An information handling system includes a three dimensional camera and a processor. The three dimensional camera is configured to capture a three dimensional image. The processor is configured to communicate with the three dimensional camera. The processor to provide the three dimensional image to be displayed on a display screen of the information handling system, to determine three dimensional coordinates for an object within the three dimensional image, and to calculate a dimension of the object based on the three dimensional coordinates.

The scale of modeled building objects from collected imagery is determined by identifying architectural elements within building object imagery, determining a scale of the identified architectural elements by matching them to known industry standard architectural element based on dimensional ratio comparisons and deriving an average scaling factor based on scale of the identified architectural elements. A three dimensional model of the building object is scaled according to the average scaling. Scaled architecture elements within a relative error can be used for scaling the model according to an updated scale factor.

A navigation program for an autonomous vehicle, the navigation program configured to: receive an initial model of an object to be inspected by the autonomous vehicle; identify an inspection target associated with the initial model of the object; and determine an inspection location for the autonomous vehicle from which inspection target is inspectable by an inspection system of the autonomous vehicle, wherein the initial model includes one or more convex shapes representing the object.

In scan data retrieval, a mesh is fit (32) to surface data of a current patient, such as data from an optical or depth sensor (18). Meshes are also fit (48) to medical scan data, such as fitting (48) to skin surface segments of computed tomography data. The meshes or parameters derived from the meshes may be more efficiently compared (34) to identify (36) a previous patient with similar body shape and/or size. The scan configuration (38) for that patient, or that patient as altered to account for differences from the current patient, is used. In some embodiments, the parameter vector used for searching (34) includes principle component analysis coefficients. In further embodiments, the principle component analysis coefficients may be projected to a more discriminative space using metric learning.

Methods, apparatus, systems, and articles of manufacture are disclosed. An example apparatus includes an index map generator to generate a first index map based on a first virtual environment generated by a first mobile device, the first mobile device associated with a first user, a collision detector to determine a collision likelihood based on the first index map, and an object placer to, in response to the collision likelihood satisfying a threshold, modify the first virtual environment.

Described herein are methods and systems for closed-form 3D model generation of non-rigid complex objects from scans with large holes. A computing device receives (i) a partial scan of a non-rigid complex object captured by a sensor coupled to the computing device; (ii) a partial 3D model corresponding to the object, and (iii) a whole 3D model corresponding to the object, wherein the partial 3D scan and the partial 3D model each includes one or more large holes. The device performs a rough match on the partial 3D model and changes the whole 3D model using the rough match to generate a deformed 3D model. The device refines the deformed 3D model using a deformation graph, reshapes the refined deformed 3D model to have greater detail, and adjusts the whole 3D model according to the reshaped 3D model to generate a closed-form 3D model that closes holes in the scan.

The present disclosure provides a computer-implemented method of, and system for, characterizing the phenotype of a plant. The method includes: (i) obtaining mesh data representing a surface of the plant, said mesh data including data representing a plurality of polygons having respective sets of vertices, each vertex having a spatial coordinate; and (ii) applying at least two segmentations of progressively finer resolution to the mesh data to assign the vertices to distinct morphological regions of the plant.

Comprehensive 2D learning images are collected for learning subjects. Standardized 2D gallery images of many gallery subjects are collected, one per gallery subject. A 2D query image of a query subject is collected, of arbitrary viewing aspect, illumination, etc. 3D learning models, 3D gallery models, and a 3D query model are determined from the learning, gallery, and query images. A transform is determined for the selected learning model and each gallery model that yields or approximates the query image. The transform is at least partly 3D, such as 3D illumination transfer or 3D orientation alignment. The transform is applied to each gallery model so that the transformed gallery models more closely resemble the query model. 2D transformed gallery images are produced from the transformed gallery models, and are compared against the 2D query image to identify whether the query subject is also any of the gallery subjects.

A monitoring system and method for a tool of working equipment, preferably a ground engaging tool comprising wear members such as an excavator bucket, the system/method including one or more sensors mounted on the working equipment and directed towards the tool and a processor configured to: receive data relating to the tool from the one or more sensors, generate a three dimensional representation of at least a portion of the tool using the received data, compare the generated three dimensional representation with a previously generated three dimensional representation, and identify one or more of wear and loss of at least a portion of the tool, preferably a wear member portion, using the comparison of the generated three dimensional representation with the previously generated three dimensional representation.

A method implemented on a visual computing device to authenticate one or more users includes receiving a first three-dimensional pattern from a user. The first three-dimensional pattern is sent to a server computer. At a time of user authentication, a second three-dimensional pattern is received from the user. The second three-dimensional pattern is sent to the server computer. An indication is received from the server computer as to whether the first three-dimensional pattern matches the second three-dimensional pattern within a margin of error. When the first three-dimensional pattern matches the second three-dimensional pattern within the margin of error, the user is authenticated at the server computer. When the first three-dimensional pattern does not match the second three-dimensional pattern within the margin of error, user is prevented from being authenticated at the server computer.