Abstract: This application discloses techniques for generating and querying projective hash maps. More specifically, projective hash maps can be used for spatial hashing of data related to N-dimensional points. Each point is projected onto a projection surface to convert the three-dimensional (3D) coordinates for the point to two-dimensional (2D) coordinates associated with the projection surface. Hash values based on the 2D coordinates are then used as an index to store data in the projective hash map. Utilizing the 2D coordinates rather than the 3D coordinates allows for more efficient searches to be performed to locate points in the 3D space. In particular, projective hash maps can be utilized by graphics applications for generating images, and the improved efficiency can, for example, enable a game streaming application on a server to render images transmitted to a user device via a network at faster frame rates.

Abstract: In some embodiments, a given frame or picture may have different shading rates. In one embodiment in some areas of the frame or picture the shading rate may be less than once per pixel and in other places it may be once per pixel. Examples where the shading rate may be reduced include areas where there is motion and camera defocus, areas of peripheral blur, and in general, any case where the visibility is reduced anyway. The shading rate may be changed in a region, such as a shading quad, by changing the size of the region.

Abstract: A neural network-based rendering technique increases temporal stability and image fidelity of low sample count path tracing by optimizing a distribution of samples for rendering each image in a sequence. A sample predictor neural network learns spatio-temporal sampling strategies such as placing more samples in dis-occluded regions and tracking specular highlights. Temporal feedback enables a denoiser neural network to boost the effective input sample count and increases temporal stability. The initial uniform sampling step typically present in adaptive sampling algorithms is not needed. The sample predictor and denoiser operate at interactive rates to achieve significantly improved image quality and temporal stability compared with conventional adaptive sampling techniques.

Abstract: A method, computer readable medium, and system are disclosed for temporally stable data reconstruction. A sequence of input data including artifacts is received. A first input data frame is processed using layers of a neural network model to produce external state including a reconstructed first data frame that approximates the first input data frame without artifacts. Hidden state generated during processing of the first input data is not provided as an input to the layer to process second input data. The external state is warped, using difference data corresponding to changes between input data frames, to produce warped external state more closely aligned with the second input data frame. The second input data frame is processed, based on the warped external state, using the layers of the neural network model to produce a reconstructed second data frame that approximates the second data frame without artifacts.

Abstract: An algorithm and associated set of rules enable a given polygon micro-mesh type to always be able to represent a more compressed micro-mesh type. These rules, in conjunction with additional constraints on the order used to encode displaced micro-meshes, enable lossy compression techniques to efficiently store geometric displacements as a parallel algorithm, with little communication required among independently compressed displaced micro-meshes, while guaranteeing high quality watertight (crack-free) results for vector displacements, triangle textures, and ray and path tracing.

Abstract: A Displaced Micro-mesh (DMM) primitive enables high complexity geometry for ray and path tracing while minimizing the associated builder costs and preserving high efficiency. A structured, hierarchical representation implicitly encodes vertex positions of a triangle micro-mesh based on a barycentric grid, and enables microvertex displacements to be encoded efficiently (e.g., as scalars linearly interpolated between minimum and maximum triangle surfaces). The resulting displaced micro-mesh primitive provides a highly compressed representation of a potentially vast number of displaced microtriangles that can be stored in a small amount of space. Improvements in ray tracing hardware permit automatic processing of such primitive for ray-geometry intersection testing by ray tracing circuits without requiring intermediate reporting to a shader.

Abstract: A Displaced Micro-mesh (DMM) primitive enables high complexity geometry for ray and path tracing while minimizing the associated builder costs and preserving high efficiency. A structured, hierarchical representation implicitly encodes vertex positions of a triangle micro-mesh based on a barycentric grid, and enables microvertex displacements to be encoded efficiently (e.g., as scalars linearly interpolated between minimum and maximum triangle surfaces). The resulting displaced micro-mesh primitive provides a highly compressed representation of a potentially vast number of displaced microtriangles that can be stored in a small amount of space. Improvements in ray tracing hardware permit automatic processing of such primitive for ray-geometry intersection testing by ray tracing circuits without requiring intermediate reporting to a shader.

Abstract: A neural network-based rendering technique increases temporal stability and image fidelity of low sample count path tracing by optimizing a distribution of samples for rendering each image in a sequence. A sample predictor neural network learns spatio-temporal sampling strategies such as placing more samples in dis-occluded regions and tracking specular highlights. Temporal feedback enables a denoiser neural network to boost the effective input sample count and increases temporal stability. The initial uniform sampling step typically present in adaptive sampling algorithms is not needed. The sample predictor and denoiser operate at interactive rates to achieve significantly improved image quality and temporal stability compared with conventional adaptive sampling techniques.

Abstract: A neural network-based rendering technique increases temporal stability and image fidelity of low sample count path tracing by optimizing a distribution of samples for rendering each image in a sequence. A sample predictor neural network learns spatio-temporal sampling strategies such as placing more samples in dis-occluded regions and tracking specular highlights. Temporal feedback enables a denoiser neural network to boost the effective input sample count and increases temporal stability. The initial uniform sampling step typically present in adaptive sampling algorithms is not needed. The sample predictor and denoiser operate at interactive rates to achieve significantly improved image quality and temporal stability compared with conventional adaptive sampling techniques.

Abstract: This application discloses techniques for generating and querying projective hash maps. More specifically, projective hash maps can be used for spatial hashing of data related to N-dimensional points. Each point is projected onto a projection surface to convert the three-dimensional (3D) coordinates for the point to two-dimensional (2D) coordinates associated with the projection surface. Hash values based on the 2D coordinates are then used as an index to store data in the projective hash map. Utilizing the 2D coordinates rather than the 3D coordinates allows for more efficient searches to be performed to locate points in the 3D space. In particular, projective hash maps can be utilized by graphics applications for generating images, and the improved efficiency can, for example, enable a game streaming application on a server to render images transmitted to a user device via a network at faster frame rates.

Abstract: A neural network-based rendering technique increases temporal stability and image fidelity of low sample count path tracing by optimizing a distribution of samples for rendering each image in a sequence. A sample predictor neural network learns spatio-temporal sampling strategies such as placing more samples in dis-occluded regions and tracking specular highlights. Temporal feedback enables a denoiser neural network to boost the effective input sample count and increases temporal stability. The initial uniform sampling step typically present in adaptive sampling algorithms is not needed. The sample predictor and denoiser operate at interactive rates to achieve significantly improved image quality and temporal stability compared with conventional adaptive sampling techniques.

Abstract: Apparatuses, systems, and techniques are presented to generate images. In at least one embodiment, one or more neural networks are used to generate one or more images using one or more pixel weights determined based, at least in part, on one or more sub-pixel offset values.

Abstract: Apparatuses, systems, and techniques are presented to generate images. In at least one embodiment, one or more neural networks are used to generate one or more images using one or more pixel weights determined based, at least in part, on one or more sub-pixel offset values.

Abstract: Apparatuses, systems, and techniques are presented to generate images. In at least one embodiment, one or more neural networks are used to generate one or more images using one or more pixel weights determined based, at least in part, on one or more sub-pixel offset values.

Abstract: Apparatuses, systems, and techniques are presented to generate images. In at least one embodiment, one or more neural networks are used to generate one or more images using one or more pixel weights.

Abstract: Apparatuses, systems, and techniques are presented to generate images. In at least one embodiment, at least a first optical flow network (OFN) and at least a first reconstruction network (RN) can be used to generate one or more images based, at least in part, upon the OFN and the RN using a shared loss function.

Abstract: A method dynamically selects one of a first sampling order and a second sampling order for a ray trace of pixels in a tile where the selection is based on a motion vector for the tile. The sampling order may be a bowtie pattern or an hourglass pattern. Subframes generated based on the sampling order are communicated over a bus along with motion vectors for tiles of the subframes.

Abstract: A method, computer readable medium, and system are disclosed for performing spatiotemporal filtering. The method includes identifying image data to be rendered, reconstructing the image data to create reconstructed image data, utilizing a filter including a neural network having one or more skip connections and one or more recurrent layers, and returning the reconstructed image data.

Abstract: A neural network structure, namely a warped external recurrent neural network, is disclosed for reconstructing images with synthesized effects. The effects can include motion blur, depth of field reconstruction (e.g., simulating lens effects), and/or anti-aliasing (e.g., removing artifacts caused by sampling frequency). The warped external recurrent neural network is not recurrent at each layer inside the neural network. Instead, the external state output by the final layer of the neural network is warped and provided as a portion of the input to the neural network for the next image in a sequence of images. In contrast, in a conventional recurrent neural network, hidden state generated at each layer is provided as a feedback input to the generating layer. The neural network can be implemented, at least in part, on a processor. In an embodiment, the neural network is implemented on at least one parallel processing unit.

Abstract: In some embodiments, a given frame or picture may have different shading rates. In one embodiment in some areas of the frame or picture the shading rate may be less than once per pixel and in other places it may be once per pixel. Examples where the shading rate may be reduced include areas where there is motion and camera defocus, areas of peripheral blur, and in general, any case where the visibility is reduced anyway. The shading rate may be changed in a region, such as a shading quad, by changing the size of the region.