IP Library Granted Patent US 12,450,893
Granted Patent B2
US 12,450,893 · App. 18/214,214 · Granted Oct 21, 2025

Object detection with instance detection and general scene understanding

Inventors: Aitor Aldoma Buchaca (Bavaria-Bayern, DE); Andreas N. Moeller (Bayern, DE); Michael Kuhn (Bayern, DE)
Assignee: Apple Inc.
G06V10/82G06N20/00G06V10/764G06V20/20G06V20/35G06V20/64
View Patent ↗
Loading inventors, assignments & file history…
Monitor This Case
Get email alerts when status or documents change.
Order Certified Copies
Most orders are placed with the USPTO same day — all within 24 business hours.
Order via The Patent Place →
Pre-filled with this patent's details
Quick Facts
Patent No.
US 12,450,893
App. No.
18/214,214
Granted
Oct 21, 2025
Kind
B2
Abstract

Various implementations disclosed herein include devices, systems, and methods that determine a particular object instance in CGR environments. In some implementations, an object type of an object depicted in an image of a physical environment is identified. Then, a particular instance is determined based on the object type and the image. In some implementations, objects of the particular instance have a set of characteristics that differs from sets of characteristics associated with other instances of the object type. Then, the set of characteristics of the particular instance of the object depicted in the physical environment is obtained.

Claims (39)

1. A method comprising:

at an electronic device having a processor:

identifying an object type of an object depicted in an image of a physical environment;

determining a particular instance of the object based on the object type and the image, objects of the particular instance having a set of characteristics that differs from sets of characteristics of other instances of the object type;

obtaining the set of characteristics of the particular instance of the object depicted in the physical environment; and

combining the set of characteristics of the particular instance of the object with a CGR environment depicting the physical environment.

2. The method of claim 1 , wherein identifying the object type of the object depicted in an image includes detecting the object type of a plurality of object types of the object in the image using a first machine learning model.

3. The method of claim 1 , wherein determining a particular instance of the object based on the object type and the image comprises using features extracted from a portion of the image depicting the object to generate a representation of the particular instance of the object; and

determining the particular instance using the representation.

4. The method of claim 3 , wherein determining the particular instance using the representation comprises:

querying a first database of instances of the object type using the representation; and

receiving the particular instance from the first database.

5. The method of claim 4 , wherein obtaining the set of characteristics of the particular instance of the object depicted in the physical environment comprises accessing a database to receive information on materials, dimensions, physical properties, or visual properties of the particular instance of the object.

6. The method of claim 3 , wherein determining the particular instance using the representation comprises using a second machine learning model that inputs the object type and the representation of the particular instance of the object and outputs the particular instance of the object.

7. The method of claim 6 , wherein obtaining the set of characteristics of the particular instance of the object depicted in the physical environment comprises accessing a database to receive information on materials, dimensions, physical properties, or visual properties of the particular instance of the object.

8. The method of claim 1 , wherein combining the set of characteristics of the particular instance of the object with the CGR environment depicting the physical environment comprises using the set of characteristics of the particular instance of the object to perform environment lighting in the CGR environment depicting the physical environment.

9. The method of claim 1 , wherein combining the set of characteristics of the particular instance of the object with the CGR environment depicting the physical environment comprises using the set of characteristics of the particular instance of the object to perform scene understanding of the CGR environment depicting the physical environment.

10. The method of claim 1 , wherein combining the set of characteristics of the particular instance of the object with the CGR environment depicting the physical environment comprises using the set of characteristics of the particular instance of the object to perform scene reconstruction in the CGR environment depicting the physical environment.

11. The method of claim 1 , wherein combining the set of characteristics of the particular instance of the object with the CGR environment depicting the physical environment comprises using the set of characteristics of the particular instance of the object to perform material detection in the CGR environment depicting the physical environment.

12. The method of claim 1 , wherein combining the set of characteristics of the particular instance of the object with the CGR environment depicting the physical environment comprises using the set of characteristics of the particular instance of the object to perform environment texturing in the CGR environment depicting the physical environment.

13. The method of claim 1 , wherein combining the set of characteristics of the particular instance of the object with the CGR environment depicting the physical environment comprises using the set of characteristics of the particular instance of the object to generate reflections of virtual objects in the CGR environment or reflections of real objects of the CGR environment in the virtual objects of the CGR environment depicting the physical environment.

14. The method of claim 1 , wherein combining the set of characteristics of the particular instance of the object with the CGR environment depicting the physical environment comprises using the set of characteristics of the particular instance of the object to perform physics simulations in the CGR environment depicting the physical environment.

15. The method of claim 1 , wherein combining the set of characteristics of the particular instance of the object with the CGR environment depicting the physical environment comprises using the set of characteristics of the particular instance of the object to reconstruct object portions in the CGR environment that are not in the image depicting the physical environment.

16. The method of claim 1 , wherein combining the set of characteristics of the particular instance of the object with the CGR environment depicting the physical environment comprises using the set of characteristics of the particular instance of the object to determine object or plane boundaries in the CGR environment depicting the physical environment.

17. The method of claim 1 , wherein combining the set of characteristics of the particular instance of the object with the CGR environment depicting the physical environment comprises using the set of characteristics of the particular instance of the object to effect removal of real objects from the CGR environment depicting the physical environment.

18. A system comprising:

a non-transitory computer-readable storage medium; and

one or more processors coupled to the non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium comprises program instructions that, when executed on the one or more processors, cause the system to perform operations comprising:

identifying an object type of an object depicted in an image of a physical environment;

determining a particular instance of the object based on the object type and the image, objects of the particular instance having a set of characteristics that differs from sets of characteristics of other instances of the object type;

obtaining the set of characteristics of the particular instance of the object depicted in the physical environment; and

combining the set of characteristics of the particular instance of the object with a CGR environment depicting the physical environment.

19. A non-transitory computer-readable storage medium, storing program instructions computer-executable on a computer to perform operations comprising:

identifying an object type of an object depicted in an image of a physical environment;

determining a particular instance of the object based on the object type and the image, objects of the particular instance having a set of characteristics that differs from sets of characteristics of other instances of the object type;

obtaining the set of characteristics of the particular instance of the object depicted in the physical environment; and

combining the set of characteristics of the particular instance of the object with a CGR environment depicting the physical environment.

20. The non-transitory computer-readable storage medium of claim 19 , wherein determining a particular instance of the object based on the object type and the image comprises using features extracted from a portion of the image depicting the object to generate a representation of the particular instance of the object; and

determining the particular instance using the representation.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Jun 26, 2023
From: BUCHACA, AITOR ALDOMA; MOELLER, ANDREAS N.; KUHN, MICHAEL
To: APPLE INC.
Reel/Frame 064061/0939 →
Continuity (3)
Continuation 16986737 · Aug 6, 2020
Provisional Application 62897625 · Sep 9, 2019
Related Publication 20240005652A1 · Jan 4, 2024
References Cited (46)
US 9327406B1 · Hinterstoisser et al. · 2016 [cited by applicant]
US 10346709B2 · Guerreiro et al. · 2019 [cited by applicant]
US 10984607B1 · Wang · 2021 [cited by applicant]
US 11043193B2 · Mak · 2021 [cited by examiner]
US 11106327B2 · Mani et al. · 2021 [cited by applicant]
US 20130093788A1 · Liu · 2013 [cited by examiner]
US 20140267404A1 · Mitchell · 2014 [cited by examiner]
US 20170200308A1 · Nguyen · 2017 [cited by applicant]
US 20170206714A1 · Arcas · 2017 [cited by examiner]
US 20170323488A1 · Mott et al. · 2017 [cited by applicant]
US 20170358141A1 · Stafford · 2017 [cited by examiner]
US 20180190022A1 · Zamir · 2018 [cited by applicant]
US 20180350056A1 · Cardenas Bernal · 2018 [cited by examiner]
US 20190050000A1 · Kennedy · 2019 [cited by examiner]
US 20190066380A1 · Berk · 2019 [cited by examiner]
US 20190094040A1 · Lewis · 2019 [cited by applicant]
US 20190102946A1 · Spivack · 2019 [cited by applicant]
US 20190347846A1 · Olson · 2019 [cited by applicant]
US 20190354175A1 · Torkos · 2019 [cited by examiner]
US 20190391726A1 · Iskandar · 2019 [cited by applicant]
US 20200117335A1 · Mani · 2020 [cited by examiner]
US 20200304375A1 · Chennai · 2020 [cited by examiner]
US 20200342668A1 · Chojnacka · 2020 [cited by examiner]
US 20210049901A1 · Young · 2021 [cited by applicant]
CN 109658449A · 2019 [cited by applicant]
EP 3171297 · 2017 [cited by applicant]
KR 1020180058175A · 2018 [cited by applicant]
KR 1020180086639A · 2018 [cited by applicant]
Korean Intellectual Property Office, Notice of Preliminary Rejection (with English translation) issued Sep. 13, 2023 which pertains to Korean Patent Application No. 10-2023-0108387. 7 pages. [cited by applicant]
U.S. Patent and Trademark Office, Non-Final Office Action, U.S. Appl. No. 16/986,737, 8 pages, Apr. 4, 2022. [cited by applicant]
U.S. Patent and Trademark Office, Notice of Allowance, U.S. Appl. No. 16/986,737, 6 pages, Dec. 14, 2022. [cited by applicant]
U.S. Patent and Trademark Office, Notice of Allowance, U.S. Appl. No. 16/986,737, 6 pages, Mar. 30, 2023. [cited by applicant]
Schwartz, Gabriel; Nishino, Ko; “Recongnizing Material Properties from Images”; arXiv: 1801.03127v1[cs:CV], pp. 1-14; Jan. 9, 2018. [cited by applicant]
Nesterenko, Ivan; “Realistic reflections and environment textures in ARKit 2.0”; pp. 1-4; Jun. 13, 2018. [cited by applicant]
Cai, Su; Wang, Xu; Gao, Mengnan; Yu, Shengquan; “Simulation Teaching in 3D Augmented Reality Environment”, 2012 IIAI International Conference on Advanced Applied Informatics; Conference Paper, pp. 83-88; Sep. 2012. [cited by applicant]
Scheer, Fabian; Abert, Oliver; Muller, Stefan; “Towards Using Realistic Ray Tracing in Augmented Reality Applications with Natural Lighting”; University of Koblenz-Landau; pp. 1-8; 2007. [cited by applicant]
Ropinski, Timo; Wachenfeld, Steffen; Hinrichs, Klaus; “Virtual Reflections for Augmented Reality Environments”; ICAT 2004; pp. 1-8, 2004. [cited by applicant]
Smart Home, “GrokStyle Makes Finding the Perfect Furniture As Easy As Taking a Picture”; pp. 1-13; 2019. [cited by applicant]
Gupta, Saurabh, Arbelaez, Pablo; Girshick, Ross; Malik, Jiendra; “Indoor Scene Understanding with RGB-D Images, Bottom-up Segmentation, Object Detection and Sematic Segmentation”; pp. 1-16; Nov. 21, 2014. [cited by applicant]
New World Notes; Wagner James Au reports on Virtual Worlds & VR; “iOS' ARKit Enables Reflection of Real Objects on Virtual Surfaces”; pp. 1-3; Jun. 12, 2018. [cited by applicant]
Mori, Shohei; Ikeda, Sei; Saito, Hideo; “A survey of diinished reality: Techniques for visually concealing, eliminating, and seeing through real objects”; IPSJ Transactions on Computer Vision and Applications; 2017, pp.… [cited by applicant]
Korean Intellectual Property Office, Notice of Preliminary Rejection (with English translation), Korean Patent Application No. 10-2020-0112155, 10 pages, Apr. 26, 2022. [cited by applicant]
Korean Intellectual Property Office, Notice of Preliminary Rejection (with English translation), Korean Patent Application No. 10-2020-0112155, 7 pages, Nov. 14, 2022. [cited by applicant]
China National Intellectual Property Administration, Notification of the First Office Action (with partial English Translation), Chinese Patent Application No. 202010938753.2, 12 pages, Apr. 26, 2024. [cited by applicant]
Korean Intellectual Property Office, Notice of Preliminary Rejection (with English translation), Korean Patent Application No. 10-2024-0108819, 7 pages, Jun. 9, 2025. [cited by applicant]
China National Intellectual Property Administration, Board Opinion (with English translation), Chinese Patent Application No. 202010938753.2, 7 pages, Jul. 10, 2025. [cited by applicant]