Microsoft Technology Licensing, LLC

Facebook, Inc.

Matthew Adam Simari

Lin Liang

Vijay Baiyya

Christian F Huitema

Michael Anthony Hall

Alvaro Collet Romea

Simon Stachniak

Krishnan Ramnath

Sean Eron Anderson

Monica Stewart

Szyman Piotr Stachniak

In one embodiment, a method includes determining, using one or more location sensors of a computing device, an approximate location of the computing device, identifying a content object located within a threshold distance of the approximate location, wherein an augmented-reality map associates the content object with a stored model of a real-world object and specifies a location of the content object on or relative to the stored model of the real-world object, obtaining an image from a camera of the device, identifying, in the image, a target real-world object that matches the stored model of the real-world object, determining a content object location based on a location of the target real-world object and the location of the content object on or relative to the model of the real-world object, and displaying the content object at the content object location.

Mobile persistent augmented-reality experiences

A system and method are disclosed for determining and alerting a user as to whether an object will successfully scan before the post-processing of the scan data. In embodiments, before post-processing of the scan data begins, the scan data is processed by a machine learning algorithm which is able to determine whether and/or how likely the scan data is to return an accurate scanned reproduction of the scanned object. The machine learning algorithm may also suggest new positions for the object in the environment where the scan is more likely to be successful.

Real-time detection of object scanability

A method for virtual, three-dimensional modeling of a subject using a depth-imaging camera operatively coupled to a modeling computer. A brightness image and a coordinate depth image of the subject acquired from each of a plurality of inequivalent vantage points are received from the depth-imaging camera. An angle-dependent reflectance is determined based on the brightness and coordinate depth images acquired from each of the vantage points.

Video imaging to assess specularity

A method for generating a three-dimensional virtual representation includes performing a first scan of the physical object while the physical object has a first position. A displayed visualization of the physical object is generated based on the first scan. Aligning input is received that causes increased correspondence between the displayed visualization and a second position of the physical object. A second scan of the physical object is performed while the object is in the second position. Based on the first scan and the second scan, a three-dimensional virtual representation of the physical object is generated.

Visualization alignment for three-dimensional scanning

A method for operating an image processing device coupled to a color camera and a depth camera is provided. The method includes receiving a color image of a 3-dimensional scene from a color camera, receiving a depth map of the 3-dimensional scene from a depth camera, generating an aligned 3-dimensional face mesh from a plurality of color images received from the color camera indicating movement of a subject's head within the 3-dimensional scene and form the depth map, determining a head region based the depth map, segmenting the head region into a plurality of facial sections based on both the color image, depth map, and the aligned 3-dimensional face mesh, and overlaying the plurality of facial sections on the color image.

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Seattle, WA, United States of America

Matthew Adam Simari