Microsoft Technology Licensing, LLC

Steve Sullivan

Alvaro Collet Romea

Ming Chuang

Pat Sweeney

Adam G Kirk

Don Gillett

A computing system is configured for context-adaptive allocation of render model resources that may sacrifice some level of detail in a computational description of a 3D scene before rendering in order to accommodate resource limitations in a rendering environment such as available processor cycles, and/or bandwidth for data transmission to a processor. Such resource limitations can often preclude rendering a richly detailed 3D scene, particularly in full-motion and/or in real time. An importance function describing the relative perceptual importance of elements that make up the 3D scene is utilized to enable resources to be adaptively allocated so that more resources go to visual elements of the 3D scene that have a higher perceptual importance. The rendered output may thus optimize visual fidelity for the computational description within the resource constrained rendering environment.

Context-adaptive allocation of render model resources

Consistent tessellation via topology-aware surface tracking is provided in which a series of meshes is approximated by taking one or more meshes from the series and calculating a transformation field to transform the keyframe mesh into each mesh of the series, and substituting the transformed keyframe meshes for the original meshes. The keyframe mesh may be selected advisedly based upon a scoring metric. An error measurement on the transformed keyframe exceeding tolerance or threshold may suggest another keyframe be selected for one or more frames in the series. The sequence of frames may be divided into a number of subsequences to permit parallel processing, including two or more recursive levels of keyframe substitution. The transformed keyframe meshes achieve more consistent tessellation of the object across the series.

Consistent tessellation via topology-aware surface tracking

A three-dimensional (3D) scene is computationally reconstructed using a combination of plural modeling techniques. Point clouds representing an object in the 3D scene are generated by different modeled techniques and each point is encoded with a confidence value which reflects a degree of accuracy in describing the surface of the object in the 3D scene based on strengths and weaknesses of each modeling technique. The point clouds are merged in which a point for each location on the object is selected according to the modeling technique that provides the highest confidence.

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Steve Sullivan