Systems and methods for automated detection of changes in extent of structures using imagery are disclosed, including a non-transitory computer readable medium storing computer executable code that when executed by a processor cause the processor to: align, with an image classifier model, an outline of a structure at a first instance of time to pixels within an image depicting the structure captured at a second instance of time; assess a degree of alignment between the outline and the pixels depicting the structure, so as to classify similarities between the structure depicted within the pixels of the image and the outline using a machine learning model to generate an alignment confidence score; and determine an existence of a change in the structure based upon the alignment confidence score indicating a level of confidence below a predetermined threshold level of confidence that the outline and the pixels within the image are aligned.

Techniques are described herein for generating and using a unified shape representation that encompasses features of different types of shape representations. In some embodiments, the unified shape representation is a unicode comprising a vector of embeddings and values for the embeddings. The embedding values are inferred, using a neural network that has been trained on different types of shape representations, based on a first representation of a three-dimensional (3D) shape. The first representation is received as input to the trained neural network and corresponds to a first type of shape representation. At least one embedding has a value dependent on a feature provided by a second type of shape representation and not provided by the first type of shape representation. The value of the at least one embedding is inferred based upon the first representation and in the absence of the second type of shape representation for the 3D shape.

A device that executes a program stored in the memory to perform generating a local graph for the 3D point cloud based on distances and angles between 3D points in the 3D point cloud; matching the local graph using a similarity function and determining a feature matching pair in a matched local graph; and estimating a rigid body transformation matrix using the feature matching pair is provided.

For patient model estimation from surface data in a medical system, a stream or sequence of depth camera captures are performed. The fitting of the patient model is divided between different times or parts of the sequence, using the streaming capture to distribute processing and account for patient movement. Less manual involvement may be needed due to the regular availability of image captures. Subsequent fitting may benefit from previous fitting.

A method of scanning an oral cavity including: acquiring, using an intraoral scanner (IOS) head, without changing a position of the IOS head, a first image of a first region of interest (ROI) and a second image of a second ROI where the first and the second ROIs are of different portions of a dental arch of the oral cavity and do not overlap; reconstructing depth information for the first and the second ROI; and generating a single model of the dental arch by combing the depth information.

A method includes, in a processor, receiving example representations of geometrical shapes of a given type of organ. In a training phase, a neural network model is trained using the example representations. In a modeling phase, the trained neural network model is applied to a set of location measurements acquired in an organ of the given type, to produce a three-dimensional model of the organ.

An edge data network for providing a three-dimensional (3D) character image to a user equipment and an operating method thereof are provided. The edge data network obtains key points information including feature point coordinates related to the body parts of a first user, from a first user equipment via a network, and obtains view points information including virtual position coordinate value information of virtual view points from which a second user views a 3D character image from a second user equipment, measures a key points similarity and a view points similarity by respectively comparing the obtained key points information and view points information with key points information and view points information cached in a data cache, and reads out a 3D character image cached in the data cache based on the measured key points similarity and the measured view points similarity, and transmits the read out 3D character image to the second user equipment.

A LIDAR device is positioned on a crane and is moved along a track to collect 3D image data of the area below the crane. The resulting image data is sent to a computing device that applies or more filtering algorithms to the image data and searches for known image shapes therein through a comparison of the image data and one or more 3D object models based on known shapes or geometric primitives. If an object is identified in the image data and is determined to be accessible, position information for that object may be sent to a device configured to control movement of the crane to grab/pick up the object.

Methods of assessing wear of an electrical insulator and rail pad of an assembled rail fastening system utilize data from a 3D scan of the system to derive measurements of the system from which wear of the side post of the electrical insulator, or wear of the rail pad beneath the rail, may be assessed.

According to some embodiments, a system, method and non-transitory computer-readable medium are provided comprising a 3D building modeling module; a memory for storing program instructions; a 3D building modeling processor, coupled to the memory, and in communication with the 3D building modeling module and operative to execute program instructions to: receive a region of interest; receive an image of the region of image from a data source; generate a surface model based on the received image including one or more buildings; generate a digital height model; decompose each building into a set of shapes; apply a correction process to the set of shapes; execute a primitive classification process to each shape; execute a fitting process to each classified shape; select a best fitting model; and generate a 3D model of each building. Numerous other aspects are provided.