Qualcomm Incorporated

Daqri, LLC

Kiyoung Kim

Daniel Wagner

Yanghai Tsin

Youngmin Park

Brian Mullins

Kai Zhou

Erick Mendez Mendez

Bernhard Jung

Dominik Schnitzer

Clemens Birklbauer

Lucas Kazansky

Seyed Hesameddin Najafi Shoushtari

Frank Chester Irving, Jr

Roy Lawrence Ashok Inigo

Dheeraj Ahuja

Jonathan Trevor Freeman

Erick Mendez

An augmented reality (AR) display application generates mapped visualization content overlaid on a real world physical environment. The AR display application receives sensor feeds, location information, and orientation information from wearable devices within the environment. A tessellation surface is visually mapped to surfaces of the environment based on a depth-based point cloud. A texture is applied to the tessellation surface and the tessellation may be viewed overlaying the surfaces of the environment via a wearable device.

Environmental mapping system

Disclosed are a device, apparatus, and method for performing line mapping. A three-dimensional (3D) map that includes at least a first and a second 3D line corresponding to an aspect of a real world environment may be obtained. One or more images of the aspect may also be obtained and hypotheses 3D lines may be determined. The hypothesis 3D lines may be verified with the one or more images and the 3D map may be updated. The determination and verification of the hypothesis 3D lines may include creating a plane in 3D space and using co-planarity or orthogonality assumptions.

Hypotheses line mapping and verification for 3D maps

Disclosed embodiments facilitate keypoint selection in part by assigning a similarity score to each candidate keypoint being considered for selection. The similarity score may be based on the maximum measured similarity of an image patch associated with a keypoint in relation to an image patch in a local image section in a region around the image patch. A subset of the candidate keypoints with the lowest similarity scores may be selected and used to detect and/or track objects in subsequent images and/or to determine camera pose.

Keypoint detection with trackability measurements

Disclosed is a method and apparatus for adaptively executing one or more motion blur estimation methods to estimate a motion blur associated with an image target frame in an Augmented Reality environment produced by an Augmented Reality application. In one embodiment, the functions implemented include: applying a first motion blur estimation method to estimate the motion blur; determining whether computational resources are available for a second motion blur estimation method; and applying the second motion blur estimation method to estimate the motion blur in response to a determination that computational resources are available for the second motion blur estimation method.

Fast adaptive estimation of motion blur for coherent rendering

Disclosed embodiments pertain to apparatus, systems, and methods for robust feature based tracking. In some embodiments, a score may be computed for a camera captured current image comprising a target object. The score may be based on one or more metrics determined from a comparison of features in the current image and a prior image captured by the camera. The comparison may be based on an estimated camera pose for the current image. In some embodiments, one of a point based, an edge based, or a combined point and edge based feature correspondence method may be selected based on a comparison of the score with a point threshold and/or a line threshold, the point and line thresholds being obtained from a model of the target. The camera pose may be refined by establishing feature correspondences using the selected method between the current image and a model image.

Robust tracking using point and line features

Embodiments include selecting edgels for edge based tracking by dividing a reference image frame (RF) into N&#xd7;M bins of pixels and projecting a subset of the edgels per bin into a current image frame (CF) using an estimated pose to identify valid bins of the RF as bins having their projected one edgel found within the borders of the CF. Then, K edgels of RF with different orientations from each valid bins may be selected. Then, the selected RF edgels of bins may be reduced by removing bins randomly, or first removing bins from the center of the RF of the image (then next removing the next further outward bins), until a desirable edgel number is obtained. Edge-based tracking can then be performed using the desirable edgel number, to track edges in current frame that are found in prior frame.

Edgel sampling for edge-based tracking

A method of real-time tracking of an object includes capturing a first and a second image of the object. The object is detected in the first image and movement of the object is tracked between the images. Tracking of the object includes obtaining an initial pose of the camera; projecting an image of a model object onto the second image; determining a gradient profile of the second image from an edge point of the model object along a first direction that is normal to the edge of the model object; computing a radius on the gradient profile; determining a rank order of the peaks of the gradient profile along the radius; comparing the rank order with a predetermined rank order to generate a feature candidate point; and reducing a distance along the first direction between the feature candidate point and the edge point on the edge of the model object.

Growing community of inventors

Vienna, Austria

Kiyoung Kim