Google Inc.

David Chu

Sebastian Sylvan

Hugues Hoppe

Mohammad Moharrami

Ivana Tosic Rodgers

Bernhard Reinert

Grigory Javadyan

Egor Yusov

Kelvin Ritland

A method includes obtaining a first frame of image data including a plurality of pixels. Each pixel of the plurality of pixels is associated with a respective color value and a respective depth value. The first frame renders a scene from a first point of view. The method includes generating a three-dimensional (3D) polygon mesh using the plurality of pixels and the respective depth values. The 3D polygon mesh includes a plurality of portions. Each respective portion defines a respective plurality of vertices defining a respective mesh density representative of a density of the respective plurality of vertices. Each vertex is associated with a corresponding pixel of the first frame of image data. The method includes generating a second frame of image data via reprojection using the generated 3D polygon mesh. The second frame of image data has a second point of view different from the first point of view.

Adaptive mesh reprojection for low latency 6DOF rendering

Systems, methods, and computer program products are described for receiving a request for a head pose prediction for an augmented reality experience, identifying at least one positional indicator and at least one rotational indicator associated with the augmented reality experience, and providing the at least one positional indicator and the at least one rotational indicator to a Recurrent Neural Network (RNN) comprising a plurality of cells. The RNN may include a plurality of recurrent steps that each include at least one of the plurality of cells and at least one fully connected (FC) layer. The RNN may be used to generate at least one pose prediction corresponding to head pose changes for the augmented reality experience for at least one upcoming time period, provide the at least one pose prediction and trigger display of augmented reality content based on the at least one pose prediction.

Pose prediction with recurrent neural networks

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Seattle, WA, United States of America

David Chu