TY - GEN
T1 - 3D laplacian pyramid signature
AU - Hu, Kaimo
AU - Fang, Yi
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - We introduce a simple and effective point descriptor, called 3D Laplacian Pyramid Signature (3DLPS), by extending and adapting the Laplacian Pyramid defined in 2D images to 3D shapes. The signature is represented as a high-dimensional feature vector recording the magnitudes of mean curvatures, which are captured through sequentially applying Laplacian of Gaussian (LOG) operators on each vertex of 3D shapes. We show that 3DLPS organizes the intrinsic geometry information concisely, while possessing high sensitivity and specificity. Compared with existing point signatures, 3DLPS is robust and easy to compute, yet captures enough information embedded in the shape. We describe how 3DLPS may potentially benefit the applications involved in shape analysis, and especially demonstrate how to incorporate it in point correspondence detection, best view selection and automatic mesh segmentation. Experiments across a collection of shapes have verified its effectiveness.
AB - We introduce a simple and effective point descriptor, called 3D Laplacian Pyramid Signature (3DLPS), by extending and adapting the Laplacian Pyramid defined in 2D images to 3D shapes. The signature is represented as a high-dimensional feature vector recording the magnitudes of mean curvatures, which are captured through sequentially applying Laplacian of Gaussian (LOG) operators on each vertex of 3D shapes. We show that 3DLPS organizes the intrinsic geometry information concisely, while possessing high sensitivity and specificity. Compared with existing point signatures, 3DLPS is robust and easy to compute, yet captures enough information embedded in the shape. We describe how 3DLPS may potentially benefit the applications involved in shape analysis, and especially demonstrate how to incorporate it in point correspondence detection, best view selection and automatic mesh segmentation. Experiments across a collection of shapes have verified its effectiveness.
UR - http://www.scopus.com/inward/record.url?scp=84942515649&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84942515649&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-16634-6_23
DO - 10.1007/978-3-319-16634-6_23
M3 - Conference contribution
AN - SCOPUS:84942515649
SN - 9783319166339
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 306
EP - 321
BT - Computer Vision - 12th Asian Conference on Computer Vision, ACCV 2014, Revised Selected Papers
A2 - Jawahar, C.V.
A2 - Shan, Shiguang
PB - Springer Verlag
T2 - 12th Asian Conference on Computer Vision, ACCV 2014
Y2 - 1 November 2014 through 5 November 2014
ER -