Techniques are disclosed relating to arbitration for computer memory resources. In some embodiments, an apparatus includes queue circuitry that implements multiple queues configured to queue requests to access a memory bus. Control circuitry may, in response to detecting a first threshold condition associated with the queue circuitry, generate a first snapshot that indicates numbers of requests in respective queues of the multiple queues at a first time. The control circuitry may generate a second snapshot that indicates numbers of requests in respective queues of the multiple queues at a second time that is subsequent to the first time. The control circuitry may arbitrate between requests from the multiple queues to select requests to access the memory bus, where the arbitration is based on snapshots to which requests from the multiple queues belong. Disclosed techniques may approximate age-based scheduling while reducing area and power consumption.

Techniques are disclosed relating to arbitration for computer memory resources. In some embodiments, an apparatus includes queue circuitry that implements multiple queues configured to queue requests to access a memory bus. Control circuitry may, in response to detecting a first threshold condition associated with the queue circuitry, generate a first snapshot that indicates numbers of requests in respective queues of the multiple queues at a first time. The control circuitry may generate a second snapshot that indicates numbers of requests in respective queues of the multiple queues at a second time that is subsequent to the first time. The control circuitry may arbitrate between requests from the multiple queues to select requests to access the memory bus, where the arbitration is based on snapshots to which requests from the multiple queues belong. Disclosed techniques may approximate age-based scheduling while reducing area and power consumption.

A method may include: receiving facial image of the patient that depicts the patient's teeth; receiving a 3D model of the patient's teeth; determining color palette of the depiction of the patient's teeth; coding 3D model of the patient's teeth based on attributes of the 3D model; providing the 3D model, the color palette, and the coded 3D model to a neural network; processing the 3D model, the color palette, and the coded 3D model by the neural network to generate a processed image of the patient's teeth; simulating specular highlights on the processed image of the patient's teeth; and inserting the processed image of the patient's teeth into a mouth opening of the facial image.

A method may include: receiving facial image of the patient that depicts the patient's teeth; receiving a 3D model of the patient's teeth; determining color palette of the depiction of the patient's teeth; coding 3D model of the patient's teeth based on attributes of the 3D model; providing the 3D model, the color palette, and the coded 3D model to a neural network; processing the 3D model, the color palette, and the coded 3D model by the neural network to generate a processed image of the patient's teeth; simulating specular highlights on the processed image of the patient's teeth; and inserting the processed image of the patient's teeth into a mouth opening of the facial image.

In one embodiment, a method is for rendering medical volumetric images from received volumetric data, using a cinematic rendering approach, based on a Monte Carlo path tracing algorithm (MCPT). The MCPT algorithm uses at least one microfacet-based bidirectional reflectance distribution function (BRDF) for computing a probability how light is reflected at an implicit surface which is used for shading the implicit surface. In one embodiment, the method includes detecting if a surface scatter event is triggered. If yes, the method includes modifying the computation of a local gradient in the BRDF by perturbing the respective received volumetric data by applying a noise function for simulating a roughness of the implicit surface; and shading the implicit surfaces for rendering the received volumetric data.

In one embodiment, a method is for rendering medical volumetric images from received volumetric data, using a cinematic rendering approach, based on a Monte Carlo path tracing algorithm (MCPT). The MCPT algorithm uses at least one microfacet-based bidirectional reflectance distribution function (BRDF) for computing a probability how light is reflected at an implicit surface which is used for shading the implicit surface. In one embodiment, the method includes detecting if a surface scatter event is triggered. If yes, the method includes modifying the computation of a local gradient in the BRDF by perturbing the respective received volumetric data by applying a noise function for simulating a roughness of the implicit surface; and shading the implicit surfaces for rendering the received volumetric data.

A three-dimensional (3D) rendering method includes extracting sample points from a 3D scene, acquiring rendering result information for the sample points by rendering the sample points, and generating a rendering result image corresponding to an entire rendering based on the rendering result information for the sample points and feature information of the 3D scene.

A three-dimensional (3D) rendering method includes extracting sample points from a 3D scene, acquiring rendering result information for the sample points by rendering the sample points, and generating a rendering result image corresponding to an entire rendering based on the rendering result information for the sample points and feature information of the 3D scene.

A method may include: receiving facial image of the patient that depicts the patient's teeth; receiving a 3D model of the patient's teeth; determining color palette of the depiction of the patient's teeth; coding 3D model of the patient's teeth based on attributes of the 3D model; providing the 3D model, the color palette, and the coded 3D model to a neural network; processing the 3D model, the color palette, and the coded 3D model by the neural network to generate a processed image of the patient's teeth; simulating specular highlights on the processed image of the patient's teeth; and inserting the processed image of the patient's teeth into a mouth opening of the facial image.

A method may include: receiving facial image of the patient that depicts the patient's teeth; receiving a 3D model of the patient's teeth; determining color palette of the depiction of the patient's teeth; coding 3D model of the patient's teeth based on attributes of the 3D model; providing the 3D model, the color palette, and the coded 3D model to a neural network; processing the 3D model, the color palette, and the coded 3D model by the neural network to generate a processed image of the patient's teeth; simulating specular highlights on the processed image of the patient's teeth; and inserting the processed image of the patient's teeth into a mouth opening of the facial image.