4 results about "Supersampling" patented technology

A plant three-dimensional reconstruction method for a weak texture image and scale distortion comprises the following steps: step 1, recovering an initial Gaussian point cloud and camera parameters through an SfM method by using a multi-view RGB image; 2, differential rendering is executed in training iteration, a predicted image is compared with a real image, and a loss function containing various constraints is calculated to serve as a basis for gradient solving and parameter updating; 3, calculating a pixel weighted average gradient and an artifact suppression coefficient of each Gaussian in a visible view angle; 4, updating Gaussian parameters through back propagation, and adaptively triggering Gaussian cloning, splitting or deleting operation based on the pixel gradient, the covariance matrix and transparency; and 5, dynamically adjusting the Gaussian projection matrix according to the focal length and the resolution of the camera during rendering so as to maintain scale consistency, and performing anti-aliasing rendering in combination with pixel-level super-sampling. According to the method, the reconstruction precision and the structure reduction capability of the virtual plant in the weak texture region are remarkably improved.

The application discloses a global illumination neural rendering method and system based on neural field space-time domain joint super sampling, which comprises the following steps: encoding input scene dynamic information to generate global illumination information of each object; obtaining current frame position related information and converting to object neural field independent coordinate space to form object position data; performing space-time joint super sampling through a global illumination transformation network combined with historical features; performing low-resolution aggregation and global illumination decoding on the super sampling samples to obtain global illumination features of each object; combining the global illumination features of each object with current frame geometry related information to generate fusion features; encoding and decoding the fusion features in a low-resolution space and performing end upsampling to output rendering results and update historical features. The application realizes global illumination neural rendering by adopting a neural field space-time domain joint super sampling method, and has the advantages of high efficiency, good details and strong time sequence stability.

Temporally amortized supersampling in graphics processing is described. An example of an apparatus includes a computer memory to store data for processing, including graphics data for a graphical application, and one or more processors including a graphical processing unit (GPU). The GPU includes a network to perform spatiotemporal upscaling filter kernel prediction for supersampling for the graphical application, the network including an input processing stage, a neural network stage, and an output filtering stage.

Generating synthesized data includes capturing one or more frames of a scene at a first frame rate by one or more cameras of a wearable device, determining body position parameters for the frames, and obtaining geometry data for the scene in accordance with the one or more frames. The frames, body position parameters, and geometry data are applied to a trained network which predicts one or more additional frames. With respect to virtual data, generating a synthesized frame includes determining current body position parameters in accordance with the one or more frames, predicting a future gaze position based on the current body position parameters, and rendering, at a first resolution, a gaze region of a frame in accordance with the future gaze position. A peripheral region is predicted for the frame at a second resolution, and the combined regions form a frame that is used to drive a display.