Automatic 3D model reconstruction based on novel pose estimation and integration techniques

Abstract

An automatic three-dimensional (3D) model reconstruction technique is presented to acquire complete and closed 3D models of real objects. The technique is based on novel approaches to pose estimation and integration. Two different poses of an object are used because a single pose often hides some surfaces from a range sensor. A second pose is used to expose such surfaces to the sensor. Two partial 3D models are reconstructed for two different poses of the object using a multi-view 3D modeling technique. The two 3D models are then registered in two steps—coarse registration, and its refinement. Coarse registration is facilitated by a novel pose estimation technique, which estimates a rigid transformation between two models. The pose is estimated by matching a stable tangent plane (STP) of each pose-model with the base tangent plane, which is invariant for a vision system. We employ geometric constraints to find the STP. After registration refinement, two models are integrated to a complete 3D model based on voxel classification defined in multi-view integration. Texture mapping is done to obtain a photo-realistic reconstruction of the object. Reconstruction results and error analysis are presented for several real objects.