Abstract: Various embodiments include a method for facilitating tomographic reconstruction comprising: emitting an x-ray beam from an x-ray unit; ascertaining an attenuation of the x-ray beam during transmission through an object situated in a beam path of the x-ray beam; ascertaining structure data of the object based at least in part on the attenuation of the x-ray beam; and ascertaining a pose of the x-ray unit relative to the object using a digital model of the object and based at least in part on the attenuation of the x-ray beam.

Abstract: Various embodiments include a method for determining a pose of an object in its surroundings comprising: using an optical capture device to capture the object and its surroundings as current recording; determining the pose of the object using optical image analysis; and using a neural network to ascertain the pose of the object. The neural network is taught with multi-task learning using pose regression and descriptor learning using a triplet-wise loss function and a pair-wise loss function. The pose regression uses quaternions. Determining the triplet-wise loss function includes using a dynamic margin term. Determining the pair-wise loss function includes an anchoring function.

Abstract: Various embodiments include a method for facilitating tomographic reconstruction comprising: emitting an x-ray beam from an x-ray unit; ascertaining an attenuation of the x-ray beam during transmission through an object situated in a beam path of the x-ray beam; ascertaining structure data of the object based at least in part on the attenuation of the x-ray beam; and ascertaining a pose of the x-ray unit relative to the object using a digital model of the object and based at least in part on the attenuation of the x-ray beam.

Abstract: Various embodiments include a method for determining a pose of an object in its surroundings comprising: using an optical capture device to capture the object and its surroundings as current recording; determining the pose of the object using optical image analysis; and using a neural network to ascertain the pose of the object. The neural network is taught with multi-task learning using pose regression and descriptor learning using a triplet-wise loss function and a pair-wise loss function. The pose regression uses quaternions. Determining the triplet-wise loss function includes using a dynamic margin term. Determining the pair-wise loss function includes an anchoring function.