8 results about "Warping function" patented technology

An example computing device may include memory and one or more processors. The one or more processors may be configured to parallel entropy decode encoded video data from a received bitstream to generate entropy decoded data. The one or more processors may be configured to predict a motion vector based on the entropy decoded data. The one or more processors may be configured to decode a motion vector residual from the entropy decoded data. The one or more processors may be configured to add the motion vector residual and motion vector. The one or more processors may be configured to warp previous reconstructed video data with an overlapped block-based warp function using the motion vector to generate predicted current video data. The one or more processors may be configured to sum the predicted current video data with a residual block to generate current reconstructed video data.

Methods and computer systems for methods, computer systems, and computer-readable memory media for determining a warp function. An as-designed watertight spline model of an object is received. A set of sweep points along a sweep trajectory for manufacturing the object are used to determine a sweep surface. Model points are sampled from the as-designed watertight spline model and projected onto nearest as-executed points on the sweep surface. The projected as-executed points are used to construct an as-executed model of the object. A warp function is determined based on a difference between the as-designed watertight spline model and the as-executed model. The warp function is a continuous function approximating differences between the as-designed model and the as-manufactured object. The warp function is stored in a non-transitory computer-readable memory medium.

The invention relates to the technical field of computer vision, and provides an asynchronous space warping function evaluation method and device, equipment, a medium and a product. The method comprises the following steps: collecting a plurality of evaluation parameters in a first dynamic scene; determining a plurality of evaluation indexes based on the plurality of evaluation parameters, wherein the plurality of evaluation indexes at least comprise a motion vector residual error and a geometric distortion residual error; and evaluating the asynchronous spatial warping function according to the plurality of evaluation indexes to obtain a first evaluation result. According to the method, the motion prediction precision and the geometric deformation matching degree of the asynchronous spatial warping function are reflected through the residual error generated by the motion vector field and geometric warping, and the accuracy of the asynchronous spatial warping function is evaluated through the key performance of the asynchronous spatial warping function.

A method includes obtaining an image that includes a plurality of pixels. The method includes identifying a first subset of the plurality of pixels that defines a particular geometry that a user of the electronic device is currently focusing on. The method includes warping the first subset of the plurality of pixels according to a first warping function that is based on a movement of the electronic device. The method includes warping a second subset of the plurality of pixels, that is different from the first subset, according to a second warping function that is different from the first warping function.

An example computing device may include a memory and one or more processors. The one or more processors may be configured to perform parallel entropy decoding of the encoded video data from the received bitstream to generate entropy decoded data. The one or more processors may be configured to predict a motion vector based on the entropy decoded data. The one or more processors may be configured to decode a motion vector residual from the entropy decoded data. The one or more processors may be configured to add the motion vector residual to the motion vector. The one or more processors may be configured to warp previously reconstructed video data using the motion vector with a warp function based on overlapping blocks to generate predicted current video data. The one or more processors may be configured to sum the predicted current video data with the residual block to generate current reconstructed video data.

A network device (110) generates (810) video data representing a viewing frustrum of a three-dimensional scene. A plurality of virtual objects are within the viewing frustrum. The network device (110) transmits (820) pose information and the video data to a computing device (120) over a first transport channel (130a) and a second transport channel (130b), respectively. The first transport channel (130a) has lower latency characteristics than the second transport channel (130b) and the pose information comprises a pose of a virtual object within the viewing frustrum. The computing device (120) receives (860), from the network device (110), the pose information and video data over the first transport channel (130a) and the second transport channel (130b), respectively. The computing device (120) predicts (870) a newer pose of the virtual object from the pose information and generates (880) a two-dimensional image using the predicted pose and the video data as inputs to a warping function.

A method of decoding an image according to a present disclosure, the method includes decoding a Gaussian parameter and Gaussian embedding for a first Gaussian in a canonical space; obtaining a variation of a Gaussian parameter for the first Gaussian by inputting the Gaussian embedding into a deformation function; and reconstructing a second Gaussian at a target timestamp by applying the variation to the Gaussian parameter for the first Gaussian, wherein the Gaussian embedding is derived individually for each Gaussian.