Inferring Shape and Materials under Real-World Lighting
Harvard School of Engineering and Applied Sciences
system is tasked with inferring the observable properties of a
scene---shape, materials, and so on---from one or more of its images. The
task is made hard by the fact that the mapping from scene properties to
images is many-to-one: For any given image, there are infinite
scenes to explain it.
approach for dealing with this ambiguity is designing systems that combine
prior visual experience with loose, redundant constraints induced by
texture, shading, and various other aspects of optical stimulation.
The basic idea is that each cue reduces the set of interpretations in some way, and by combining them, systems will be better
equipped to sift through the infinite set of possibilities andarrive at a
with this approach, we need to understand the various ways that shape and
materials are encoded in image data, and in this talk I will describe
two that remain poorly understood. Each of these exists in the
presence of complex "real-world" lighting, and for each I will summarize our
recent progress in: 1) characterizing the constraints induced on a
scene, and 2) creating algorithms for inference.