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: An apparatus and method are described for asynchronous texel shading. For example, one embodiment of a graphics processing apparatus comprises: a first shader to perform shading operations on a plurality of pixels in a first pass and to submit a request to shade texels; and a texel shader to responsively perform texel shading operations in response to the request from the first shader, the texel shader to write results to a procedural texture stored in a memory subsystem, the procedural texture to be read during a second pass by the first shader or another 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: A method, computer readable medium, and system are disclosed for training a neural network. The method includes the steps of selecting an input sample from a set of training data that includes input samples and noisy target samples, where the input samples and the noisy target samples each correspond to a latent, clean target sample. The input sample is processed by a neural network model to produce an output and a noisy target sample is selected from the set of training data, where the noisy target samples have a distribution relative to the latent, clean target sample. The method also includes adjusting parameter values of the neural network model to reduce differences between the output and the noisy target sample.

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: An apparatus and method are described for asynchronous texel shading. For example, one embodiment of a graphics processing apparatus comprises: a first shader to perform shading operations on a plurality of pixels in a first pass and to submit a request to shade texels; and a texel shader to responsively perform texel shading operations in response to the request from the first shader, the texel shader to write results to a procedural texture stored in a memory subsystem, the procedural texture to be read during a second pass by the first shader or another shader.

Abstract: A method, computer readable medium, and system are disclosed for training a neural network. The method includes the steps of selecting an input sample from a set of training data that includes input samples and noisy target samples, where the input samples and the noisy target samples each correspond to a latent, clean target sample. The input sample is processed by a neural network model to produce an output and a noisy target sample is selected from the set of training data, where the noisy target samples have a distribution relative to the latent, clean target sample. The method also includes adjusting parameter values of the neural network model to reduce differences between the output and the noisy target sample.

Abstract: A method, computer readable medium, and system are disclosed for training a neural network model. The method includes the step of selecting an input vector from a set of training data that includes input vectors and sparse target vectors, where each sparse target vector includes target data corresponding to a subset of samples within an output vector of the neural network model. The method also includes the steps of processing the input vector by the neural network model to produce output data for the samples within the output vector and adjusting parameter values of the neural network model to reduce differences between the output vector and the sparse target vector for the subset of the samples.

Abstract: A graphics processing apparatus and method are described.

Abstract: An algorithm may reconstruct defocus blur from a sparsely sampled light field. Light field samples are generated, using stochastic rasterization or ray tracing as examples. Then the samples are partitioned into depth layers. These depth layers are filtered independently and then combined together, taking into account inter-layer visibility. Since each layer corresponds to a smaller depth range, it results in more effective reconstruction filters than previous approaches.

Abstract: A computer graphics processor and a method for rendering a three-dimensional image on a display screen. The computer graphics processor comprises a rasterizer configured to perform pixel traversal of a primitive after projection of the primitive. Furthermore, the rasterizer is configured to perform the pixel traversal of a first primitive for a plurality of views prior to performing pixel traversal of a next primitive for one or several views.

Abstract: A computer graphics processor and a method for rendering a three-dimensional image on a display screen. The computer graphics processor comprises a rasterizer configured to perform pixel traversal of a primitive after projection of the primitive. Furthermore, the rasterizer is configured to perform the pixel traversal of a first primitive for a plurality of views prior to performing pixel traversal of a next primitive for one or several views.

Abstract: An algorithm may reconstruct defocus blur from a sparsely sampled light field. Light field samples are generated, using stochastic rasterization or ray tracing as examples. Then the samples are partitioned into depth layers. These depth layers are filtered independently and then combined together, taking into account inter-layer visibility. Since each layer corresponds to a smaller depth range, it results in more effective reconstruction filters than previous approaches.