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We propose a method for 3D object reconstruction and 6D-pose estimation from2D images that uses knowledge about object shape as the primary key. In theproposed pipeline, recognition and labeling of objects in 2D images deliver 2Dsegment silhouettes that are compared with the 2D silhouettes of projectionsobtained from various views of a 3D model representing the recognized objectclass. By computing transformation parameters directly from the 2D images, thenumber of free parameters required during the registration process is reduced,making the approach feasible. Furthermore, 3D transformations and projectivegeometry are employed to arrive at a full 3D reconstruction of the object incamera space using a calibrated set up. Inclusion of a second camera allowsresolving remaining ambiguities. The method is quantitatively evaluated usingsynthetic data and tested with real data, and additional results for thewell-known Linemod data set are shown. In robot experiments, successfulgrasping of objects demonstrates its usability in real-world environments, and,where possible, a comparison with other methods is provided. The method isapplicable to scenarios where 3D object models, e.g., CAD-models or pointclouds, are available and precise pixel-wise segmentation maps of 2D images canbe obtained. Different from other methods, the method does not use 3D depth fortraining, widening the domain of application.

3D object reconstruction and 6D-pose estimation from 2D shape for robotic grasping of objects