Intel Corporation

Omek Interactive, Inc.

Gershom Kutliroff

Amit Bleiweiss

Shahar Fleishman

Yaron Yanai

Mark Kliger

Yotam Livny

Maoz Madmony

Jonathan Epstein

Itamar Glazer

Shahar Fleischmann

Maoz Madmoni

Itamar Ben-Ari

Eran Eilat

Dagan Eshar

Gideon Kleimer

A system and method for close range object tracking are described. Close range depth images of a user's hands and fingers or other objects are acquired using a depth sensor. Using depth image data obtained from the depth sensor, movements of the user's hands and fingers or other objects are identified and tracked, thus permitting the user to interact with an object displayed on a screen, by using the positions and movements of his hands and fingers or other objects.

System and method for close-range movement tracking

SLAM systems are provided that utilize an artificial neural network to both map environments and locate positions within the environments. In some example embodiments, a sensor arrangement is used to map an environment. The sensor arrangement acquires sensor data from the various sensors and associates the sensor data, or data derived from the sensor data, with spatial regions in the environment. The sensor data may include image data and inertial measurement data that effectively describes the visual appearance of a spatial region at a particular location and orientation. This diverse sensor data may be fused into camera poses. The map of the environment includes camera poses organized by spatial region within the environment. Further, in these examples, an artificial neural network is adapted to the features of the environment by a transfer learning process using image data associated with camera poses.

System for building a map and subsequent localization

Techniques are provided for context-based 3D scene reconstruction employing fusion of multiple instances of an object within the scene. A methodology implementing the techniques according to an embodiment includes receiving 3D image frames of the scene, each frame associated with a pose of a depth camera, and creating a 3D reconstruction of the scene based on depth pixels that are projected and accumulated into a global coordinate system. The method may also include detecting objects, based on the 3D reconstruction, the camera pose and the image frames. The method may further include classifying the detected objects into one or more object classes; grouping two or more instances of objects in one of the object classes based on a measure of similarity of features between the object instances; and combining point clouds associated with each of the grouped object instances to generate a fused object.

Three dimensional scene reconstruction based on contextual analysis

Techniques are provided for segmentation of objects in a 3D image of a scene. An example method may include receiving, 3D image frames of a scene. Each of the frames is associated with a pose of a depth camera that generated the 3D image frames. The method may also include detecting the objects in each of the frames based on object recognition; associating a label with the detected object; calculating a 2D bounding box around the object; and calculating a 3D location of the center of the bounding box. The method may further include matching the detected object to an existing object boundary set, created from a previously received image frame, based on the label and the location of the center of the bounding box, or, if the match fails, creating a new object boundary set associated with the detected object.

Recognition-based object segmentation of a 3-dimensional image

Systems, apparatuses and/or methods may provide for generating a packing order of items within a container that consolidates the items into a reduced space. Items may be scanned with a three-dimensional (3D) imager, and models may be generated of the items based on the data from the 3D imager. The items may be located within minimal-volume enclosing bounding boxes, which may be analyzed to determine whether they may be merged together in one of their bounding boxes, or into a new bounding box that is spatially advantageous in terms of packing. If a combination of items is realizable and is determined to take up less space in a bounding box than the bounding boxes of the items considered separately, then they may be merged into a single bounding box. Thus, a spatially efficient packing sequence for a plurality of real objects may be generated to maximize packing efficiency.

Efficient packing of objects

Techniques are provided for 3D analysis of a scene including detection, segmentation and registration of objects within the scene. The analysis results may be used to implement augmented reality operations including removal and insertion of objects and the generation of blueprints. An example method may include receiving 3D image frames of the scene, each frame associated with a pose of a depth camera, and creating a 3D reconstruction of the scene based on depth pixels that are projected and accumulated into a global coordinate system. The method may also include detecting objects, and associated locations within the scene, based on the 3D reconstruction, the camera pose and the image frames. The method may further include segmenting the detected objects into points of the 3D reconstruction corresponding to contours of the object and registering the segmented objects to 3D models of the objects to determine their alignment.

3-dimensional scene analysis for augmented reality operations

Systems and methods for combining three-dimensional tracking of a user's movements with a three-dimensional user interface display is described. A tracking module processes depth data of a user performing movements, for example, movements of the user's hands and fingers. The tracked movements are used to animate a representation of the hand and fingers, and the animated representation is displayed to the user using three-dimensional display. Also displayed are one or more virtual objects with which the user can interact. In some embodiments, the interaction of the user with the virtual objects controls an electronic device.

System and method for combining three-dimensional tracking with a three-dimensional display for a user interface

Techniques are provided for generating a 3-Dimensional (3D) reconstruction of a handheld object. An example method may include receiving 3D image frames of the object from a static depth camera, each frame including a color image and a depth map. Each of the frames is associated with an updated pose of the object during scanning. The method may also include extracting a segment of each frame corresponding to the object and the hand; isolating the hand from each extracted segment; and filtering each frame by removing regions of the frame outside of the extracted segment and removing regions of the frame corresponding to the isolated hand. The method may further include calculating the updated object pose in each filtered frame; and calculating a 3D position for each depth pixel from the depth map of each filtered frame based on the updated object pose associated with that filtered frame.

Scanning of a handheld object for 3-dimensional reconstruction

Systems and methods for combining three-dimensional tracking of a user's movements with a three-dimensional user interface display is described. A tracking module processes depth data of a user performing movements, for example, movements of the user's hand and fingers. The tracked movements are used to animate a representation of the hand and fingers, and the animated representation is displayed to the user using a three-dimensional display. Also displayed are one or more virtual objects with which the user can interact. In some embodiments, the interaction of the user with the virtual objects controls an electronic device.

A method for modeling and tracking a subject using image depth data includes locating the subject's trunk in the image depth data and creating a three-dimensional (3D) model of the subject's trunk. Further, the method includes locating the subject's head in the image depth data and creating a 3D model of the subject's head. The 3D models of the subject's head and trunk can be exploited by removing pixels from the image depth data corresponding to the trunk and the head of the subject, and the remaining image depth data can then be used to locate and track an extremity of the subject.

Method and system for modeling subjects from a depth map

The present disclosure describes a target tracker that evaluates frames of data of one or more targets, such as a body part, body, and/or object, acquired by a depth camera. Positions of the joints of the target(s) in the previous frame and the data from a current frame are used to determine the positions of the joints of the target(s) in the current frame. To perform this task, the tracker proposes several hypotheses and then evaluates the data to validate the respective hypotheses. The hypothesis that best fits the data generated by the depth camera is selected, and the joints of the target(s) are mapped accordingly.

Model-based multi-hypothesis target tracker

A system and method for creating a gesture and generating a classifier that can identify the gesture for use with an application is described. The designer constructs a training set of data containing positive and negative examples of the gesture. Machine learning algorithms are used to compute the optimal classification of the training data into positive and negative instances of the gesture. The machine learning algorithms generate a classifier which, given input data, makes a decision on whether the gesture was performed in the input data or not.

System and method for automatically defining and identifying a gesture

A method of image acquisition and data pre-processing includes obtaining from a sensor an image of a subject making a movement. The sensor may be a depth camera. The method also includes selecting a plurality of features of interest from the image, sampling a plurality of depth values corresponding to the plurality of features of interest, projecting the plurality of features of interest onto a model utilizing the plurality of depth values, and constraining the projecting of the plurality of features of interest onto the model utilizing a constraint system. The constraint system may comprise an inverse kinematics solver.

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Alon Shvut, Israel

Gershom Kutliroff