TY - JOUR
T1 - Joint weighted least squares for normal decomposition of 3D measurement surface
AU - Xie, Haoran
AU - Wang, Fu Lee
AU - Wang, Qiong
AU - Wei, Mingqiang
AU - Kwan, Reggie
AU - Qin, Jing
N1 - Publisher Copyright:
© 2020 IOP Publishing Ltd.
PY - 2020
Y1 - 2020
N2 - 3D optical and laser measurement devices can obtain the digital representation of physical objects by boundary surface meshes. Such a representation, however, has no semantic information to describe the object's basic shape and its geometric details individually. Meanwhile, existing mesh filters, which process surface normals as signals defined on the Gauss sphere, mainly deal with noise corrupted by measurement and computational errors. While useful in that they preserve geometric structures, they are not intended for filtering out geometric details whose scales are much larger than that of noise. We assume that a 3D surface contains three geometric properties, i.e. geometric detail, structural pattern, and smooth-varying shape, and consider normals as surface signals defined over both the input mesh and the underlying surface of this mesh. We propose a joint weighted least squares (JWLS) to solve the challenging problem of how to filter out the detailed appearance (geometric details) and preserve intrinsic geometric properties (structural patterns) of any measurement surface simultaneously. Specifically, we first suppress high-contrast detail normals, and then detect salient feature normals to produce a feature-guided normal field, and finally jointly fit the original shape. We have shown that a variety of geometric processing tasks benefit from our JWLS, e.g. detail-preserving bas-relief modeling, detail-free mesh smoothing, and detail-enhancing Laplacian coating.
AB - 3D optical and laser measurement devices can obtain the digital representation of physical objects by boundary surface meshes. Such a representation, however, has no semantic information to describe the object's basic shape and its geometric details individually. Meanwhile, existing mesh filters, which process surface normals as signals defined on the Gauss sphere, mainly deal with noise corrupted by measurement and computational errors. While useful in that they preserve geometric structures, they are not intended for filtering out geometric details whose scales are much larger than that of noise. We assume that a 3D surface contains three geometric properties, i.e. geometric detail, structural pattern, and smooth-varying shape, and consider normals as surface signals defined over both the input mesh and the underlying surface of this mesh. We propose a joint weighted least squares (JWLS) to solve the challenging problem of how to filter out the detailed appearance (geometric details) and preserve intrinsic geometric properties (structural patterns) of any measurement surface simultaneously. Specifically, we first suppress high-contrast detail normals, and then detect salient feature normals to produce a feature-guided normal field, and finally jointly fit the original shape. We have shown that a variety of geometric processing tasks benefit from our JWLS, e.g. detail-preserving bas-relief modeling, detail-free mesh smoothing, and detail-enhancing Laplacian coating.
KW - 3D measurement surface
KW - bas-relief modeling
KW - mesh smoothing
KW - surface geometry
UR - http://www.scopus.com/inward/record.url?scp=85081975896&partnerID=8YFLogxK
U2 - 10.1088/1361-6501/ab4e50
DO - 10.1088/1361-6501/ab4e50
M3 - Article
AN - SCOPUS:85081975896
SN - 0957-0233
VL - 31
JO - Measurement Science and Technology
JF - Measurement Science and Technology
IS - 4
M1 - 045401
ER -