Social and Behavioral Sciences Commons^™

Bayesian Modeling Of 3d Shape Inference From Line Drawings, Seha Kim, Jacob Feldman, Manish Singh

MODVIS Workshop

Human depth comparisons in line drawings reflect the underlying uncertainty of perceived 3D shape. We propose a Bayesian model that estimates the 3D shape from line drawings based on the local and non-local contour cues. This model estimates the posterior distribution over depth differences at two points on a line drawing. The likelihood is numerically computed by assuming a generative model, which generates random 3D surfaces and, via projection, random line drawings. The 3D surfaces are inflated from random skeletons and projected into line drawings. Given a novel line drawing, the model samples probable local surfaces based on the relations …