IP Library › Granted Patent US 12,749,207
Granted Patent B2
US 12,749,207 · App. 18/233,611 · Granted Sep 29, 2026

Electronic device for generating depth map and operation method thereof

Inventors: Serhii Koliiev (Kyiv, UA); Andrii Bugaiov (Kyiv, UA); Andrii Hyryla (Kyiv, UA); Andriy Begun (Kyiv, UA)
Assignee: SAMSUNG ELECTRONICS CO., LTD.
G06T7/507G06T7/55G06T2207/10028
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Quick Facts
Patent No.
US 12,749,207
App. No.
18/233,611
Granted
Sep 29, 2026
Kind
B2
Abstract

A method of generating a depth map corresponding to input data includes providing light to a target object in a plurality of patterns that change over time, obtaining a plurality of captured images respectively corresponding to the plurality of patterns, by photographing the target object to which the light is provided, obtaining the input data by preprocessing the plurality of captured images and generating the depth map based on the input data.

Claims (54)

1 . A method of generating a depth map corresponding to input data, the method comprising:

providing light to a target object in a plurality of patterns that change over time;

obtaining a plurality of captured images respectively corresponding to the plurality of patterns, by photographing the target object to which the light is provided;

obtaining a plurality of illumination images respectively corresponding to the plurality of patterns by photographing a light-emitting device configured to provide the light;

obtaining the input data by preprocessing the plurality of captured images and the plurality of illumination images; and

generating the depth map based on the input data.

2 . The method of claim 1 , wherein each of the plurality of patterns comprises a light-emitting region to which the light is provided, and

wherein a position of the light-emitting region changes over time.

3 . The method of claim 1 , wherein each of the plurality of patterns comprises a light-emitting region to which the light is provided, and

wherein an area of at least a partial region of the light-emitting region changes over time.

4 . The method of claim 1 , wherein an illumination of the light in each of the plurality of patterns changes over time.

5 . The method of claim 1 , further comprising obtaining characteristic information of the light-emitting device,

wherein the obtaining of the input data comprises obtaining the input data by preprocessing the plurality of captured images, the plurality of illumination images, and the characteristic information.

6 . The method of claim 5 , wherein the characteristic information comprises information about a size of the light-emitting device.

7 . The method of claim 5 , further comprising:

providing sub-light to the target object; and

obtaining sub-characteristic information of a sub-light-emitting device configured to provide the sub-light,

wherein the obtaining of the plurality of captured images comprises obtaining the plurality of captured images by photographing the target object to which the light and the sub-light are provided, and

wherein the obtaining of the input data comprises obtaining the input data by preprocessing the plurality of captured images, the plurality of illumination images, the characteristic information, and the sub-characteristic information.

8 . The method of claim 1 , further comprising comparing an intensity of an ambient illumination of the target object with an intensity of a threshold illumination,

wherein the light is provided to the target object based on the intensity of the ambient illumination being less than or equal to the intensity of the threshold illumination, and

wherein the threshold illumination is a maximum illumination at which the plurality of captured images reflecting changes in the plurality of patterns that change over time are able to be obtained.

9 . The method of claim 1 , wherein the generating of the depth map comprises generating the depth map by providing a depth map generation module with the input data, and

wherein the depth map generation module comprises an autoencoder.

10 . An electronic device for generating a depth map corresponding to input data, the electronic device comprising:

a light-emitting device configured to provide light to a target object in a plurality of patterns that change over time;

a measuring device comprising at least one camera configured to obtain a plurality of captured images respectively corresponding to the plurality of patterns by photographing the target object to which the light is provided;

a memory storing one or more instructions; and

at least one processor configured to execute the one or more instructions stored in the memory to:

receive a plurality of illumination images that are previously captured, the plurality of illumination images being obtained by photographing the light-emitting device, the plurality of illumination images respectively corresponding to the plurality of patterns;

obtain the input data by preprocessing the plurality of captured images and the plurality of illumination images, and

generate the depth map based on the input data.

11 . The electronic device of claim 10 , wherein each of the plurality of patterns comprises a light-emitting region to which the light is provided, and

wherein a position of the light-emitting region changes over time.

12 . The electronic device of claim 10 , wherein each of the plurality of patterns comprises a light-emitting region to which the light is provided, and

wherein an area of at least a partial region of the light-emitting region changes over time.

13 . The electronic device of claim 10 , wherein an illumination of the light in each of the plurality of patterns changes over time.

14 . The electronic device of claim 10 , wherein the at least one processor is further configured to execute the one or more instructions to

receive characteristic information of the light-emitting device, and

obtain the input data by preprocessing the plurality of captured images, the plurality of illumination images, and the characteristic information.

15 . The electronic device of claim 14 , wherein the characteristic information comprises information about a size of the light-emitting device.

16 . The electronic device of claim 14 , further comprising a sub-light-emitting device configured to provide sub-light to the target object,

wherein the measuring device is further configured to obtain the plurality of captured images by photographing the target object to which the light and the sub-light are provided, and

wherein the at least one processor is further configured to execute the one or more instructions to

receive sub-characteristic information of the sub-light-emitting device, and

obtain the input data by preprocessing the plurality of captured images, the plurality of illumination images, the characteristic information, and the sub-characteristic information.

17 . The electronic device of claim 10 , wherein the at least one processor is further configured to execute the one or more instructions to generate the depth map by providing a depth map generation module with the input data, and

wherein the depth map generation module comprises an autoencoder.

18 . A non-transitory computer-readable recording medium storing instructions that, when executed by at least one processor, cause the at least one processor to:

provide light toward a target object in a plurality of patterns that change over time;

obtain a plurality of captured images respectively corresponding to the plurality of patterns, by photographing the target object toward which the light is provided;

obtain a plurality of illumination images respectively corresponding to the plurality of patterns by photographing a light-emitting device configured to provide the light;

obtain input data by preprocessing the plurality of captured images and the plurality of illumination images; and

generate a depth map based on the input data.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Aug 14, 2023
From: KOLIIEV, SERHII; BUGAIOV, ANDRII; HYRYLA, ANDRII; BEGUN, ANDRIY
To: SAMSUNG ELECTRONICS CO., LTD.
Reel/Frame 064581/0130 →
Priority Claims (1)
KR 10-2022-0075778 · Jun 21, 2022 · national
Continuity (2)
Continuation PCTKR2023008553 · Jun 20, 2023
Related Publication 20230410335A1 · Dec 21, 2023
References Cited (70)
US 6438272B1 · Huang et al. · 2002 [cited by applicant]
US 7545516B2 · Jia et al. · 2009 [cited by applicant]
US 7570370B2 · Steinbichler et al. · 2009 [cited by applicant]
US 8773508B2 · Daniel et al. · 2014 [cited by applicant]
US 10498109B1 · Tsai · 2019 [cited by examiner]
US 11113832B2 · Wadhwa et al. · 2021 [cited by applicant]
US 11393112B2 · Lee et al. · 2022 [cited by applicant]
US 20030160970A1 · Basu et al. · 2003 [cited by applicant]
US 20070165246A1 · Kimmel · 2007 [cited by applicant]
US 20080118143A1 · Gordon et al. · 2008 [cited by applicant]
US 20080279446A1 · Hassebrook et al. · 2008 [cited by applicant]
US 20090238449A1 · Zhang et al. · 2009 [cited by applicant]
US 20130162811A1 · Song · 2013 [cited by examiner]
US 20130258060A1 · Kotake · 2013 [cited by applicant]
US 20140240464A1 · Lee · 2014 [cited by applicant]
US 20150062558A1 · Koppal · 2015 [cited by examiner]
US 20150116460A1 · Jouet · 2015 [cited by examiner]
US 20150229907A1 · Bridges · 2015 [cited by examiner]
US 20150304617A1 · Chang · 2015 [cited by examiner]
US 20150304638A1 · Cho · 2015 [cited by examiner]
US 20150312552A1 · Lu · 2015 [cited by examiner]
US 20160261850A1 · Debevec et al. · 2016 [cited by applicant]
US 20170132790A1 · Jeong et al. · 2017 [cited by applicant]
US 20180063403A1 · Ryu · 2018 [cited by examiner]
US 20180073873A1 · Takao et al. · 2018 [cited by applicant]
US 20180084240A1 · Campbell · 2018 [cited by examiner]
US 20180173947A1 · Kang · 2018 [cited by examiner]
US 20180260623A1 · Kang · 2018 [cited by examiner]
US 20190005711A1 · Marin et al. · 2019 [cited by applicant]
US 20190285405A1 · Raz · 2019 [cited by applicant]
US 20200007853A1 · Mlinar · 2020 [cited by examiner]
US 20200045297A1 · Van Der Sijde · 2020 [cited by examiner]
US 20200074698A1 · Schaffer et al. · 2020 [cited by applicant]
US 20200142069A1 · Onal · 2020 [cited by examiner]
US 20200286248A1 · Shi · 2020 [cited by examiner]
US 20200404243A1 · Saphier · 2020 [cited by examiner]
US 20210065390A1 · Lee · 2021 [cited by examiner]
US 20210183089A1 · Wadhwa · 2021 [cited by examiner]
US 20210201517A1 · Yang · 2021 [cited by examiner]
US 20210264625A1 · Atanassov · 2021 [cited by examiner]
US 20210273795A1 · Dahlberg · 2021 [cited by examiner]
US 20210295539A1 · Kikuchi · 2021 [cited by examiner]
US 20210341620A1 · Raz · 2021 [cited by examiner]
US 20210358211A1 · Kim et al. · 2021 [cited by applicant]
US 20220292703A1 · Matsumoto · 2022 [cited by examiner]
US 20220353447A1 · Price · 2022 [cited by examiner]
US 20230177768A1 · Wang · 2023 [cited by examiner]
US 20230410335A1 · Koliiev · 2023 [cited by examiner]
US 20250052564A1 · Song · 2025 [cited by examiner]
CN 112825491A · 2021 [cited by examiner]
JP 6290512B2 · 2018 [cited by applicant]
JP 2021501946A · 2021 [cited by applicant]
KR 100210654 · 1999 [cited by applicant]
KR 100993486B1 · 2010 [cited by examiner]
KR 1020140066638A · 2014 [cited by applicant]
KR 1020150140838A · 2015 [cited by applicant]
KR 101624120B1 · 2016 [cited by applicant]
KR 1020170027776A · 2017 [cited by applicant]
KR 1020170055163A · 2017 [cited by applicant]
KR 1020190124542A · 2019 [cited by applicant]
KR 20190124542A · 2019 [cited by examiner]
KR 102238573B1 · 2021 [cited by applicant]
KR 102287472B1 · 2021 [cited by applicant]
KR 1020220000430A · 2022 [cited by applicant]
KR 1020220075283A · 2022 [cited by applicant]
Communication dated May 14, 2025 issued by the European Patent Office in European Patent Application No. 23827496.3. [cited by applicant]
ISR and Written Opinion (PCT/ISA/220, PCT/ISA/210, and PCT/ISA/237) issued Sep. 12, 2023 by the ISA in International Application No. PCT/KR2023/008553. [cited by applicant]
Alhashim, Ibraheem et al., “High Quality Monocular Depth Estimation via Transfer Learning”, arXiv:1812.11941v2 [cs.CV], Mar. 10, 2019. (12 pages total). [cited by applicant]
Communication dated Aug. 22, 2025, issued by European Patent Office in European Patent Application No. 23827496.3. [cited by applicant]
Office Action issued Jul. 20, 2026 by the Korean Ministry of Intellectual Property for KR Patent Application No. 10-2022-0075778. [cited by applicant]