Abstract: Various embodiments include a pose estimation method for refining an initial multi-dimensional pose of an object of interest to generate a refined multi-dimensional object pose Tpr(NL) with NL?1. The method may include: providing the initial object pose Tpr(0) and at least one 2D-3D-correspondence map ?pri with i=1, . . . , I and I?1; and estimating the refined object pose Tpr(NL) using an iterative optimization procedure of a loss according to a given loss function LF(k) based on discrepancies between the one or more provided 2D-3D-correspondence maps ?pri and one or more respective rendered 2D-3D-correspondence maps ?rendk,i.

Abstract: 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.

Abstract: Embodiments presented herein are generally directed to a protective sleeve for an external component of a hearing prosthesis.

Abstract: Embodiments presented herein are generally directed to a protective sleeve for an external component of a hearing prosthesis.

Abstract: 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.

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: A method of removing noise from a depth image includes presenting real-world depth images in real-time to a first generative adversarial neural network (GAN), the first GAN being trained by synthetic images generated from computer assisted design (CAD) information of at least one object to be recognized in the real-world depth image. The first GAN subtracts the background in the real-world depth image and segments the foreground in the real-world depth image to produce a cleaned real-world depth image. Using the cleaned image, an object of interest in the real-world depth image can be identified via the first GAN trained with synthetic images and the cleaned real-world depth image. In an embodiment the cleaned real-world depth image from the first GAN is provided to a second GAN that provides additional noise cancellation and recovery of features removed by the first GAN.

Abstract: The disclosure relates to a method how to recover an object from a cluttered image. The disclosure also relates to a computer program product and a computer-readable storage medium including instructions which, when the program is executed by a computer, cause the computer to carry out the acts of the mentioned method. Further, the disclosure relates to methods how to train components of a recognition system for recovering an object from such a cluttered image. In addition, the disclosure relates to such a recognition system.

Abstract: 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.

Abstract: A method and apparatus for performing a data driven pairwise registration of 3D point clouds, which includes at least one scanner adapted to capture a first local point cloud in a first scan and a second local point cloud in a second scan; a PPF deriving unit adapted to process both captured local point clouds to derive associated point pair features; a PPF-Autoencoder adapted to process the derived point pair features to extract corresponding PPF-feature vectors; a PC-Autoencoder adapted to process the captured local point clouds to extract corresponding PC-feature vectors; a subtracter adapted to subtract the PPF-feature vectors from the corresponding PC-vectors to calculate latent difference vectors for both captured point clouds concatenated to a latent difference vector; and a pose prediction network adapted to calculate a relative pose prediction, between the first and second scan performed by the scanner on the basis of the concatenated latent difference vector.

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: A method for training a generative network that is configured for converting cluttered images into a representation of the synthetic domain and a method for recovering an object from a cluttered image.

Abstract: The disclosure relates to a method how to recover an object from a cluttered image. The disclosure also relates to a computer program product and a computer-readable storage medium including instructions which, when the program is executed by a computer, cause the computer to carry out the acts of the mentioned method. Further, the disclosure relates to methods how to train components of a recognition system for recovering an object from such a cluttered image. In addition, the disclosure relates to such a recognition system.

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: A device, including a behind-the-ear (BTE) device ear interface fixture, the fixture including a loop portion configured to enable a pinna of a recipient to be inserted there through and an attachment portion configured to removably attach the fixture to a BTE electronics module and/or a BTE battery, wherein the inner perimeter of the loop portion is non-circular when the loop portion is in a relaxed state.

Abstract: A method of removing noise from a depth image includes presenting real-world depth images in real-time to a first generative adversarial neural network (GAN), the first GAN being trained by synthetic images generated from computer assisted design (CAD) information of at least one object to be recognized in the real-world depth image. The first GAN subtracts the background in the real-world depth image and segments the foreground in the real-world depth image to produce a cleaned real-world depth image. Using the cleaned image, an object of interest in the real-world depth image can be identified via the first GAN trained with synthetic images and the cleaned real-world depth image. In an embodiment the cleaned real-world depth image from the first GAN is provided to a second GAN that provides additional noise cancellation and recovery of features removed by the first GAN.

Abstract: Various embodiments of the teachings herein may include a method for identifying an object instance and determining an orientation of localized objects in noisy environments using an artificial neural network may include: recording a plurality of images of an object for obtaining a multiplicity of samples containing image data, object identity, and orientation; generating a training set and a template set from the samples; training the artificial neural network using the training set and a loss function; and determining the object instance and/or the orientation of the object by evaluating the template set using the artificial neural network. The loss function includes a dynamic margin.

Abstract: An ear hook apparatus, including an ear hook tip, an ear hook chassis, and a male connector, wherein the ear hook apparatus is configured such that the male connector attaches to one or more components of a BTE device at and/or below a base of a BTE electronics module of the BTE device.

Abstract: Embodiments presented herein are generally directed to a protective sleeve for an external component of a hearing prosthesis.