Fast Joint Estimation of Silhouettes and Dense 3D Geometry from Multiple Images

—We propose a probabilistic formulation of joint silhouette extraction and 3D reconstruction given a series of calibrated 2D images. Instead of segmenting each image separately in order to construct a 3D surface consistent with the estimated silhouettes, we compute the most probable 3D shape that gives rise to the observed color information. The probabilistic framework, based on Bayesian inference, enables robust 3D reconstruction by optimally taking into account the contribution of all views. We solve the arising maximum a posteriori shape inference in a globally optimal manner by convex relaxation techniques in a spatially continuous representation. For an interactively provided user input in the form of scribbles specifying foreground and background regions, we build corresponding color distributions as multivariate Gaussians and find a volume occupancy that best fits to this data in a variational sense. Compared to classical methods for silhouette-based multiview reconstruction, ...

• Type: Journal
• Year: 2012
• Where: PAMI
• Authors: Kalin Kolev, Thomas Brox, Daniel Cremers
• Keywords: Background Clutter, Color Distributions, PAMI 2012, Software Engineering, Volume Grids