Method and system for inserting recognized object data into a virtual world

• US 10,533,850 B2
• Filed: 05/05/2015
• Issued: 01/14/2020
• Est. Priority Date: 07/12/2013
• Status: Active Grant

• Current Assignee: Magic Leap, Inc.
• Original Assignee: Magic Leap, Inc.
• Inventors: Abovitz, Rony, Schowengerdt, Brian T., Watson, Mathew D.
• Primary Examiner(s): Wang, Jin Cheng
• Application Number:
  US14/704,800
• Publication Number:
  US 20150235088A1
• Time in Patent Office: 1,715 Days
• Field of Search: 345633, 382103, 382190, 715852, 707609, 707722
• CPC Class Codes: A61B 2034/101 Computer-aided simulation of surgical operations
• CPC Class Codes: A61B 2562/0204 Acoustic sensors
• CPC Class Codes: A61B 2562/0219 Inertial sensors, e.g. accelerometers, gyroscopes, tilt switches
• CPC Class Codes: A61B 2562/0247 Pressure sensors
• CPC Class Codes: A61B 3/0008 provided with illuminating means A61B3/117 takes precedence; goniolenses used for laser treatment A61F9/009; illuminating means for optical instruments G02B27/00
• CPC Class Codes: A61B 3/022 for testing contrast sensitivity
• CPC Class Codes: A61B 3/024 for determining the visual field, e.g. perimeter types
• CPC Class Codes: A61B 3/028 for testing visual acuity; for determination of refraction, e.g. phoropters
• CPC Class Codes: A61B 3/063 for testing light sensitivity, i.e. adaptation
• CPC Class Codes: A61B 3/066 for testing colour vision
• CPC Class Codes: A61B 3/08 for testing binocular or stereoscopic vision, e.g. strabismus
• CPC Class Codes: A61B 3/085 for testing strabismus
• CPC Class Codes: A61B 3/10 Objective types, i.e. instruments for examining the eyes independent of the patients' perceptions or reactions
• CPC Class Codes: A61B 3/1015 for wavefront analysis
• CPC Class Codes: A61B 3/102 for optical coherence tomography [OCT]
• CPC Class Codes: A61B 3/1035 for measuring astigmatism testing astigmatism A61B3/036
• CPC Class Codes: A61B 3/113 for determining or recording eye movement
• CPC Class Codes: A61B 3/12 for looking at the eye fundus, e.g. ophthalmoscopes A61B3/13 takes precedence
• CPC Class Codes: A61B 3/1216 for diagnostics of the iris
• CPC Class Codes: A61B 3/13 Ophthalmic microscopes
• CPC Class Codes: A61B 3/14 : Arrangements specially adapted for eye photography apparatus or arrangements for taking photographs per se G03B
• CPC Class Codes: A61B 3/165 : Non-contacting tonometers
• CPC Class Codes: A61B 34/10 : Computer-aided planning, simulation or modelling of surgical operations
• CPC Class Codes: A61B 5/0059 : using light, e.g. diagnosis by transillumination, diascopy, fluorescence A61B5/0093, A61B5/1455, A61B5/02416 and A61B5/0261 take precedence
• CPC Class Codes: A61B 5/0066 : Optical coherence imaging
• CPC Class Codes: A61B 5/0077 : Devices for viewing the surface of the body, e.g. camera, magnifying lens
• CPC Class Codes: A61B 5/01 : Measuring temperature of body parts ; Diagnostic temperature sensing, e.g. for malignant or inflamed tissue clinical contact thermometers G01K13/20
• CPC Class Codes: A61B 5/14532 : for measuring glucose, e.g. by tissue impedance measurement
• CPC Class Codes: A61B 5/1455 : using optical sensors, e.g. spectral photometrical oximeters
• CPC Class Codes: A61B 5/14555 : specially adapted for the eye fundus
• CPC Class Codes: A61B 5/369 : Electroencephalography [EEG] devices for psychotechnics A61B5/16
• CPC Class Codes: A61B 5/398 : Electrooculography [EOG], e.g. detecting nystagmus; Electroretinography [ERG]
• CPC Class Codes: A61B 5/6803 : Head-worn items, e.g. helmets, masks, headphones or goggles
• CPC Class Codes: A63F 13/00 : Video games, i.e. games using an electronically generated display having two or more dimensions gaming systems which provide a financial reward G07F17/32
• CPC Class Codes: A63F 13/213 : comprising photodetecting means, e.g. cameras, photodiodes or infrared cells A63F13/219, A63F13/655 take precedence
• CPC Class Codes: A63F 13/428 : involving motion or position input signals, e.g. signals representing the rotation of an input controller or a player's arm motions sensed by accelerometers or gyroscopes
• CPC Class Codes: G01B 11/303 : using photoelectric detection means
• CPC Class Codes: G02B 2027/0105 : Holograms with particular structures
• CPC Class Codes: G02B 2027/0127 : comprising devices increasing the depth of field
• CPC Class Codes: G02B 2027/0138 : comprising image capture systems, e.g. camera
• CPC Class Codes: G02B 2027/014 : comprising information/image processing systems
• CPC Class Codes: G02B 2027/0178 : Eyeglass type eyeglass details G02C
• CPC Class Codes: G02B 2027/0185 : Displaying image at variable distance
• CPC Class Codes: G02B 2027/0187 : slaved to motion of at least a part of the body of the user, e.g. head, eye
• CPC Class Codes: G02B 27/0101 : characterised by optical features G02B27/0172 takes precedence
• CPC Class Codes: G02B 27/017 : Head mounted
• CPC Class Codes: G02B 27/0172 : characterised by optical features
• CPC Class Codes: G02B 27/0179 : Display position adjusting means not related to the information to be displayed
• CPC Class Codes: G02B 27/42 : Diffraction optics , i.e. systems including a diffractive element being designed for providing a diffractive effectG02B27/60 takes precedence
• CPC Class Codes: G02B 27/4205 : having a diffractive optical element [DOE] contributing to image formation, e.g. whereby modulation transfer function MTF or optical aberrations are relevant
• CPC Class Codes: G02B 27/4227 : in image scanning systems
• CPC Class Codes: G02B 6/10 : of the optical waveguide type G02B6/02, G02B6/24 take precedence; devices or arrangements for the control of light by electric, magnetic, electro-magnetic or acoustic means G02F1/00; transferring the modulation of modulated light G02F2/00; optical logic elements G02F3/00; optical analogue/digital converters G02F7/00
• CPC Class Codes: G02B 6/34 : utilising prism or grating G02B6/293 takes precedence
• CPC Class Codes: G06F 16/7837 : using objects detected or recognised in the video content
• CPC Class Codes: G06F 18/22 : Matching criteria, e.g. proximity measures
• CPC Class Codes: G06F 3/005 : Input arrangements through a video camera
• CPC Class Codes: G06F 3/011 : Arrangements for interaction with the human body, e.g. for user immersion in virtual reality blind teaching G09B21/00
• CPC Class Codes: G06F 3/013 : Eye tracking input arrangements G06F3/015 takes precedence
• CPC Class Codes: G06F 3/017 : Gesture based interaction, e.g. based on a set of recognized hand gestures interaction based on gestures traced on a digitiser G06F3/04883
• CPC Class Codes: G06F 3/0304 : Detection arrangements using opto-electronic means constructional details of pointing devices not related to the detection arrangement using opto-electronic means G06F3/033; optical digitisers G06F3/042
• CPC Class Codes: G06F 3/04815 : Interaction with a metaphor-based environment or interaction object displayed as three-dimensional, e.g. changing the user viewpoint with respect to the environment or object
• CPC Class Codes: G06F 3/04842 : Selection of displayed objects or displayed text elements G06F3/0482 takes precedence
• CPC Class Codes: G06F 3/04845 : for image manipulation, e.g. dragging, rotation, expansion or change of colour
• CPC Class Codes: G06F 3/0485 : Scrolling or panning
• CPC Class Codes: G06F 3/0487 : using specific features provided by the input device, e.g. functions controlled by the rotation of a mouse with dual sensing arrangements, or of the nature of the input device, e.g. tap gestures based on pressure sensed by a digitiser
• CPC Class Codes: G06F 3/04883 : for inputting data by handwriting, e.g. gesture or text
• CPC Class Codes: G06Q 30/0643 : Graphical representation of items or shoppers
• CPC Class Codes: G06T 19/006 : Mixed reality object pose determination, tracking or camera calibration for mixed reality G06T7/00
• CPC Class Codes: G06T 2200/04 : involving 3D image data
• CPC Class Codes: G06T 2200/24 : involving graphical user interfaces [GUIs]
• CPC Class Codes: G06T 2207/10004 : Still image; Photographic image
• CPC Class Codes: G06T 2207/10024 : Color image
• CPC Class Codes: G06T 2207/10148 : Varying focus
• CPC Class Codes: G06T 2207/10152 : Varying illumination
• CPC Class Codes: G06T 2207/30041 : Eye; Retina; Ophthalmic
• CPC Class Codes: G06T 2207/30196 : Human being; Person
• CPC Class Codes: G06T 2210/41 : Medical
• CPC Class Codes: G06T 2219/024 : Multi-user, collaborative environment
• CPC Class Codes: G06T 7/60 : Analysis of geometric attributes
• CPC Class Codes: G06V 10/20 : Image preprocessing
• CPC Class Codes: G06V 10/40 : Extraction of image or video features
• CPC Class Codes: G06V 10/758 : Involving statistics of pixels or of feature values, e.g. histogram matching
• CPC Class Codes: G06V 20/10 : Terrestrial scenes scenes under surveillance with static cameras G06V20/52; scenes perceived from the exterior of a vehicle G06V20/56; scenes perceived from the interior of a vehicle G06V20/59
• CPC Class Codes: G06V 20/20 : in augmented reality scenes
• CPC Class Codes: G06V 20/40 : in video content extracting overlay text G06V20/62; video retrieval G06F16/70; processing of video elementary streams in video servers H04N21/234; processing of video elementary streams in video clients H04N21/44
• CPC Class Codes: G06V 20/653 : by matching three-dimensional models, e.g. conformal mapping of Riemann surfaces
• CPC Class Codes: G06V 40/113 : Recognition of static hand signs
• CPC Class Codes: G06V 40/28 : Recognition of hand or arm movements, e.g. recognition of deaf sign language static hand signs G06V40/113
• CPC Class Codes: G16H 40/00 : ICT specially adapted for the management or administration of healthcare resources or facilities; ICT specially adapted for the management or operation of medical equipment or devices
• CPC Class Codes: G16H 40/20 : for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms
• CPC Class Codes: G16H 40/67 : for remote operation
• CPC Class Codes: H04B 10/25891 : Transmission components H04B10/40 takes precedence

Abstract

A method of rendering virtual content comprises capturing an image of a field of view of a user, extracting a set of map points based on the captured image, recognizing an object based on the extracted set of map points, retrieving semantic data associated with the recognized object and attaching the semantic data to object data associated with the recognized object, and inserting the recognized object data and the semantic data attached thereto into a virtual world model such that virtual content, when rendered at a user device of the user, is displayed in relation to the recognized object.

Claims

1. A method of rendering virtual content, comprising:

capturing, at an image capture device of a virtual or augmented reality system, at least one image of a field of view of a user;

extracting a set of map points based on the at least one image;

recognizing an object based at least in part on the set of map points, recognizing the object comprising;

segmenting the set of map points into a plurality of smaller subsets of map points;

storing a first smaller subset of the plurality of smaller subsets of map points locally on the virtual or augmented reality system but not on a remote computational resource communicatively coupled to the virtual or augmented reality system based at least in part upon density of a plurality of fiducial points for registration of one or more other map points of the set of map points;

recognizing the object as a first type of objects at least by processing a first smaller subset of the plurality of smaller subsets of map points into a first processed subset representing the first type of objects with a first object recognizer on the virtual or augmented reality system, wherein

map points in the first smaller subset include information comprising respective coordinates of the map points that are used to localize the map points and to recognize the object as the first type of objects; and

recognizing the object as a purveyor object specific to a purveyor at least by processing the first processed subset with a second object recognizer pertaining to the purveyor based at least in part upon an interaction of the user with the object; and

rendering virtual content that includes an offer of product or service from an establishment of the purveyor near the user and displaying the virtual content to the user in relation to the object.

2. The method of claim 1, further comprising:

grooming the set of map points based at least in part upon the density of fiducial points for the registration of the at least some of the set of map points on the remote computational resource, grooming the set of map points comprising;

storing one or more other map points in the plurality of smaller subsets of map points on the remote computational resource based at least in part upon the density of the fiducial points for the registration of the one or more other map points.

3. The method of claim 1, further comprising:

capturing a plurality of images of respective fields of view of the user, wherein the image is pose-tagged; and

extracting the set of map points based in part or in whole on the plurality of captured images.

4. The method of claim 1, wherein the set of map points includes sparse three-dimensional (3D) points.

5. The method of claim 1, wherein recognizing the object comprises performing two-dimensional (2D) image segmentation on the set of map points.

6. The method of claim 1, wherein recognizing the object comprises utilizing one or more image recognizers.

7. The method of claim 1, wherein recognizing the object comprises performing two-dimensional or three-dimensional data fusion.

8. The method of claim 1, wherein recognizing the object comprises recognizing a structure of the object.

9. The method of claim 1, wherein recognizing the object comprises recognizing a property of the object.

10. The method of claim 1, further comprising parameterizing a geometry of the object.

11. The method of claim 10, further comprising synchronizing the geometry of the object to a networked memory.

12. The method of claim 1, further comprising inserting the object data and semantic data attached thereto into a world model, wherein the world model comprises a persistent digital representation of a physical environment in which the user is located.

13. The method of claim 1, further comprising inserting the object data and semantic data attached thereto into a world model, wherein the semantic information includes description of at least one characteristic of the object.

14. The method of claim 13, further comprising querying the user regarding at least one characteristic of the object, wherein the semantic data is generated based in part or in whole on an answer provided by the user.

15. The method of claim 1, further comprising generating and storing semantic data that is attached to object data associated with the object.

16. The method of claim 15, wherein the semantic data is stored in a cloud-based repository.

17. The method of claim 16, wherein one or more computing systems on a cloud computing platform analyze the set of map points and recognize the object.

18. The method of claim 15, wherein the semantic data is generated by a virtual or augmented reality (AR) system.

19. The method of claim 1, recognizing the object further comprising:

identifying a second object recognizer;

reducing the set of map points into a reduced set of map points by removing a second smaller subset recognized with the second object recognizer from the set of map points; and

recognizing the object with a first object recognizer by processing at least a portion of the reduced set of map points including the first smaller of map points.

20. The method of claim 1, recognizing the object further comprising:

identifying an object identifier corresponding to the object;

identifying the object at least by processing a smaller subset of the plurality of smaller subsets of map points with the object identifier without regard to one or more characteristics of the object;

identifying a separate object identifier corresponding to the object; and

performing nested object identification by processing at least the smaller subset of the plurality of smaller subsets with the separate object identifier.