Abstract: Embodiments of the present disclosure provide a method and apparatus for content rendering, a device, a medium and a product. The method includes: obtaining a point cloud group to be rendered; for respective Gaussian spheres, determining a projection matrix of the Gaussian spheres in a binocular camera coordinate system corresponding to a virtual reality device; for respective pixel points in the projection area, determining a target area in the screen space corresponding to the pixel points; determining attenuation coefficients of the Gaussian spheres in the target area based on a rendering resolution corresponding to the target area, a rendering resolution corresponding to the gaze area and the projection matrix; determining rendering parameters of the pixel points according to the attenuation coefficients and color information corresponding to the Gaussian spheres, and performing a rendering operation on the pixel points based on the rendering parameters.

Abstract: The technique of dynamic mode decomposition (DMD) is disclosed herein for the purpose of robustly separating video frames into background (low-rank) and foreground (sparse) components in real-time. Foreground/background separation is achieved at the computational cost of just one singular value decomposition (SVD) and one linear equation solve, thus producing results orders of magnitude faster than robust principal component analysis (RPCA). Additional techniques, including techniques for analyzing the video for multi-resolution time-scale components, and techniques for reusing computations to allow processing of streaming video in real time, are also described herein.

Abstract: The technique of dynamic mode decomposition (DMD) is disclosed herein for the purpose of robustly separating video frames into background (low-rank) and foreground (sparse) components in real-time. Foreground/background separation is achieved at the computational cost of just one singular value decomposition (SVD) and one linear equation solve, thus producing results orders of magnitude faster than robust principal component analysis (RPCA). Additional techniques, including techniques for analyzing the video for multi-resolution time-scale components, and techniques for reusing computations to allow processing of streaming video in real time, are also described herein.