Purchase this article with an account.
Julia Mazzarella, Steven Cholewiak, Flip Phillips, Roland Fleming; Limits on the estimation of shape from specular surfaces . Journal of Vision 2014;14(10):721. doi: https://doi.org/10.1167/14.10.721.
Download citation file:
© ARVO (1962-2015); The Authors (2016-present)
Humans are generally remarkably good at inferring 3D shape from distorted patterns of reflections on mirror-like objects (Fleming et al, 2004). However, there are conditions in which shape perception fails (complex planar reliefs under certain illuminations; Faisman and Langer, 2013). A good theory of shape perception should predict failures as well as successes of shape perception, so here we sought to map out systematically the conditions under which subjects fail to estimate shape from specular reflections and to understand why. To do this, we parametrically varied the spatial complexity (spatial frequency content) of both 3D relief and illumination, and measured under which conditions subjects could and could not infer shape. Specifically, we simulated surface reliefs with varying spatial frequency content and rendered them as perfect mirrors under spherical harmonic light probes with varying frequency content. Participants viewed the mirror-like surfaces and performed a depth-discrimination task. On each trial, the participants task was to indicate which of two locations on the surfaceselected randomly from a range of relative depth differences on the objects surfacewas higher in depth. We mapped out performance as a function of both the relief and the lighting parameters. Results show that while participants were accurate within a given range for each manipulation, there also existed a range of spatial frequencies namely very high and low frequencies where participants could not estimate surface shape. Congruent with previous research, people were able to readily determine 3D shape using the information provided by specular reflections; however, performance was highly dependent upon surface and environment complexities. Image analysis reveals the specific conditions that subjects rely on to perform the task, explaining the pattern of errors.
Meeting abstract presented at VSS 2014
This PDF is available to Subscribers Only