Abstract: The present approach relates to an automatic and efficient motion plan for a drone to collect and save a qualified dataset that may be used to improve reconstruction of 3D models using the acquired data. The present architecture provides an automatic image processing context, eliminating low quality images and providing improved image data for point cloud generation and texture mapping.

Abstract: The present approach relates to the use of a point cloud of an object to initialize or seed a space carving technique used to generate a 3D model of the object. In one implementation, feature matching is performed on 2D images, with matched features constituting the points of a point cloud model. The point cloud generated in this manner, is one input of a foreground/background segmentation algorithm, which generates a set of segmented 2D images used by a space carving routine to generate a 3D model of the object.

Abstract: Methods and systems are provided for generating deep learning training data with an imaging system. In one embodiment, a method for an imaging system comprises performing a scan of a subject to acquire imaging data, training a deep neural network on the imaging data to obtain updates to the deep neural network, and transmitting the updates to a server for training a central deep neural network. In this way, imaging data may be leveraged for training and developing global deep learning models without transmitting the imaging data itself, thereby preserving patient privacy.

Abstract: The present approach relates to the use of a point cloud of an object to initialize or seed a space carving technique used to generate a 3D model of the object. In one implementation, feature matching is performed on 2D images, with matched features constituting the points of a point cloud model. The point cloud generated in this manner, is one input of a foreground/background segmentation algorithm, which generates a set of segmented 2D images used by a space carving routine to generate a 3D model of the object.

Abstract: The present approach relates to the use of a point cloud of an object to initialize or seed a space carving technique used to generate a 3D model of the object. In one implementation, feature matching is performed on 2D images, with matched features constituting the points of a point cloud model. The point cloud generated in this manner, is one input of a foreground/background segmentation algorithm, which generates a set of segmented 2D images used by a space carving routine to generate a 3D model of the object.

Abstract: Methods and systems are provided for capturing deep learning training data from imaging systems. In one embodiment, a method for an imaging system comprises performing a scan of a subject to acquire imaging data, inputting the imaging data to a deep neural network, displaying an output of the deep neural network and an image reconstructed from the imaging data, and transmitting an intermediate representation of the imaging data generated by the deep neural network to a server for training a central deep neural network. In this way, imaging data may be leveraged for training and developing global deep learning models without transmitting the imaging data itself, thereby preserving patient privacy.

Abstract: The present approach relates to an automatic and efficient motion plan for a drone to collect and save a qualified dataset that may be used to improve reconstruction of 3D models using the acquired data. The present architecture provides an automatic image processing context, eliminating low quality images and providing improved image data for point cloud generation and texture mapping.

Abstract: A robotic system includes a processing system comprising at least one processor. The processor generates a plan to monitor the asset. The plan comprises one or more tasks to be performed by the at least one robot. The processor receives sensor data from at least one sensor indicating one or more characteristics of the asset. The processor adjusts the plan to monitor the asset by adjusting or adding one or more tasks to the plan based on one or both of the quality of the acquired data or a potential defect of the asset. The adjusted plan causes the at least one robot to acquire additional data related to the asset when executed.

Abstract: The present approach relates to an automatic and efficient motion plan for a drone to collect and save a qualified dataset that may be used to improve reconstruction of 3D models using the acquired data. The present architecture provides an automatic image processing context, eliminating low quality images and providing improved image data for point cloud generation and texture mapping.

Abstract: Methods and systems are provided for generating deep learning training data with an imaging system. In one embodiment, a method for an imaging system comprises performing a scan of a subject to acquire imaging data, training a deep neural network on the imaging data to obtain updates to the deep neural network, and transmitting the updates to a server for training a central deep neural network. In this way, imaging data may be leveraged for training and developing global deep learning models without transmitting the imaging data itself, thereby preserving patient privacy.

Abstract: Methods and systems are provided for capturing deep learning training data from imaging systems. In one embodiment, a method for an imaging system comprises performing a scan of a subject to acquire imaging data, inputting the imaging data to a deep neural network, displaying an output of the deep neural network and an image reconstructed from the imaging data, and transmitting an intermediate representation of the imaging data generated by the deep neural network to a server for training a central deep neural network. In this way, imaging data may be leveraged for training and developing global deep learning models without transmitting the imaging data itself, thereby preserving patient privacy.

Abstract: The present approach relates to an automatic and efficient motion plan for a drone to collect and save a qualified dataset that may be used to improve reconstruction of 3D models using the acquired data. The present architecture provides an automatic image processing context, eliminating low quality images and providing improved image data for point cloud generation and texture mapping.

Abstract: The present approach relates to the use of a point cloud of an object to initialize or seed a space carving technique used to generate a 3D model of the object. In one implementation, feature matching is performed on 2D images, with matched features constituting the points of a point cloud model. The point cloud generated in this manner, is one input of a foreground/background segmentation algorithm, which generates a set of segmented 2D images used by a space carving routine to generate a 3D model of the object.

Abstract: A robotic system includes a processing system comprising at least one processor. The processor generates a plan to monitor the asset. The plan comprises one or more tasks to be performed by the at least one robot. The processor receives sensor data from at least one sensor indicating one or more characteristics of the asset. The processor adjusts the plan to monitor the asset by adjusting or adding one or more tasks to the plan based on one or both of the quality of the acquired data or a potential defect of the asset. The adjusted plan causes the at least one robot to acquire additional data related to the asset when executed.