Abstract: Methods and systems are described that are configured for generating a digital twin of a physical object. A computing device may process imaging data associated with a physical object captured by one or more imaging devices. The imaging data may be used to generate a point cloud of the physical object. A voxel filter may be applied to the point cloud to reduce one or more data points of the point cloud. A refinement process may be applied to the reduced point cloud to smooth, and increase the accuracy of, the point cloud. The filtered and refined point cloud may then be converted to a digital twin of the physical object. Raycasting and projection may be applied to the imaging data and the digital twin to generate measurement data associated with the physical object allowing a user interact with the digital twin and perform metric analysis.

Abstract: Methods and systems are described that are configured for generating a digital twin of a physical object. A computing device may process imaging data associated with a physical object captured by one or more imaging devices. The imaging data may be used to generate a point cloud of the physical object. A voxel filter may be applied to the point cloud to reduce one or more data points of the point cloud. A refinement process may be applied to the reduced point cloud to smooth, and increase the accuracy of, the point cloud. The filtered and refined point cloud may then be converted to a digital twin of the physical object. Raycasting and projection may be applied to the imaging data and the digital twin to generate measurement data associated with the physical object allowing a user interact with the digital twin and perform metric analysis.

Abstract: Training images can be synthesized in order to obtain enough data to train a model (e.g., a neural network) to recognize various classifications of a type of object. Images can be synthesized by blending images of objects labeled using those classifications into selected background images. To improve results, one or more operations are performed to determine whether the synthesized images can still be used as training data, such as by verifying one or more objects of interested represented in those images is not occluded, or at least satisfies a threshold level of acceptance. The training images can be used with real world images to train the model.

Abstract: In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus may maintain an first dataset configured to select pick points for objects. The apparatus may receive, from a user device, a user dataset including a user selected pick point associated with at least one object and a first image of the at least one first object. The apparatus may generate a second dataset based at least in part on the first dataset and the user dataset. The apparatus may receive a second image of a second object. The apparatus may select a pick point for the second object using the second dataset and the second image of the second object. The apparatus may send information associated with the pick point selected for the second object to a robotics device for picking up the second object.

Abstract: In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus may maintain an first dataset configured to select pick points for objects. The apparatus may receive, from a user device, a user dataset including a user selected pick point associated with at least one object and a first image of the at least one first object. The apparatus may generate a second dataset based at least in part on the first dataset and the user dataset. The apparatus may receive a second image of a second object. The apparatus may select a pick point for the second object using the second dataset and the second image of the second object. The apparatus may send information associated with the pick point selected for the second object to a robotics device for picking up the second object.

Abstract: Training images can be synthesized in order to obtain enough data to train a model (e.g., a neural network) to recognize various classifications of a type of object. Images can be synthesized by blending images of objects labeled using those classifications into selected background images. To improve results, one or more operations are performed to determine whether the synthesized images can still be used as training data, such as by verifying one or more objects of interested represented in those images is not occluded, or at least satisfies a threshold level of acceptance. The training images can be used with real world images to train the model.

Abstract: In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus may maintain an first dataset configured to select pick points for objects. The apparatus may receive, from a user device, a user dataset including a user selected pick point associated with at least one object and a first image of the at least one first object. The apparatus may generate a second dataset based at least in part on the first dataset and the user dataset. The apparatus may receive a second image of a second object. The apparatus may select a pick point for the second object using the second dataset and the second image of the second object. The apparatus may send information associated with the pick point selected for the second object to a robotics device for picking up the second object.

Abstract: Training images can be synthesized in order to obtain enough data to train a model (e.g., a neural network) to recognize various classifications of a type of object. Images can be synthesized by blending images of objects labeled using those classifications into selected background images. To improve results, one or more operations are performed to determine whether the synthesized images can still be used as training data, such as by verifying one or more objects of interested represented in those images is not occluded, or at least satisfies a threshold level of acceptance. The training images can be used with real world images to train the model.

Abstract: Training images can be synthesized in order to obtain enough data to train a model (e.g., a neural network) to recognize various classifications of a type of object. Images can be synthesized by blending images of objects labeled using those classifications into selected background images. To improve results, one or more operations are performed to determine whether the synthesized images can still be used as training data, such as by verifying one or more objects of interested represented in those images is not occluded, or at least satisfies a threshold level of acceptance. The training images can be used with real world images to train the model.

Abstract: In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus may maintain an first dataset configured to select pick points for objects. The apparatus may receive, from a user device, a user dataset including a user selected pick point associated with at least one object and a first image of the at least one first object. The apparatus may generate a second dataset based at least in part on the first dataset and the user dataset. The apparatus may receive a second image of a second object. The apparatus may select a pick point for the second object using the second dataset and the second image of the second object. The apparatus may send information associated with the pick point selected for the second object to a robotics device for picking up the second object.

Abstract: In an aspect of the disclosure, a method, a computer-readable medium, and an apparatus are provided. The apparatus may maintain an first dataset configured to select pick points for objects. The apparatus may receive, from a user device, a user dataset including a user selected pick point associated with at least one object and a first image of the at least one first object. The apparatus may generate a second dataset based at least in part on the first dataset and the user dataset. The apparatus may receive a second image of a second object. The apparatus may select a pick point for the second object using the second dataset and the second image of the second object. The apparatus may send information associated with the pick point selected for the second object to a robotics device for picking up the second object.