Abstract: Embodiments of the present disclosure relate to neural components for differentiable ray tracing of radio propagation. Differentiable ray tracing may be used to refine the scene geometry of the physical environment, to learn or optimize the scene properties of objects in the scene, to learn or optimize the scene properties of antennas, and to learn or optimize antenna patterns, array geometries, and orientations and positions of transmitters and receivers. Once scene properties have been learned or optimized, the differentiable ray tracer may further be used to simulate the performance of different configurations of the transmitters, receivers, and scene geometry. In an embodiment, one or more of the scene geometry, scene properties, and antenna characteristics are computed by a differentiable parametric function, such as a neural network, etc. and parameters of the differentiable parametric function are learned using the differentiable ray tracing.

Abstract: In various examples, images (e.g., novel views) of an object may be rendered using an optimized number of samples of a 3D representation of the object. The optimized number of the samples may be determined based at least on casting rays into a scene that includes the 3D representation of the object and/or an acceleration data structure corresponding to the object. The acceleration data structure may include features corresponding to characteristics of the object, and the features may be indicative of the number of samples to be obtained from various portions of the 3D representation of the object to render the images. In some examples, the 3D representation may be a neural radiance field that includes, as a neural output, a spatially varying kernel size predicting the characteristics of the object, and the features of the acceleration data structure may be related to the spatially varying kernel size.

Abstract: A computer-implemented inverse rendering method comprising: computing an adjoint image by differentiating an objective function that evaluates the quality of a rendered image, image elements of the adjoint image encoding the sensitivity of spatially corresponding image elements of the rendered image with respect to the objective function; sampling the adjoint image at a respective sample position to determine a respective adjoint radiance value associated with the respective sample position; emitting the respective adjoint radiance value into a scene model characterised by scene parameters; determining an interaction location of a respective incident adjoint radiance value with a surface and/or volume of the scene model; determining a respective incident radiance value or an approximation thereof at the interaction location; and updating a scene parameter gradient.

Abstract: Embodiments of the present disclosure relate to learning digital twins of radio environments. Differentiable ray tracing may be used to refine the scene geometry of the physical environment, to learn or optimize the scene properties of objects in the scene, to learn or optimize the scene properties of antennas, and to learn or optimize antenna patterns, array geometries, and orientations and positions of transmitters and receivers. Once scene properties have been learned or optimized, the differentiable ray tracer may further be used to simulate radio wave propagation to simulate the performance of different configurations of the scene geometry and radio devices, such as antennas. In an embodiment, one or more of the scene geometry, scene properties, and antenna characteristics are computed by a differentiable parametric function, such as a neural network, etc. and parameters of the differentiable parametric function are learned using the differentiable ray tracing.

Abstract: A computer-implemented inverse rendering method comprising: computing an adjoint image by differentiating an objective function that evaluates the quality of a rendered image, image elements of the adjoint image encoding the sensitivity of spatially corresponding image elements of the rendered image with respect to the objective function; sampling the adjoint image at a respective sample position to determine a respective adjoint radiance value associated with the respective sample position; emitting the respective adjoint radiance value into a scene model characterised by scene parameters; determining an interaction location of a respective incident adjoint radiance value with a surface and/or volume of the scene model; determining a respective incident radiance value or an approximation thereof at the interaction location; and updating a scene parameter gradient.