In a simulation apparatus for a cockpit system of a vehicle, a driving environment simulation unit performs simulation of a driving environment of the vehicle involving a driving behavior to acquire driving behavior data and generate a travel image as an image simulating scenery visible from the vehicle during travelling. A travel image display unit displays the travel image. A component display unit displays an image of a component generated based on the driving behavior data. A processing load calculation unit calculates a processing load of the simulation apparatus based on at least one of a time difference between acquisition of the driving behavior data and display of the image of the component corresponding to the driving behavior data and a time difference between acquisition of the driving behavior data and display of the travel image generated in response to the driving behavior data.

The present invention discloses a system and method for simulating radio frequency (RF) signal propagation through a plurality of mediums using a plurality of configurable digital representations of building information model in two dimensional (2D) or augmented reality (AR) or virtual reality (VR) environments. The system comprising: a client interface, a processor and a memory. The processor comprising a building information model (BIM) generation unit, RF equipment conversion unit and a RF propagation prediction unit. The BIM generation unit is configured to translate 2D graphics into BIM data-sets, by reference to administrator input, to act as one or more parameters to control a generation of 2D or AR or VR environments. The RF equipment conversion unit is configured to transform adjunct data related to a physical configuration of one or more RF equipment into data models. The RF propagation prediction unit is configured to utilize the one or more parameters and the transformed data related to the physical configuration of the one or more RF equipment for simulating the RF signal propagation pattern predictions through the plurality of mediums.

Three-dimensional (3D) modeling systems and methods are described for automatically generating photorealistic, virtual 3D package and product models from two-dimensional (2D) imaging assets and dimensional data. The 3D modeling systems and methods include storing, by a memory with one or more processors, 2D imaging assets and dimensional datasets, obtaining, with an imaging asset manipulation script, a shape classification defining a real-world product or product package to be virtually modeled in 3D space, generating, with the imaging asset manipulation script, a spline based on an alpha channel extracted from a 2D imaging asset depicting the real-world product or package, and generating, with the imaging asset manipulation script, a parametric model based on the spline, the dimensional dataset, and the shape classification. A virtual 3D model is generated based on the parametric model and rendered, via a graphical display or environment, as a photorealistic image representing the real-world product or product package.

A material screening process of generating input features for each material of a subset of materials to be screened, generating target properties for each material of the subset of materials, inputting screening conditions, the input features, and the target properties into a material screening artificial intelligence model and training the material screening artificial intelligence model based on the inputs. Once the model is trained, inputting a dataset of materials to be screened into the trained material screening artificial intelligence model, the dataset of materials includes the subset of materials used to train the model, screening the dataset of materials on the trained material screening artificial intelligence model using the screening conditions and ranking the materials of the dataset based on predicted target properties obtained from the screening.

A computer-implemented method of machine-learning is described that includes obtaining a dataset of virtual scenes. The dataset of virtual scenes belongs to a first domain. The method further includes obtaining a test dataset of real scenes. The test dataset belongs to a second domain. The method further includes determining a third domain. The third domain is closer to the second domain than the first domain in terms of data distributions. The method further includes learning a domain-adaptive neural network based on the third domain. The domain-adaptive neural network is a neural network configured for inference of spatially reconfigurable objects in a real scene. Such a method constitutes an improved method of machine learning with a dataset of scenes including spatially reconfigurable objects.

A computer-implemented method for selecting an edge among edges of a 3D object in a 3D immersive environment of a CAD system. Each edge is oriented in the 3D immersive environment. The method comprises displaying the 3D object in the 3D immersive environment, detecting a hand gesture including all fingers folded except thumb, determining an oriented line formed with the folded fingers of the hand in the 3D immersive environment, and identifying the edge of the 3D object having the closest orientation with the oriented line.

A process and method for generation of 3D interactive rendition of space by first digitizing the space as a TXLD file from point clouds, 2-D floor plans, photographs and/or videos, and/or elevation views then processing the file to create 3D interactive renditions of the space using virtual reality, augmented reality, or mixed reality technologies, constantly growing and evolving 3D library of digital representations of both fixtures and non-fixtures based on real products, automating and personalizing product selection and placement for a given user and target environment, using an ensemble recommendation system, which relies on weighted averages of probabilistic, content-based, clustering, and collaborative filtering models, among other suitable models that may be added to the ensemble in the future. If a medium does not necessitate 3D model creation of the environment the products are automatically placed in the medium view based on the encoded position in the TXLD file.

Embodiments of the invention are directed to systems, methods, and computer program products for adaptive augmented reality for dynamic processing of spatial component parameters based on detecting accommodation factors in real time. The system is further configured for dynamic capture, analysis and modification of spatial component parameters in a virtual reality (VR) space and real-time transformation to composite plan files. Moreover, the system comprises one or more composite credential sensor devices, comprising one or more VR spatial sensor devices configured for capture and imaging of VR spatial movement and position credentials. The system is also configured to dynamically transform and adapt a first immersive virtual simulation structure associated with the first physical location sector, in real-time, based on detecting and analyzing mobility assist devices associated with users.

A system allows a user to select designs for apparel and preview these designs before manufacture. The previews provide photorealistic visualizations in two or three dimensions, and can be presented on a computer display, projected on a screen, or presented in virtual reality (e.g., via headset), or augmented reality. The system can also include a projection system that projects the new designs onto garments on mannequins in a showroom. The garments and the new designs projected onto the garments have a three-dimensional appearance as if the garments having the new designs are worn by people. Software and lasers are used in finishing the garments to produce garments with the new designs and the finished garments have an appearance of the three-dimensional appearance of the garments and new designs on the mannequins with the new designs projected onto the garments by the projection system.

Media, method and system for approximating a finite element analysis texture map for an object. To accomplish this, the object is converted to a computer generated model and finite element analysis is performed for a plurality of different simulated inputs to generate a plurality of simulated mappings. Each simulated mapping is converted into a simulated texture map. A machine learning model is trained on the simulated inputs and simulated texture maps to generate a texture map which approximates a finite element analysis. The machine learning model receives a user input and generates the texture map therefrom. The texture map is then wrapped to the object and displayed.