Computational Perception of Surface Material and 3D Spatial Layout

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A fundamental task in visual perception is to estimate the spatial layout of a scene, for example, the slope of the ground terrain on which the observer can move and the positions of any obstacles such an objects or walls. Standard computational models for solving this layout estimation problem have concentrated on scenes that have matte reflectance, but scenes with glossy reflecting surfaces have received relatively little attention. Important examples include navigating outdoor street scenes in the rain at night, which is challenging because such scenes are dominated by specular highlights from street lamps and car head lamps. Other scene examples with glossy surfaces include lake or ocean surfaces that are viewed from a boat or from a low flying aerial vehicle, or indoor scenes with polished floors, glossy painted or glass walls, or windows. This research will address how to interpret the specular highlights and mirror reflections that arise from large near-planar glossy surfaces. The approach will be highly interdisciplinary, addressing problem in robotic vision, human visual perception, and applied perception in computer graphics. A key goal in the applied perception problems in particular is to understand how well people perceive layout of glossy planar surfaces in virtual environments that are viewed using binocular, head-tracked, wide field of view displays such as the Oculus Rift. Such displays yield remarkable immersion effects, and it is likely that they will revolutionize how we experience real and virtual worlds. This research should provide insight into the strengths and limitations of these displays for conveying spatial layout and material information about glossy scenes.**