3D Surface Reconstruction Using a Generalized Distance Function


August 25, 2011


Daniel Keren


Department of Computer Science University of Haifa


I will define a new distance function on an unoriented 3D point set and describe how it may be used to reconstruct a surface approximating these points. This distance function is shown to be a Mahalanobis distance in a higher-dimensional embedding space of the points, and the resulting reconstruction algorithm is a natural extension of the classical Radial Basis Function (RBF) approach. Experimental results show the superiority of our reconstruction algorithm to RBF and other methods in a variety of practical scenarios. I will also relate the distance function to a more general notion of indicator functions and discuss possible applications to learning and classification.

Joint work with Craig Gotsman and Roi Poranne, Technion.


Daniel Keren

Daniel Keren received his Ph.D at the Hebrew University in Jerusalem in 1991,
and has been a post-doctoral fellow at Brown University until 1994. Since then, he is
with the Department of Computer Science in Haifa University, Israel. He also worked
as a consultant for HP during 1995-98, and was a visitor in the Max Planck Institute for
Plasma Physics in the summer of 1997.

His interests are Bayesian analysis, image detection, and model fitting.