Microsoft Technology Licensing, LLC

David G Molyneaux

Shahram Izadi

Stephen Edward Hodges

Otmar Hilliges

David Kim

Jamie Daniel Joseph Shotton

Pushmeet Kohli

Andrew William Fitzgibbon

David Alexander Butler

Richard Newcombe

Nicolas Villar

Patrick Luke Olivier

James William Scott

Harper LaFave

Mike Hazas

Antonio Criminisi

Yang Liu

Fan Yang

Zicheng Liu

Xin Tong

Michael Isard

Stuart Taylor

Alexandru Octavian Balan

Jason S Flaks

John Michael Winn

Carsten Curt Eckard Rother

Oliver M Williams

Paul Ronald Barham

Sebastian Nowozin

Jamie Shotton

Jay P Kapur

Emily Yang

Eric Chang

Matthew Darius Cook

Hsiang-Tao Wu

Mat Cook

Ross Brook Girshick

Otmar Hiliges

Stefan Johannes Josef Holzer

Steve Hodges

In one or more implementations, a static geometry model is generated, from one or more images of a physical environment captured using a camera, using one or more static objects to model corresponding one or more objects in the physical environment. Interaction of a dynamic object with at least one of the static objects is identified by analyzing at least one image and a gesture is recognized from the identified interaction of the dynamic object with the at least one of the static objects to initiate an operation of the computing device.

Gesture recognition techniques

Systems and methods for reducing interference between multiple infra-red depth cameras are described. In an embodiment, the system comprises multiple infra-red sources, each of which projects a structured light pattern into the environment. A controller is used to control the sources in order to reduce the interference caused by overlapping light patterns. Various methods are described including: cycling between the different sources, where the cycle used may be fixed or may change dynamically based on the scene detected using the cameras; setting the wavelength of each source so that overlapping patterns are at different wavelengths; moving source-camera pairs in independent motion patterns; and adjusting the shape of the projected light patterns to minimize overlap. These methods may also be combined in any way. In another embodiment, the system comprises a single source and a mirror system is used to cast the projected structured light pattern around the environment.

Reducing interference between multiple infra-red depth cameras

Augmented reality with direct user interaction is described. In one example, an augmented reality system comprises a user-interaction region, a camera that captures images of an object in the user-interaction region, and a partially transparent display device which combines a virtual environment with a view of the user-interaction region, so that both are visible at the same time to a user. A processor receives the images, tracks the object's movement, calculates a corresponding movement within the virtual environment, and updates the virtual environment based on the corresponding movement. In another example, a method of direct interaction in an augmented reality system comprises generating a virtual representation of the object having the corresponding movement, and updating the virtual environment so that the virtual representation interacts with virtual objects in the virtual environment. From the user's perspective, the object directly interacts with the virtual objects.

Augmented reality with direct user interaction

User interface control using a keyboard is described. In an embodiment, a user interface displayed on a display device is controlled using a computer connected to a keyboard. The keyboard has a plurality of alphanumeric keys that can be used for text entry. The computer receives data comprising a sequence of key-presses from the keyboard, and generates for each key-press a physical location on the keyboard. The relative physical locations of the key-presses are compared to calculate a movement path over the keyboard. The movement path describes the path of a user's digit over the keyboard. The movement path is mapped to a sequence of coordinates in the user interface, and the movement of an object displayed in the user interface is controlled in accordance with the sequence of coordinates.

User interface control using a keyboard

Real-time camera tracking using depth maps is described. In an embodiment depth map frames are captured by a mobile depth camera at over 20 frames per second and used to dynamically update in real-time a set of registration parameters which specify how the mobile depth camera has moved. In examples the real-time camera tracking output is used for computer game applications and robotics. In an example, an iterative closest point process is used with projective data association and a point-to-plane error metric in order to compute the updated registration parameters. In an example, a graphics processing unit (GPU) implementation is used to optimize the error metric in real-time. In some embodiments, a dense 3D model of the mobile camera environment is used.

Real-time camera tracking using depth maps

Generating computer models of 3D objects is described. In one example, depth images of an object captured by a substantially static depth camera are used to generate the model, which is stored in a memory device in a three-dimensional volume. Portions of the depth image determined to relate to the background are removed to leave a foreground depth image. The position and orientation of the object in the foreground depth image is tracked by comparison to a preceding depth image, and the foreground depth image is integrated into the volume by using the position and orientation to determine where to add data derived from the foreground depth image into the volume. In examples, the object is hand-rotated by a user before the depth camera. Hands that occlude the object are integrated out of the model as they do not move in sync with the object due to re-gripping.

Generating computer models of 3D objects

A method for constructing an avatar of a human subject includes acquiring a depth map of the subject, obtaining a virtual skeleton of the subject based on the depth map, and harvesting from the virtual skeleton a set of characteristic metrics. Such metrics correspond to distances between predetermined points of the virtual skeleton. In this example method, the characteristic metrics are provided as input to an algorithm trained using machine learning. The algorithm may be trained using a human model in a range of poses, and a range of human models in a single pose, to output a virtual body mesh as a function of the characteristic metrics. The method also includes constructing a virtual head mesh distinct from the virtual body mesh, with facial features resembling those of the subject, and connecting the virtual body mesh to the virtual head mesh.

Avatar construction using depth camera

Learning image processing tasks from scene reconstructions is described where the tasks may include but are not limited to: image de-noising, image in-painting, optical flow detection, interest point detection. In various embodiments training data is generated from a 2 or higher dimensional reconstruction of a scene and from empirical images of the same scene. In an example a machine learning system learns at least one parameter of a function for performing the image processing task by using the training data. In an example, the machine learning system comprises a random decision forest. In an example, the scene reconstruction is obtained by moving an image capture apparatus in an environment where the image capture apparatus has an associated dense reconstruction and camera tracking system.

Learning image processing tasks from scene reconstructions

A system and method for providing an augmented reality environment in which the environmental mapping process is decoupled from the localization processes performed by one or more mobile devices is described. In some embodiments, an augmented reality system includes a mapping system with independent sensing devices for mapping a particular real-world environment and one or more mobile devices. Each of the one or more mobile devices utilizes a separate asynchronous computing pipeline for localizing the mobile device and rendering virtual objects from a point of view of the mobile device. This distributed approach provides an efficient way for supporting mapping and localization processes for a large number of mobile devices, which are typically constrained by form factor and battery life limitations.

Distributed asynchronous localization and mapping for augmented reality

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Cambridge, United Kingdom

David G Molyneaux