<title>3D-3D registration of free-formed objects using shape and texture</title>

Abstract

Automatic generation of textured object models from a sequence of range and color images requires two major tasks: measurement registration and measurement integration. Measurement registration is the estimation of the current position and orientation of the object in 3D space with respect to an arbitrary fixed reference, given the current measurement and the 3D object model under construction. Measurement integration is the updating of the 3D object model using the current registered measurement. In this paper we present an iterative 3D-3D registration technique that uses both texture and shape information available in the 3D object models and the 3D measurements. The proposed technique handles probabilistic models that are potentially incomplete before the measurement integration step. Measurements are acquired via a sensor characterized by a probabilistic sensor model. The object models are constructed automatically without user interaction. Each model is a compact uniform tessellation of 3D space, where each cell of the tessellation represents shape and texture in a probabilistic fashion. Free formed objects are supported and no prior knowledge about the object shape, texture or pose is assumed. Traditional registration methods consider only shape and geometric information. We consider texture information as an additional evidence by defining a generalized intercell distance measure that considers both the relative positioning of cells in space and the texture discrepancy between cells. Experimental results demonstrate the efficiency and robustness of the proposed method. The usefulness of texture in registration is highlighted in a comparison with results obtained considering only geometric information.