Siemens Aktiengesellschaft

Ivan Shugurov

Sergey Zakharov

Slobodan Ilic

Andreas Hutter

Roman Kaskman

Various embodiments of the teachings herein include a computer implemented pose estimation method for providing poses of objects of interest in a scene. The scene comprises a visual representation of the objects of interest in an environment. The method comprising: conducting for each one of the objects of interest a pose estimation; determining edge data of the object of interest from the visual representation representing the edges of the respective object of interest; determining keypoints of the respective object of interest by a previously trained artificial neural keypoint detection network, wherein the artificial neural keypoint detection network utilizes the determined edge data of the respective object of interest Oi as input and provides the keypoints of the respective object of interest as output; and estimating the pose of the respective object of interest based on the respective object's keypoints provided by the artificial neural keypoint detection network.

Pose estimation method and apparatus

A method and a system for object detection and pose estimation within an input image. A 6-degree-of-freedom object detection and pose estimation is performed using a trained encoder-decoder convolutional artificial neural network including an encoder head, an ID mask decoder head, a first correspondence color channel decoder head and a second correspondence color channel decoder head. The ID mask decoder head creates an ID mask for identifying objects, and the color channel decoder heads are used to create a 2D-to-3D-correspondence map. For at least one object identified by the ID mask, a pose estimation based on the generated 2D-to-3D-correspondence map and on a pre-generated bijective association of points of the object with unique value combinations in the first and the second correspondence color channels is generated.

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Munich, Germany

Ivan Shugurov