The present disclosure provides a method for correcting and encrypting space coordinates of a three-dimensional model. The method for correcting space coordinates of a three-dimensional model includes: step S1, reading information of an original coordinate frame of a three-dimensional model in a first format and the origin of coordinates of the model; reading information of nodes from three-dimensional model data in the first format, and calculating original coordinates of the nodes; step S2, calculating parameters of correction between the original coordinate frame and a target coordinate frame based on space coordinates of four or more control points in the original coordinate frame in the first format and corresponding space coordinates of the control points in the target coordinate frame in a second format, and constructing a space coordinate correction matrix; step S3, transforming and correcting the coordinates of the origin and nodes of the three-dimensional model in the first format one by one by using the space coordinate correction matrix to obtain information of coordinate points of the three-dimensional model in the second format; and step S4, storing a file of the three-dimensional model in the second format with corrected space coordinates. Thus, the production efficiency is improved.

Computer vision systems and methods for generating building models using three-dimensional sensing and augmented reality (AR) techniques are provided. Image frames including images of a structure to be modeled are captured by a camera of a mobile device such as a smart phone, as well as three-dimensional data corresponding to the image frames. An object of interest, such as a structural feature of the building, is detected using both the image frames and the three-dimensional data. An AR icon is determined based upon the type of object detected, and is displayed on the mobile device superimposed on the image frames. The user can manipulate the AR icon to better fit or match the object of interest in the image frames, and can capture the object of interest using a capture icon displayed on the display of the mobile device.

A process for creating, responsive to a desired analysis, estimate information from received imagery by determining whether the imagery meets a minimum criterion for creating a multi-dimensional model that can address the desired analysis and building an aggregate set of images meeting the minimum criterion from which a multi-dimensional model is then built and an estimate responsive to the desired analysis is derived. The multi-dimensional model, and the responsive estimate, may be refined as additional images are provided to the aggregate set. Desired analyses may include dimensions such as square footage of a roof for building objects.

The present disclosure relates to an extended reality (XR)-based supervision assisting device, which finds a construction error by comparing a real design object photographed by a camera of the supervision assisting device with building information modelling (BIM) design data, and corrects the BIM design data to fit the real design object when the construction error between the real design object and the BIM design data exceeds a preset error tolerance range, thereby adjusting the BIM design data according to the construction error.

An Artificial Intelligence (Al) three-dimensional modeling system that analyzes and segments imagery of a room, generates a three-dimensional model of the room from the segmented imagery, identifies objects within the room, and conducts an assessment of the room based on the identified objects.

An arrangement for the relocating of virtual object images within a real non electronic space including a virtual object image creator adapted to project a plurality of virtual object images in a real non electronic space and at least one activatable tangible object, wherein the or each activatable tangible object is locatable within a corresponding virtual object image created by the virtual object image creator such that upon activation of the activatable tangible object by a user when the activatable tangible object is located within a virtual object image allows physical movement of the virtual object image within the real non electronic space that corresponds with physical movement and location of the activated activatable tangible object.

Methods and systems provide a computer technologic enhanced experience to a user when the user visits an indoor environment. A method may include computer readable instructions for identifying and tracking the user in the indoor environment using an indoor positioning system. An indoor positioning system may include a leaky feeder cable network, a plurality of Wi-Fi access points and location tracking using triangulation and Tine-to-Flight calculations. Further, based on the tracking of the user, the user is provided with an augmented navigation route for navigating in the indoor environment. The optimized navigation route is displayed on a virtual 3D model of the indoor environment. Further, the method comprises providing augmented item list to the user while navigating in the indoor environment, such that the augmented item list is generated via the use of advanced analytics, AI/Machine learning capabilities and computer vision based machine learning model for object tracking and recognition.

A system for generating three-dimensional models of an exterior physical environment such as the exterior of a building. In some cases, the system may utilize third-party data to supplement data captured at the physical environment when generating the three-dimensional models. The system may also utilize third-party data to assist with aligning models of an exterior of a building with an interior of the building.

A method of automatically producing maps and measures that visualize and quantify placement and progress of construction elements, such as walls, ducts, etc. in images. From a set of images depicting a scene, element confidences per pixel in each of the images are produced using a classification model that assigns such confidences. Thereafter, element confidences for each respective one of a set of 3D points represented in the scene are determined by aggregating the per-pixel element confidences from corresponding pixels of each of the images that is known to observe the respective 3D points. These element confidences are then updated based on primitive templates representing element geometry to produce a 3D progress model of the scene.

High-fidelity three-dimensional (3D) models and other high-fidelity digital media that depict objects with a high-level of detail may be computationally demanding to display on some devices. According to some embodiments of the present disclosure, digital media may be supplemented with one or more 3D models to improve the overall level of detail provided by the digital media without excessively increasing computational requirements. An example computer-implemented method includes instructing a user device to display digital media depicting an object, receiving an indication selecting a region of the depicted object, and instructing the user device to display a 3D model corresponding to the selected region of the depicted object, where the 3D model is different from the digital media.