Abstract: A method for pose estimation includes receiving an image containing an object, a first pose of the object in the image, and 3D boundary features of a model corresponding to the object; computing a first pose confidence of the first pose based on the image, the 3D boundary features, and the first pose; perturbing the first pose to obtain a second pose; computing a second pose confidence of the second pose based on the image, the 3D boundary features, and the second pose; determining if the second pose confidence is greater than the first pose confidence, and; and outputting the second pose and the second pose confidence if the second pose confidence is greater than the first pose confidence.

Abstract: Training a pose detection algorithm and deriving an object pose using a trained pose detection algorithm. Specifying a first view of a 3D model with respect to a virtual camera, generating a depth map by projecting the 3D model based on the first view, generating a second view by perturbing the first view, selecting reference 2D locations in an overlapping area of the 3D model between the first view and the second view. Transforming the reference 2D locations into a 3D point set corresponding to the second view. For each reference 2D location, calculating a depth difference between the first view and the second view at the reference 2D location. Associating the perturbation between the second view and the first view with the calculated depth differences, and training an algorithm model to identify a pose of an object corresponding to the 3D model.

Abstract: Training a pose detection algorithm and deriving an object pose using a trained pose detection algorithm. Specifying a first view of a 3D model with respect to a virtual camera, generating a depth map by projecting the 3D model based on the first view, generating a second view by perturbing the first view, selecting reference 2D locations in an overlapping area of the 3D model between the first view and the second view. Transforming the reference 2D locations into a 3D point set corresponding to the second view. For each reference 2D location, calculating a depth difference between the first view and the second view at the reference 2D location. Associating the perturbation between the second view and the first view with the calculated depth differences, and training an algorithm model to identify a pose of an object corresponding to the 3D model.

Abstract: A method for pose estimation includes receiving an image containing an object, a first pose of the object in the image, and 3D boundary features of a model corresponding to the object; computing a first pose confidence of the first pose based on the image, the 3D boundary features, and the first pose; perturbing the first pose to obtain a second pose; computing a second pose confidence of the second pose based on the image, the 3D boundary features, and the second pose; determining if the second pose confidence is greater than the first pose confidence, and; and outputting the second pose and the second pose confidence if the second pose confidence is greater than the first pose confidence.