[PAST EVENT] Kathleen D. Moore, Computer Science Ph.D.

May 10, 2016
10am - 11:30am
Sadler Center, James Room
200 Stadium Dr
Williamsburg, VA 23185Map this location

The appearance of an object is the result of complex light interaction with the object. Beyond the basic interplay between incident light and the object's material, a multitude of physical events occur between this illumination and the microgeometry at the point of incidence, and also beneath the surface. A given object, made as smooth and opaque as possible, will have a completely different appearance if either one of these attributes - amount of surface mesostructure or translucency - is altered. Indeed, while they are not always readily perceptible, the small-scale features of an object are as important to its appearance as its material properties. Moreover, surface mesostructure and translucency are inextricably linked in an overall effect on appearance.

In this dissertation, we present several studies examining the importance of surface mesostructure (small-scale surface orientation) and translucency on an object's appearance.

First, we present an empirical study that establishes how poorly a mesostructure estimation technique can perform when translucent objects are used as input. We investigate the two major factors in determining an object's translucency: mean free path and scattering albedo. We exhaustively vary the settings of these parameters within realistic bounds, examining the subsequent blurring effect on the output of a common shape estimation technique, photometric stereo.
Based on our findings, we identify a dramatic effect that the input of a translucent material has on the quality of the resultant estimated mesostructure.

In the next project, we discuss an optimization technique for both refining estimated surface orientation of translucent objects and determining the reflectance characteristics of the underlying material.
For a globally planar object, we use simulation and real measurements to show that the blurring effect on normals that was observed in the previous study can be recovered. The key to this is the observation that the normalization factor for recovered normals is proportional to the size of the blur kernel.

Finally, we frame the study of the impact of surface normals in a practical, image-based context. We discuss our low-overhead, editing tool for natural images that enables the user to edit surface mesostructure while the system automatically updates the appearance in the natural image. Because a single photograph captures an instant of the incredibly complex interaction of light and an object, there is a wealth of information to extract from a photograph. Given a photograph of an object in natural lighting, we allow mesostructure edits and infer any holes in a realistically plausible way.

Short Bio:
Kathleen Moore is a Ph.D. candidate in the Department of Computer Science at William & Mary. In 2011, she attained an M.S. in Computer Science from W&M, and in 2009, a B.A. in Computer Science from the College of the Holy Cross in Worcester, MA.