Automated Enhancement of 3D Models

Abstract

The acquisition of a 3D model of a real environment can be accomplished using range sensors. In practice, suitable sensors to densely cover a large environment are often impractical. This paper presents ongoing work on the synthesis of 3D environment models from as little as one intensity image and sparse range data. Our method is based on interpolating the available range data using statistical inferences learned from the available intensity image and from those (sparse) regions where both range and intensity information is available. Since we compute the relationship between extisting range data and the images we start with, we do not need to make any strong assumptions about the kind of surfaces in the world (for example we do not need to assume the world exhibits only diffuse reflectance). Experimental results show the feasibility of our method.