Support Vector Machines for 3D Shape Processing

We propose statistical learning methods for approximating implicit surfaces and computing dense 3D deformation fields. Our approach is based on Support Vector (SV) Machines, which are state of the art in machine learning. It is straightforward to implement and computationally competitive; its parameters can be automatically set using standard machine learning methods. The surface approximation is based on a modified Support Vector regression. We present applications to 3D head reconstruction, including automatic removal of outliers and hole filling. In a second step, we build on our SV representation to compute dense 3D deformation fields between two objects. The fields are computed using a generalized SV Machine enforcing correspondence between the previously learned implicit SV object representations, as well as correspondences between feature points if such points are available. We apply the method to the morphing of 3D heads and other objects. Categories and Subject Descriptors (a...

• Type: Journal
• Year: 2005
• Where: CGF
• Authors: Florian Steinke, Bernhard Schölkopf, Volker Blanz
• Keywords: CGF 2005, Deformation Fields, Dense 3D Deformation, Machine