Bayesian 3D Modeling from Images Using Multiple Depth Maps

This paper addresses the problem of reconstructing the geometry and color of a Lambertian scene, given some fully calibrated images acquired with wide baselines. In order to completely model the input data, we propose to represent the scene as a set of colored depth maps, one per input image. We formulate the problem as a Bayesian MAP problem which leads to an energy minimization method. Hidden visibility variables are used to deal with occlusion, reflections and outliers. The main contributions of this work are: a prior for the visibility variables that treats the geometric occlusions; and a prior for the multiple depth maps model that smoothes and merges the depth maps while enabling discontinuities. Real world examples showing the efficiency and limitations of the approach are presented.

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: Pau Gargallo, Peter F. Sturm
• Keywords: Bayesian Map Problem, Computer Vision, CVPR 2005, Depth Maps, Energy Minimization Method, Multiple Depth Maps, Visibility Variables