Abstract: A system for producing a depth map can include a processor and a memory. The memory can store a neural network. The neural network can include an encoding portion module, a multi-frame feature matching portion module, and a decoding portion module. The encoding portion module can include instructions that, when executed by the processor, cause the processor to encode an image to produce single-frame features. The multi-frame feature matching portion module can include instructions that, when executed by the processor, cause the processor to process the single-frame features to produce information. The decoding portion module can include instructions that, when executed by the processor, cause the processor to decode the information to produce the depth map. A first training dataset, used to train the multi-frame feature matching portion module, can be different from a second training dataset used to train the encoding portion module and the decoding portion module.

Abstract: A system for producing a depth map can include a processor and a memory. The memory can store a candidate depth production module and a depth map production module. The candidate depth production module can include instructions that cause the processor to: (1) identify, in a first image, an epipolar line associated with a pixel in a second image and (2) sample, from a first image feature set, a set of candidate depths for pixels along the epipolar line. The depth map production module can include instructions that cause the processor to: (1) determine a similarity measure between a feature, from a second image feature set, and a member of the set and (2) produce, from the second image, the depth map with a depth for the pixel being a depth associated with a member, of the set, having a greatest similarity measure.

Abstract: Systems and methods described herein relate to reconstructing a scene in three dimensions from a two-dimensional image. One embodiment processes an image using a detection transformer to detect an object in the scene and to generate a NOCS map of the object and a background depth map; uses MLPs to relate the object to a differentiable database of object priors (PriorDB); recovers, from the NOCS map, a partial 3D object shape; estimates an initial object pose; fits a PriorDB object prior to align in geometry and appearance with the partial 3D shape to produce a complete shape and refines the initial pose estimate; generates an editable and re-renderable 3D scene reconstruction based, at least in part, on the complete shape, the refined pose estimate, and the depth map; and controls the operation of a robot based, at least in part, on the editable and re-renderable 3D scene reconstruction.

Abstract: The method for increasing contractility in patients with systolic heart failure involves screening for candidate small molecules which block the interaction between Rad and the plasma membrane and/or block the interaction between Rad and the CaV1.2/CaV?2 complex, or between Rad and CaV?2, in order to increase cardiac contractility. A method for preventing calcium overload and arrhythmias in heart disease involves preventing the dissociation of Rad and the CaV1.2/CaV?2 complex, or between Rad and CaV?2, during beta-adrenergic system activation. Additionally, a method of screening for drugs that block interaction between an RGK GTPase protein and a ?-subunit of the calcium channel is provided. A suitable technique, such as fluorescence resonance energy transfer (FRET), may be used to assess blocking of the interaction between the RGK GTPase protein and the ?-subunit of the calcium channel for the treatment of heart disease, pain, diabetes, skeletal muscle disorders and/or central nervous system (CNS) disorders.

Abstract: A method for 3D auto-labeling of objects with predetermined structural and physical constraints includes identifying initial object-seeds for all frames from a given frame sequence of a scene. The method also includes refining each of the initial object-seeds over the 2D/3D data, while complying with the predetermined structural and physical constraints to auto-label 3D object vehicles within the scene. The method further includes linking the auto-label 3D object vehicles over time into trajectories while respecting the predetermined structural and physical constraints.

Abstract: Systems and methods for three-dimensional object detection are disclosed herein. One embodiment inputs, to a neural network, a two-dimensional label associated with an object to produce a Normalized-Object-Coordinate-Space (NOCS) image and a shape vector, the shape vector mapping to a continuously traversable coordinate shape space (CSS); decodes the NOCS image and the shape vector to an object model in the CSS; back-projects, in a frustum, the NOCS image to a LIDAR point cloud; identifies correspondences between the LIDAR point cloud and the object model to estimate an affine transformation between the LIDAR point cloud and the object model; iteratively refines the affine transformation using a differentiable SDF renderer; extracts automatically a three-dimensional label for the object based, at least in part, on the iteratively refined affine transformation; and performs three-dimensional object detection of the object based, at least in part, on the extracted three-dimensional label for the object.

Abstract: Systems and methods for training an adapter network that adapts a model pre-trained on synthetic images to real-world data are disclosed herein. A system may include a processor and a memory in communication with the processor and having machine-readable that cause the processor to output, using a neural network, a predicted scene that includes a three-dimensional bounding box having pose information of an object, generate a rendered map of the object that includes a rendered shape of the object and a rendered surface normal of the object, and train the adapter network, which adapts the predicted scene to adjust for a deformation of the input image by comparing the rendered map to the output map acting as a ground truth.

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: 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: A method for 3D auto-labeling of objects with predetermined structural and physical constraints includes identifying initial object-seeds for all frames from a given frame sequence of a scene. The method also includes refining each of the initial object-seeds over the 2D/3D data, while complying with the predetermined structural and physical constraints to auto-label 3D object vehicles within the scene. The method further includes linking the auto-label 3D object vehicles over time into trajectories while respecting the predetermined structural and physical constraints.

Abstract: Systems and methods for three-dimensional object detection are disclosed herein. One embodiment inputs, to a neural network, a two-dimensional label associated with an object to produce a Normalized-Object-Coordinate-Space (NOCS) image and a shape vector, the shape vector mapping to a continuously traversable coordinate shape space (CSS); decodes the NOCS image and the shape vector to an object model in the CSS; back-projects, in a frustum, the NOCS image to a LIDAR point cloud; identifies correspondences between the LIDAR point cloud and the object model to estimate an affine transformation between the LIDAR point cloud and the object model; iteratively refines the affine transformation using a differentiable SDF renderer; extracts automatically a three-dimensional label for the object based, at least in part, on the iteratively refined affine transformation; and performs three-dimensional object detection of the object based, at least in part, on the extracted three-dimensional label for the object.

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 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: A seed value making method and device for a PRNG (Pseudo Random Number Generator) are provided. The seed value making method includes the steps of: accumulating in memory units of a First Data Pool data from various external sources, analyzing the data to determine a source type, computing entropy estimations for each of the external sources basing on the source type as determined, and generating a seed value by using the entropy estimations and the data accumulated in the memory units of the First Data Pool. Accordingly, in generating a seed value, dynamic estimation of random sources rate, and classification of sources on slow and fast ones, and reliable and unreliable ones, can be provided, and also, seed values can be made with taking in account rate and reliability of the sources.

Abstract: A seed value making method and device for a PRNG (Pseudo Random Number Generator) are provided. The seed value making method includes the steps of: accumulating in memory units of a First Data Pool data from various external sources, analyzing the data to determine a source type, computing entropy estimations for each of the external sources basing on the source type as determined, and generating a seed value by using the entropy estimations and the data accumulated in the memory units of the First Data Pool. Accordingly, in generating a seed value, dynamic estimation of random sources rate, and classification of sources on slow and fast ones, and reliable and unreliable ones, can be provided, and also, seed values can be made with taking in account rate and reliability of the sources.