A Bayesian Approach to High Resolution 3D Surface Reconstruction from Multiple Images

We present a radically different approach to the recovery of the three dimensional geometric and reflectance properties of a surface from image data. We pose the problem in a Bayesian framework, and proceed to infer the parameters of the model describing the surface. This allows great flexibility in the specification of the model, in terms of how both the geometrical properties and surface reflectance are specified. In the usual manner for Bayesian approaches it requires that we can simulate the data that would have been recorded for any state of the model in order to infer the model. The theoretical aspects are thus very general. We present results for one type of surface geometry (the triangular mesh) and for the Lambertian model of light scattering. Our framework also allows the easy incorporation of data from multiple sensing modalities.