Rapid prototyping 3D objects from scanned measurement data

It has become increasingly important to be able to generate free-form 3D shapes in commercial applications using rapid prototyping technologies. In many cases, the shapes of interest are taken from real-world objects that do not have pre-existing computer models. Constructing an accurate model for these objects by hand is extremely time consuming and difficult with even the latest 3D software packages. To aid in the modeling process, 3D scanners are used to capture the object shape and generate a high resolution model of the object. However, these models built from scans often have irregularities that prevent the construction of a useful prototype. This paper proposes a method for generating 3D models suitable for rapid prototyping from measurements of real-world objects taken by a 3D scanner. This is accomplished by taking a cloud of 3D point data as input and fitting a closed 3D surface to the data in such a way as to ensure accuracy in the representation of the object surface and compatibility with a rapid prototyping machine. We treat surface modeling and adaptation to the data in a new framework as 3D stochastic surface estimation.