Abstract: A scene modeling system accesses a three-dimensional (3D) scene including a 3D object. The scene modeling system applies a silhouette bidirectional texture function (SBTF) model to the 3D object to generate an output image of a textured material rendered as a surface of the 3D object. Applying the SBTF model includes determining a bounding geometry for the surface of the 3D object. Applying the SBTF model includes determining, for each pixel of the output image, a pixel value based on the bounding geometry. The scene modeling system displays, via a user interface, the output image based on the determined pixel values.

Abstract: Methods and systems disclosed herein relate generally to surface-rendering neural networks to represent and render a variety of material appearances (e.g., textured surfaces) at different scales. The system includes receiving image metadata for a texel that includes position, incoming and outgoing radiance direction, and a kernel size. The system applies a offset-prediction neural network to the query to identify an offset coordinate for the texel. The system inputs the offset coordinate to a data structure to determine a feature vector for the texel of the textured surface. The reflectance feature vector is then processed using a decoder neural network to estimate a light-reflectance value of the texel, at which the light-reflectance value is used to render the texel of the textured surface.

Abstract: A diaphragm for performing image position correction of a virtual image projectable onto a windshield of a vehicle by a head-up display. A first positioning mark, a second positioning mark, and a calibration mark are attached to a disk of the diaphragm. The disk is transparent at least in the region of the first positioning mark, the second positioning mark, and the calibration mark, so that a first image including the first positioning mark and a first positioning pattern recorded directly through the disk and output on the vehicle side, a second image including the second positioning mark and a second positioning pattern output on the vehicle side, mirrored by a mirror of the diaphragm and recorded through the disk.

Abstract: Methods and systems disclosed herein relate generally to surface-rendering neural networks to represent and render a variety of material appearances (e.g., textured surfaces) at different scales. The system includes receiving image metadata for a texel that includes position, incoming and outgoing radiance direction, and a kernel size. The system applies a offset-prediction neural network to the query to identify an offset coordinate for the texel. The system inputs the offset coordinate to a data structure to determine a feature vector for the texel of the textured surface. The reflectance feature vector is then processed using a decoder neural network to estimate a light-reflectance value of the texel, at which the light-reflectance value is used to render the texel of the textured surface.

Abstract: A diaphragm for performing image position correction of a virtual image projectable onto a windshield of a vehicle by a head-up display. A first positioning mark, a second positioning mark, and a calibration mark are attached to a disk of the diaphragm. The disk is transparent at least in the region of the first positioning mark, the second positioning mark, and the calibration mark, so that a first image including the first positioning mark and a first positioning pattern recorded directly through the disk and output on the vehicle side, a second image including the second positioning mark and a second positioning pattern output on the vehicle side, mirrored by a mirror of the diaphragm and recorded through the disk.

Abstract: Images are rendered from deeply learned raytracing parameters. Active learning, via a machine learning (ML) model (e.g., implemented by a deep neural network), is used to automatically determine, infer, and/or predict optimized, or at least somewhat optimized, values for parameters used in raytracing methods. Utilizing deep learning to determine optimized, or at least somewhat optimized, values for raytracing parameters is in contrast to conventional methods, which require users to rely of heuristics for parameter value setting. In various embodiments, one or more parameters regarding the termination and splitting of traced light paths in stochastic-based (e.g., Monte Carlo) raytracing are determined via active learning. In some embodiments, one or more parameters regarding the sampling rate of shadow rays are also determined.

Abstract: Images are rendered from deeply learned raytracing parameters. Active learning, via a machine learning (ML) model (e.g., implemented by a deep neural network), is used to automatically determine, infer, and/or predict optimized, or at least somewhat optimized, values for parameters used in raytracing methods. Utilizing deep learning to determine optimized, or at least somewhat optimized, values for raytracing parameters is in contrast to conventional methods, which require users to rely of heuristics for parameter value setting. In various embodiments, one or more parameters regarding the termination and splitting of traced light paths in stochastic-based (e.g., Monte Carlo) raytracing are determined via active learning. In some embodiments, one or more parameters regarding the sampling rate of shadow rays are also determined.

Abstract: The present invention is related to a luminescent material generated by polymerization of a pyrromethene complex by glow discharge. The polymer material of the present invention exhibits semi-conductive properties and has a luminescence maximum in a spectrum region in the range of about 540 nm to about 585 nm with a half-width of the luminescence band in the range of about 55 nm to about 75 nm, a quantum yield of photoluminescence in the range of about 0.6 to about 0.8, and an electric conductivity at a temperature of about 20° C. in the range of about 1×10?10 S/cm to about 5×10?10 S/cm. The resultant polymer layer has a thickness in the range of about 0.01 ?m to about 10 ?m on a substrate placed between or on any of the electrodes. The starting pyrromethene complex may be a 1,3,5,7,8-pentamethyl-2,6-diethylpyrromethene difluoroborate complex (pyrromethene 567).

Abstract: The present invention is related to a thermostable electroconductive polymer layer comprising an iodine-doped polymer layer based on 1 -amino-9,1 0-anthraquinone having an electrical conductivity in the range of about 10?2 Sm/cm to about 102 Sm/cm. Additionally, the present invention is related to methods for preparing the thermostable electroconductive polymer layer. The method comprises polymerizing 1 -amino-9,10-anthraquinone vapors at a reduced pressure in a direct current discharge on a cathode at a temperature in the range of about 150° C. to about 300° C., providing the necessary vapor pressure for a time period in the range of about 5 minutes to about 30 minutes and doping the prepared layer with iodine vapors.