Title:  Maximum Likelihood Surface Estimation

A confluence of technologies in 3D sensing, high-performance
computing, and interactive graphics is providing new opportunities for
generating accurate, 3D models of complex scenes or objects.  However,
as the variety of applications for this capabilities grows, so do the
demands on the quality of the models.  In some applications,
especially those that place time and space restrictions on the data
acquisition, the requirements for model fidelity can exceed the raw
capabilities of the sensor.  These developments suggest the need for
new methods of processing surface data that will combine noisy
measurements in a robust and efficient way while making the best use
of all of the available information.  This talk describes such a
framework.  The proposed framework relies on a combination of computer
science with estimation theory, differential geometry, and numerical
computing.