Range Synthesis for 3D Environment Modeling

Abstract

In this paper a range synthesis algorithm is proposed as an initial solution
to the problem of 3D  environment modeling from sparse data. 
We develop a statistical learning method for inferring and extrapolating
range data from as little as one intensity image and from those (sparse) 
regions where both range and intensity information is available. Our work is 
related to methods for texture synthesis using Markov Random Field methods. 
We demonstrate that MRF methods can also be applied to general intensity 
images with little associated range information and used to estimate range  
values where needed without making any strong assumptions about the kind of 
surfaces in the world. Experimental results show the feasibility of our 
method.