Microsoft Technology Licensing, LLC

Otmar Hilliges

Shahram Izadi

David Kim

David G Molyneaux

Stephen Edward Hodges

Jamie Daniel Joseph Shotton

Pushmeet Kohli

Andrew William Fitzgibbon

David Alexander Butler

Richard Newcombe

Patrick Luke Olivier

Carsten Curt Eckard Rother

Malte Hanno Weiss

Andrew David Wilson

Antonio Criminisi

Armando Garcia-Mendoza

David Kirk

Nicolas Villar

Stuart Taylor

John Michael Winn

Jamie Shotton

Sebastian Nowozin

Matthew Darius Cook

Jiawen Chen

Mat Cook

Ross Brook Girshick

Stefan Johannes Josef Holzer

Iason Oikonomidis

Detecting material properties such reflectivity, true color and other properties of surfaces in a real world environment is described in various examples using a single hand-held device. For example, the detected material properties are calculated using a photometric stereo system which exploits known relationships between lighting conditions, surface normals, true color and image intensity. In examples, a user moves around in an environment capturing color images of surfaces in the scene from different orientations under known lighting conditions. In various examples, surfaces normals of patches of surfaces are calculated using the captured data to enable fine detail such as human hair, netting, textured surfaces to be modeled. In examples, the modeled data is used to render images depicting the scene with realism or to superimpose virtual graphics on the real world in a realistic manner.

Using photometric stereo for 3D environment modeling

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

A wearable sensor for tracking articulated body parts is described such as a wrist-worn device which enables 3D tracking of fingers and optionally also the arm and hand without the need to wear a glove or markers on the hand. In an embodiment a camera captures images of an articulated part of a body of a wearer of the device and an articulated model of the body part is tracked in real time to enable gesture-based control of a separate computing device such as a smart phone, laptop computer or other computing device. In examples the device has a structured illumination source and a diffuse illumination source for illuminating the articulated body part. In some examples an inertial measurement unit is also included in the sensor to enable tracking of the arm and hand

Wearable sensor for tracking articulated body-parts

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

An augmented reality system which enables grasping of virtual objects is described such as to stack virtual cubes or to manipulate virtual objects in other ways. In various embodiments a user's hand or another real object is tracked in an augmented reality environment. In examples, the shape of the tracked real object is approximated using at least two different types of particles and the virtual objects are updated according to simulated forces exerted between the augmented reality environment and at least some of the particles. In various embodiments 3D positions of a first one of the types of particles, kinematic particles, are updated according to the tracked real object; and passive particles move with linked kinematic particles without penetrating virtual objects. In some examples a real-time optic flow process is used to track motion of the real object.

Grasping virtual objects in augmented reality

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

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

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Cambridge, United Kingdom

Otmar Hilliges