A system and computer-implemented method for training a machine learnable model to estimate a relative scale of objects in an image. A feature extractor and a scale estimator comprising a machine learnable model part are provided. The feature extractor may be pretrained, while the scale estimator may be trained by the system and method to transform feature maps generated by the feature extractor into relative scale estimates of objects. For that purpose, the scale estimator may be trained on training data in a specific yet non-supervised manner which may not require scale labels. During inference, the scale estimator may be applied to several image patches of an image. The resulting patch-level scale estimates may be combined into a scene geometry map which may be indicative of a geometry of a scene depicted in the image.

A method of identifying and retrieving component digital images for component fault analysis includes generating a known-fault database of digital images of known faults of a previously analyzed component and corresponding remedial actions. The method also includes accessing the known-fault database with a digital image of a current fault of a new component. The method additionally includes comparing the digital image of the current fault with the digital images in the known-fault database based on a computed target characteristic. The method also includes sorting the digital images in the known-fault database in order based on a magnitude of the computed target characteristic for each respective digital image in the known-fault database relative to the digital image of the current fault. The method further includes outputting the sorted digital images to facilitate correlation of the current fault to a particular known fault and identifying the corresponding remedial action.

A method of identifying and retrieving component digital images for component fault analysis includes generating a known-fault database of digital images of known faults of a previously analyzed component and corresponding remedial actions. The method also includes accessing the known-fault database with a digital image of a current fault of a new component. The method additionally includes comparing the digital image of the current fault with the digital images in the known-fault database based on a computed target characteristic. The method also includes sorting the digital images in the known-fault database in order based on a magnitude of the computed target characteristic for each respective digital image in the known-fault database relative to the digital image of the current fault. The method further includes outputting the sorted digital images to facilitate correlation of the current fault to a particular known fault and identifying the corresponding remedial action.

A numerical controller includes an optimum data amount calculation unit that calculates an optimum value of at least one of the number of vertices and the number of polygons of a workpiece after machining, where the number of vertices or the number of polygons are extracted from the CAD data, a three-dimensional data reduction unit that reduces the number of vertices or the number of polygons of the workpiece after machining extracted from the CAD data, a three-dimensional model generation unit that generates a three-dimensional model of the workpiece after machining based on the vertices or the polygons reduced, and a display unit that generates display data for displaying the three-dimensional model and display the generated display data on the display device.

A numerical controller includes an optimum data amount calculation unit that calculates an optimum value of at least one of the number of vertices and the number of polygons of a workpiece after machining, where the number of vertices or the number of polygons are extracted from the CAD data, a three-dimensional data reduction unit that reduces the number of vertices or the number of polygons of the workpiece after machining extracted from the CAD data, a three-dimensional model generation unit that generates a three-dimensional model of the workpiece after machining based on the vertices or the polygons reduced, and a display unit that generates display data for displaying the three-dimensional model and display the generated display data on the display device.

Aspects of the embodiments are directed to a real-time robot command and control interface. An aspect of the interface allows a user to select objects from an image of a scene and to reposition the objects within the scene. Another aspect of the interface can process the image data to determine one or more commands for instructing a robot to move the object to a desired location. The interface can use image and pattern recognition algorithms to determine the new location of the object and can use control algorithms to instruct the robot to move the object.

A system and computer-implemented method for training a machine learnable model to estimate a relative scale of objects in an image. A feature extractor and a scale estimator comprising a machine learnable model part are provided. The feature extractor may be pretrained, while the scale estimator may be trained by the system and method to transform feature maps generated by the feature extractor into relative scale estimates of objects. For that purpose, the scale estimator may be trained on training data in a specific yet non-supervised manner which may not require scale labels. During inference, the scale estimator may be applied to several image patches of an image. The resulting patch-level scale estimates may be combined into a scene geometry map which may be indicative of a geometry of a scene depicted in the image.