-
公开(公告)号:US10991079B2
公开(公告)日:2021-04-27
申请号:US16530854
申请日:2019-08-02
申请人: Nvidia Corporation
发明人: Shiqiu Liu , Jacopo Pantaleoni
摘要: This disclosure presents a method to denoise a ray traced scene where the ray tracing uses a minimal number of rays. The method can use temporal reprojections to compute a weighted average to the scene data. A spatial filter can be run on the scene data, using the temporal reprojection count to reduce the size of the utilized spatial filter radius. In some aspects, additional temporal filters can be applied to the scene data. In some aspects, global illumination temporal reprojection history counts can be used to modify the spatial filter radius. In some aspects, caustic photon tracing can be conducted to compute a logarithmic cost, which can then be utilized to reduce the denoising radius used by the spatial filter. The modified and adjusted scene data can be sent to a rendering process to complete the rendering to generate a final scene.
-
公开(公告)号:US20200058105A1
公开(公告)日:2020-02-20
申请号:US16540946
申请日:2019-08-14
申请人: NVIDIA Corporation
发明人: Shiqiu Liu , Jacopo Pantaleoni
摘要: Various approaches are disclosed to temporally and spatially filter noisy image data—generated using one or more ray-tracing effects—in a graphically rendered image. Rather than fully sampling data values using spatial filters, the data values may be sparsely sampled using filter taps within the spatial filters. To account for the sparse sampling, locations of filter taps may be jittered spatially and/or temporally. For filtering efficiency, a size of a spatial filter may be reduced when historical data values are used to temporally filter pixels. Further, data values filtered using a temporal filter may be clamped to avoid ghosting. For further filtering efficiency, a spatial filter may be applied as a separable filter in which the filtering for a filter direction may be performed over multiple iterations using reducing filter widths, decreasing the chance of visual artifacts when the spatial filter does not follow a true Gaussian distribution.
-