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Sponsored by the Center for Science and Technology Development of the Ministry of Education
Supervised by Ministry of Education of the People's Republic of China
We describe a direct way of making use of surface normal vectors at sample points in the problem of implicit surface fitting with compactly supported radial basis functions. The normal vectors are incorporated in a regularized regression problem that leads to a n by n positive definite linear system given n surface point/normal pairs. Compared with the widely used heuristic methods, our method avoids of introducing manufactured off-surface points and can fit much larger datasets effectively. We demonstrate its robust performance on several datasets.