Abstract: In various examples there is an apparatus for detecting position and orientation of an object. The apparatus comprises a memory storing at least one frame of captured sensor data depicting the object. The apparatus also comprises a trained machine learning system configured to receive the frame of the sensor data and to compute a plurality of two dimensional positions in the frame. Each predicted two dimensional position is a position of sensor data in the frame depicting a keypoint, where a keypoint is a pre-specified 3D position relative to the object. At least one of the keypoints is a floating keypoint depicting a pre-specified position relative to the object, lying inside or outside the object's surface. The apparatus comprises a pose detector which computes the three dimensional position and orientation of the object using the predicted two dimensional positions and outputs the computed three dimensional position and orientation.

Abstract: A tracker is described which comprises a processor configured to receive captured sensor data depicting an object. The processor is configured to access a rigged polygon mesh model of the object and to compute a plurality of approximate surface normals of a limit surface of the rigged polygon mesh. The processor is configured to compute values of pose parameters of the model by calculating an optimization to fit the model to the captured sensor data where the optimization uses an evaluation function based on the plurality of approximate surface normals.

Abstract: A ground truth engine is described which has a memory holding a plurality of captured images depicting an articulated item. A processor of the engine is configured to access a parameterized, three dimensional (3D) model of the item. An optimizer of the ground truth engine is configured to compute ground truth values of the parameters of the 3D model for individual ones of the captured images, such that the articulated item depicted in the captured image fits the 3D model, the optimizer configured to take into account feedback data from one or more humans, about accuracy of a plurality of the computed values of the parameters.

Abstract: A ground truth engine is described which has a memory holding a plurality of captured images depicting an articulated item. A processor of the engine is configured to access a parameterized, three dimensional (3D) model of the item. An optimizer of the ground truth engine is configured to compute ground truth values of the parameters of the 3D model for individual ones of the captured images, such that the articulated item depicted in the captured image fits the 3D model, the optimizer configured to take into account feedback data from one or more humans, about accuracy of a plurality of the computed values of the parameters.

Abstract: A tracker is described which comprises an input configured to receive captured sensor data depicting an object. The tracker has a processor configured to access a rigged, smooth-surface model of the object and to compute values of pose parameters of the model by calculating an optimization to fit the model to data related to the captured sensor data. Variables representing correspondences between the data and the model are included in the optimization jointly with the pose parameters.

Abstract: Examples describe an apparatus for calibrating a three dimensional (3D) mesh model of an articulated object. The articulated object is an instance of a specified object class. The apparatus comprises an input configured to receive captured sensor data depicting the object. The apparatus has a calibration engine configured to compute values of shape parameters of the 3D mesh model which indicate which member of the object class is depicted in the captured sensor data, in order to calibrate the 3D mesh model. The calibration engine is configured to compute the values of the shape parameters with an optimization process to find at least one potential local or global minimum of an energy function, the energy function expressing a degree of similarity between data rendered from the model and the received sensor data.

Abstract: A tracker is described which comprises a processor configured to receive captured sensor data depicting an object. The processor is configured to access a rigged polygon mesh model of the object and to compute a plurality of approximate surface normals of a limit surface of the rigged polygon mesh. The processor is configured to compute values of pose parameters of the model by calculating an optimization to fit the model to the captured sensor data where the optimization uses an evaluation function based on the plurality of approximate surface normals.

Abstract: A tracker is described which comprises an input configured to receive captured sensor data depicting an object. The tracker has a processor configured to access a rigged, smooth-surface model of the object and to compute values of pose parameters of the model by calculating an optimization to fit the model to data related to the captured sensor data. Variables representing correspondences between the data and the model are included in the optimization jointly with the pose parameters.

Abstract: Examples describe an apparatus for calibrating a three dimensional (3D) mesh model of an articulated object. The articulated object is an instance of a specified object class. The apparatus comprises an input configured to receive captured sensor data depicting the object. The apparatus has a calibration engine configured to compute values of shape parameters of the 3D mesh model which indicate which member of the object class is depicted in the captured sensor data, in order to calibrate the 3D mesh model. The calibration engine is configured to compute the values of the shape parameters with an optimization process to find at least one potential local or global minimum of an energy function, the energy function expressing a degree of similarity between data rendered from the model and the received sensor data.