Knowledge-based 3D Object Reconstruction and 6D-pose Estimation from 2D Images
3D object reconstruction and 6D-pose estimation from 2D shape for robotic grasping of objects
We propose a method for 3d object reconstruction and 6d-pose estimation from 2d images that uses knowledge about object shape as the primary key.In the proposed 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, the number of free parameters required during the registration process is reduced, making the approach feasible.Furthermore, 3d transformations and projective geometry are employed to arrive at a full 3d reconstruction of the object in camera space using a calibrated set up.The method is quantitatively evaluated using synthetic data and tested with real data, and additional results for the well-known linemod data set are shown.In robot experiments, successful grasping of objects demonstrates its usability in real-world environments, and, where possible, a comparison with other methods is provided.
