Abstract: A method for retrieval of multi spectral bidirectional reflectance distribution function (BRDF) parameters by using red-green-blue-depth (RGB-D) data includes capturing, by an RGB-D camera, at least one image of one or more objects in a scene. The captured at least one image of the one or more objects includes RGB-D data including color and geometry information of the objects. A processing unit reconstructs the captured at least one image of the one or more objects to one or more 3D reconstructions by using the RGB-D data. A deep neural network classifies the BRDF of a surface of the one or more objects based on the 3D reconstructions. The deep neural network includes an input layer, an output layer, and at least one hidden layer between the input layer and the output layer. The multi spectral BRDF parameters are retrieved by approximating the classified BRDF by using an iterative optimization method.

Abstract: A computing platform for simulating an industrial system and a method of managing the simulation are disclosed. The method relates to generating at least one instance of a simulation model of the industrial system. The method includes: generating the instance using a contiguous memory allocator for a dynamic memory region associated with a first processor of the heterogenous processors; enabling a second processor to use the instance in the dynamic memory region based on a memory pointer associated with the address of a copy of the instance; and simulating the industrial system by the second processor by accessing the instance-copy.

Abstract: A method for retrieval of multi spectral bidirectional reflectance distribution function (BRDF) parameters by using red-green-blue-depth (RGB-D) data includes capturing, by an RGB-D camera, at least one image of one or more objects in a scene. The captured at least one image of the one or more objects includes RGB-D data including color and geometry information of the objects. A processing unit reconstructs the captured at least one image of the one or more objects to one or more 3D reconstructions by using the RGB-D data. A deep neural network classifies the BRDF of a surface of the one or more objects based on the 3D reconstructions. The deep neural network includes an input layer, an output layer, and at least one hidden layer between the input layer and the output layer. The multi spectral BRDF parameters are retrieved by approximating the classified BRDF by using an iterative optimization method.

Abstract: An object in a virtual three-dimensional space is rendered. A first plurality of pixel values of a first image captured by a camera in real space is obtained, The first plurality of pixel values are associated with a first three-dimensional position, in the virtual three-dimensional space, of the camera when the first image was captured. Each one of the first plurality of pixel values are associated with an orientation in the virtual three-dimensional space, of a portion of a light field represented by the pixel. Using compressive sensing and based on the first plurality of pixel values, a light field value for at least one second position, different from the first position, is estimated in the virtual three-dimensional space. An object is rendered in the virtual three-dimensional space, located at the second position, using the estimated light field value.

Abstract: An object in a virtual three-dimensional space is rendered. A first plurality of pixel values of a first image captured by a camera in real space is obtained, The first plurality of pixel values are associated with a first three-dimensional position, in the virtual three-dimensional space, of the camera when the first image was captured. Each one of the first plurality of pixel values are associated with an orientation in the virtual three-dimensional space, of a portion of a light field represented by the pixel. Using compressive sensing and based on the first plurality of pixel values, a light field value for at least one second position, different from the first position, is estimated in the virtual three-dimensional space. An object is rendered in the virtual three-dimensional space, located at the second position, using the estimated light field value.

Abstract: A computer-implemented method for performing compressive sensing of light transport matrix includes receiving a 3D dataset comprising image volume data and randomly selecting a plurality of points on a space curve traversing the 3D dataset. A light transport matrix comprising a plurality of light transmittance values is calculated. Each light transmittance value corresponds to light transmittance between a pair of points included in the plurality of points. An optimization problem is solved to determine a plurality of sparse coefficients which reproduce the light transport matrix when the sparse coefficients are multiplied by a predetermined dictionary of basis vectors. Once determined, the sparse coefficients are stored on a computer-readable medium.