A method for pre-operative orthopedic planning includes obtaining only a high-resolution knee-joint scan of a patient, determining hip rotation center and ankle rotation center from anthropometric data based on personal data of the patient, and determining a mechanical axis of the knee joint based on the anthropometric data. The method also includes preparing at least a two-dimensional image model of the knee joint using the knee-joint scan and the determined mechanical axis, and preparing a pre-operative surgical plan based on the image of the knee joint.

A method for pre-operative orthopedic planning includes obtaining only a high-resolution knee-joint scan of a patient, determining hip rotation center and ankle rotation center from anthropometric data based on personal data of the patient, and determining a mechanical axis of the knee joint based on the anthropometric data. The method also includes preparing at least a two-dimensional image model of the knee joint using the knee-joint scan and the determined mechanical axis, and preparing a pre-operative surgical plan based on the image of the knee joint.

An information processing device includes processing circuitry. The processing circuitry obtain target information that indicates at least one of a distance to a target object or a position of the target object. The processing circuitry generate, based on the target information, map information of a space including a plurality of areas, the map information indicating presence or absence of the target object in a first area included in the plurality of areas, and a detailed position of the target object in the first area.

An information processing device includes processing circuitry. The processing circuitry obtain target information that indicates at least one of a distance to a target object or a position of the target object. The processing circuitry generate, based on the target information, map information of a space including a plurality of areas, the map information indicating presence or absence of the target object in a first area included in the plurality of areas, and a detailed position of the target object in the first area.

A method for automated flushness measurement of point cloud rivets, including: extracting a rivet outline by adopting an RANSAC circle fitting algorithm, and determining a center, a radius and a normal vector of an outline circle; extracting point cloud of a rivet head for a single rivet outline; extracting point cloud around the rivet for the single rivet outline; and generating a distance color difference map reflecting rivet flushness according to the point cloud of the rivet head and the point cloud around the rivet. According to the present invention, the point cloud of the rivet head and the point cloud around the rivet can be respectively extracted, and the distance color difference map reflecting the rivet flushness is generated according to the point cloud of the rivet head and the point cloud around the rivet, so that the rivet flushness is rapidly and effectively measured.

A method for automated flushness measurement of point cloud rivets, including: extracting a rivet outline by adopting an RANSAC circle fitting algorithm, and determining a center, a radius and a normal vector of an outline circle; extracting point cloud of a rivet head for a single rivet outline; extracting point cloud around the rivet for the single rivet outline; and generating a distance color difference map reflecting rivet flushness according to the point cloud of the rivet head and the point cloud around the rivet. According to the present invention, the point cloud of the rivet head and the point cloud around the rivet can be respectively extracted, and the distance color difference map reflecting the rivet flushness is generated according to the point cloud of the rivet head and the point cloud around the rivet, so that the rivet flushness is rapidly and effectively measured.

A method of automated tooth segmentation of a three dimensional scan data using a deep learning, includes determining a U-shape of teeth in input scan data and operating a U-shape normalization operation to the input scan data to generate first scan data, operating a teeth and gum normalization operation, in which the first scan data are received and a region of interest (ROI) of the teeth and gum is set based on a landmark formed on the tooth, to generate second scan data, inputting the second scan data to a convolutional neural network to label the teeth and the gum and extracting a boundary between the teeth and the gum using labeled information of the teeth and the gum.

A method of automated tooth segmentation of a three dimensional scan data using a deep learning, includes determining a U-shape of teeth in input scan data and operating a U-shape normalization operation to the input scan data to generate first scan data, operating a teeth and gum normalization operation, in which the first scan data are received and a region of interest (ROI) of the teeth and gum is set based on a landmark formed on the tooth, to generate second scan data, inputting the second scan data to a convolutional neural network to label the teeth and the gum and extracting a boundary between the teeth and the gum using labeled information of the teeth and the gum.

A method for autonomously maneuvering a tow vehicle towards a trailer positioned behind the tow vehicle is provided. The method includes receiving one or more images from one or more cameras positioned on a back portion of the tow vehicle. The method also includes identifying a trailer representation within the one or more images. The trailer representation being indicative of the trailer positioned behind the tow vehicle. The method also includes setting a vertical center of the trailer representation as a target. The method also includes determining a first steering wheel angle to turn the tow vehicle such that the vehicle autonomously maneuvers in a direction towards the target. The method also includes transmitting instructions to a drive system causing the tow vehicle to maneuver based on the first steering wheel angle.

A method, computer program product, and system to perform a sale transaction are provided. The method includes identifying each item of a plurality of items, based on at least one image of the plurality of items, determining a cost for each item, optionally identifying a person based on an image of the person, adding each of the items and each of the costs to a sale transaction, and charging the person for the sale transaction.