yan,Kevin W.Bowyer

Department of Computer Science and Engineering,

University of Notre Dame,

Notre Dame,IN46556

1Introduction

Since its introduction by Chen and Medioni[1]and Besl and McKay[2],the Iter-ative Closest Point(ICP)algorithm has been widely used for3D shape matching [1,3–5].It has been used in a wide range of application areas,including the integra-tion of range images[6,7]and alignment of CT and MR images[8].Here,we are speci?cally interested in3D shape matching for biometics[9–13].The ICP algo-rithm is known to be computationally expensive.With two clouds of points,source S(probe)and target T(gallery),th
e complexity of a typical single ICP iteration is O(N S log(N T))using a K-D tree data structure[9]in the expected case,where N S is the number of points in the source and N T is the number of points in the target.The ICP algorithm iteratively?nds the minimum distance between two sur-faces.With N I iterations,the overall complexity is O(N I×N S×log(N T))[2].

Since its introduction by Chen and Medioni[1]and Besl and McKay[2],the Iter-ative Closest Point(ICP)algorithm has been widely used for3D shape matching [1,3–5].It has been used in a wide range of application areas,including the integra-tion of range images[6,7]and alignment of CT and MR images[8].Here,we are speci?cally interested in3D shape matching for biometics[9–13].The ICP algo-rithm is known to be computationally expensive.With two clouds of points,source S(probe)and target T(gallery),the complexity of a typical single ICP iteration is O(N S log(N T))using a K-D tree data structure[9]in the expected case,where N S is the number of points in the source and N T is the number of points in the target.The ICP algorithm iteratively?nds the minimum distance between two sur-faces.With N I iterations,the overall complexity is O(N I×N S×log(N T))[2].

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