IP Library › Granted Patent US 12,646,278
Granted Patent B2
US 12,646,278 · App. 18/636,751 · Granted Jun 2, 2026

Annotation of 3D models with signs of use visible in 2D images

Inventors: Constantin Cosmin Atanasoaei (Chavannes-pres-Renens, CH); Daniel Milan Lütgehetmann (Lausanne, CH); Dimitri Zaganidis (Granges, CH); John Rahmon (Lausanne, CH); Michele De Gruttola (Geneva, CH)
Assignee: INAIT SA
G06T19/20G06T7/74G06T7/90G06T2207/10024G06T2219/004G06T2219/2021
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 12,646,278
App. No.
18/636,751
Granted
Jun 2, 2026
Kind
B2
Abstract

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for annotation of 3D models with signs of use that are visible in 2D images. In one aspect, methods are performed by data processing apparatus. The methods can include projecting signs of use in a relatively larger field of view image of an instance of an object onto a 3D model of the object based on a pose of the instance in the relatively larger field of view image, and estimating a relative pose of the instance of the object in a relatively smaller field of view image based on matches between the signs of use in the relatively larger field of view image and the same signs of use in the relatively smaller field of view image.

Claims (59)

1 . A method performed by data processing apparatus, the method comprising:

estimating a relative pose of an instance of an object in a relatively smaller field of view image based on matched signs of use of the object that appear in both a relatively larger field of view image and the relatively smaller field of view image;

identifying, using the relative pose, matched signs of use that satisfy a threshold likelihood that the matched signs of use are correct matches; and

projecting the matched signs of use that satisfy the threshold likelihood onto a 3D model of the object.

2 . The method of claim 1 further comprising:

determining a dominant color of the instance of the object in the relatively smaller field of view image;

identifying regions in the relatively larger field of view image, in the relatively smaller field of view image, or in both the relatively larger field of view image and the relatively smaller field of view image that deviate from the dominant color; and

matching the identified regions to match the signs of use in the relatively larger field of view image and the signs of use in the relatively smaller field of view image.

3 . The method of claim 1 , further comprising:

determining a deviation from ideality in the instance of the object in the relatively smaller field of view image; and

matching the deviation in the relatively smaller field of view image to the relatively larger field of view image to match the signs of use in the relatively larger field of view image and the signs of use in the relatively smaller field of view image.

4 . The method of claim 1 , further comprising:

computing hypothetical positions of signs of use in the relatively smaller field of view image using the estimated pose;

comparing the hypothetical positions with actual positions of the signs of use in the relatively smaller field of view image; and

identifying, based on the comparison, a subset of the signs of use that are improperly projected onto the 3D model.

5 . The method of claim 1 , wherein projecting the matched signs of use onto the 3D model comprises determining a pose of the relatively larger field of view image.

6 . The method of claim 1 , further comprising:

computing hypothetical positions of the signs of use in the relatively smaller field of view image using the estimated pose and the projection of signs of use onto the 3D model;

comparing the hypothetical positions with actual positions of the signs of use in the relatively smaller field of view image; and

identifying, based on the comparison, a subset of the signs of use that are improperly projected onto the 3D model.

7 . The method of claim 6 , wherein the improperly projected signs of use are identified based on a positional deviation between the hypothetical positions and the actual positions of the signs of use in the relatively smaller field of view image.

8 . The method of claim 6 , wherein the method comprises:

filtering the subset of the signs of use that are improperly projected onto the 3D model from the matches to establish a proper subset of the matches; and

re-estimating the relative pose of the instance of the object in the relatively smaller field of view image based on subset of the matches; and

projecting the proper subset of the matches onto the 3D model of the object using the re-estimated relative pose.

9 . The method of claim 1 , wherein projecting the matched signs of use that satisfy the threshold likelihood onto the 3D model of the object comprises:

identifying a region of the relatively smaller field of view image that includes a first sign of use that satisfies the threshold likelihood;

matching the first sign of use to a first of the signs of use in the relatively larger field of view image; and

projecting the first of the signs of use in the relatively larger field of view image onto the 3D model of the object.

10 . The method of claim 1 , further comprising deforming the 3D model using projected matched signs of use.

11 . At least one computer-readable storage medium encoded with executable instructions that, when executed by at least one processor, cause the at least one processor to perform operations comprising:

estimating a relative pose of an instance of an object in a relatively smaller field of view image based on matched signs of use of the object that appear in both a relatively larger field of view image and the relatively smaller field of view image;

identifying, using the relative pose, matched signs of use that satisfy a threshold likelihood that the matched signs of use are correct matches; and

projecting the matched signs of use that satisfy the threshold likelihood onto a 3D model of the object.

12 . The computer-readable storage medium of claim 11 , wherein the operations further comprise:

determining a dominant color of the instance of the object in the relatively smaller field of view image;

identifying regions in the relatively larger field of view image, in the relatively smaller field of view image, or in both the relatively larger field of view image and the relatively smaller field of view image that deviate from the dominant color; and

matching the identified regions to match the signs of use in the relatively larger field of view image and the signs of use in the relatively smaller field of view image.

13 . The computer-readable storage medium of claim 12 , wherein the operations further comprise:

determining a deviation from ideality in the instance of the object in the relatively smaller field of view image; and

matching the deviation in the relatively smaller field of view image to the relatively larger field of view image to match the signs of use in the relatively larger field of view image and the signs of use in the relatively smaller field of view image.

14 . The computer-readable storage medium of claim 11 , wherein the operations further comprise:

computing hypothetical positions of signs of use in the relatively smaller field of view image using the estimated pose;

comparing the hypothetical positions with actual positions of the signs of use in the relatively smaller field of view image; and

identifying, based on the comparison, a subset of the signs of use that are improperly projected onto the 3D model.

15 . The computer-readable storage medium of claim 11 , wherein projecting the matched signs of use onto the 3D model comprises determining a pose of the relatively larger field of view image.

16 . The computer-readable storage medium of claim 11 , further comprising:

computing hypothetical positions of the signs of use in the relatively smaller field of view image using the estimated pose and the projection of signs of use onto the 3D model;

comparing the hypothetical positions with actual positions of the signs of use in the relatively smaller field of view image; and identifying, based on the comparison, a subset of the signs of use that are improperly projected onto the 3D model.

17 . The computer-readable storage medium of claim 16 , wherein the improperly projected signs of use are identified based on a positional deviation between the hypothetical positions and the actual positions of the signs of use in the relatively smaller field of view image.

18 . A method performed by a data processing apparatus, the method comprising:

generating a 3D model of an object;

projecting first signs of use onto the 3D model of the object, wherein the first signs of use comprise signs of use that are visible in a relatively larger field of view image of an instance of the object and a relatively smaller field of view image of the instance of object; and

eliminating improperly projected signs of use from the projections onto the 3D model of the object, wherein the improperly projected signs of use comprise signs of use with actual positions in the relatively smaller field of view image of the instance of object that differ from hypothetical positions of the signs of use in the relatively smaller field of view image of the instance of object, the hypothetical positions computed using an estimated pose of the instance of the object in the relatively smaller field of view image of the instance of object.

19 . The method of claim 18 , wherein projecting the first signs of use onto the 3D model comprises:

identifying a region of the relatively smaller field of view image that includes a sign of use;

matching the sign of use to a corresponding sign of use in the relatively larger field of view image; and

projecting the sign of use in the relatively larger field of view image onto the 3D model of the object.

20 . The method of claim 18 , further comprising deforming the 3D model using projected matched signs of use.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Sep 25, 2024
From: ATANASOAEI, CONSTANTIN COSMIN; LÜTGEHETMANN, DANIEL MILAN; ZAGANIDIS, DIMITRI; RAHMON, JOHN; DE GRUTTOLA, MICHELE
To: INAIT SA
Reel/Frame 069047/0770 →
Priority Claims (1)
GR 20210100106 · Feb 18, 2021 · national
Continuity (3)
Continuation 18091460 · Dec 30, 2022
Continuation 17190646 · Mar 3, 2021
Related Publication 20240404229A1 · Dec 5, 2024
References Cited (147)
US 6246954B1 · Berstis et al. · 2001 [cited by applicant]
US 6826301B2 · Glickman · 2004 [cited by applicant]
US 6969809B2 · Rainey · 2005 [cited by applicant]
US 7889931B2 · Webb et al. · 2011 [cited by applicant]
US 8515152B2 · Sir · 2013 [cited by applicant]
US 8712893B1 · Brandmaier et al. · 2014 [cited by applicant]
US 9262789B1 · Tofte · 2016 [cited by applicant]
US 9307234B1 · Greiner et al. · 2016 [cited by applicant]
US 9489696B1 · Tofte · 2016 [cited by applicant]
US 9870609B2 · Kompalli et al. · 2018 [cited by applicant]
US 9886771B1 · Chen et al. · 2018 [cited by applicant]
US 10580075B1 · Brandmaier et al. · 2020 [cited by applicant]
US 10657707B1 · Leise · 2020 [cited by applicant]
US 10789786B2 · Zhang et al. · 2020 [cited by applicant]
US 11288789B1 · Chen · 2022 [cited by examiner]
US 11544914B2 · Atanasoaei et al. · 2023 [cited by applicant]
US 11983836B2 · Atanasoaei et al. · 2024 [cited by applicant]
US 20020007289A1 · Malin et al. · 2002 [cited by applicant]
US 20040113783A1 · Yagesh · 2004 [cited by applicant]
US 20040183673A1 · Nageli · 2004 [cited by applicant]
US 20050108065A1 · Dorfstatter · 2005 [cited by applicant]
US 20060103568A1 · Powell et al. · 2006 [cited by applicant]
US 20060114531A1 · Webb et al. · 2006 [cited by applicant]
US 20060124377A1 · Lichtinger et al. · 2006 [cited by applicant]
US 20060267799A1 · Mendelson · 2006 [cited by applicant]
US 20070164862A1 · Dhanjal et al. · 2007 [cited by applicant]
US 20080255887A1 · Gruter · 2008 [cited by applicant]
US 20080267487A1 · Sir · 2008 [cited by applicant]
US 20080281658A1 · Siessman · 2008 [cited by applicant]
US 20090033540A1 · Breed et al. · 2009 [cited by applicant]
US 20090256736A1 · Orr · 2009 [cited by applicant]
US 20100067420A1 · Twitchell, Jr. · 2010 [cited by applicant]
US 20100073194A1 · Ghazarian · 2010 [cited by applicant]
US 20100100319A1 · Trinko et al. · 2010 [cited by applicant]
US 20100106413A1 · Mudalige · 2010 [cited by applicant]
US 20100265104A1 · Zlojutro · 2010 [cited by applicant]
US 20100265325A1 · Lo et al. · 2010 [cited by applicant]
US 20100271196A1 · Schmitt et al. · 2010 [cited by applicant]
US 20110068954A1 · McQuade et al. · 2011 [cited by applicant]
US 20110102232A1 · Orr et al. · 2011 [cited by applicant]
US 20120029759A1 · Suh et al. · 2012 [cited by applicant]
US 20120029764A1 · Payne et al. · 2012 [cited by applicant]
US 20120062395A1 · Sonnabend et al. · 2012 [cited by applicant]
US 20140229207A1 · Swamy et al. · 2014 [cited by applicant]
US 20140309805A1 · Ricci · 2014 [cited by applicant]
US 20150287130A1 · Vercollone et al. · 2015 [cited by applicant]
US 20150332512A1 · Siddiqui et al. · 2015 [cited by applicant]
US 20160239922A1 · Jimenez · 2016 [cited by applicant]
US 20170011294A1 · Jagannathan · 2017 [cited by applicant]
US 20170293894A1 · Taliwal · 2017 [cited by applicant]
US 20180005453A1 · Siddiqui et al. · 2018 [cited by applicant]
US 20180040039A1 · Wells et al. · 2018 [cited by applicant]
US 20180182039A1 · Wang et al. · 2018 [cited by applicant]
US 20180189949A1 · Lapiere et al. · 2018 [cited by applicant]
US 20180197048A1 · Micks · 2018 [cited by applicant]
US 20180260793A1 · Li et al. · 2018 [cited by applicant]
US 20180293552A1 · Zhang et al. · 2018 [cited by applicant]
US 20180293664A1 · Zhang et al. · 2018 [cited by applicant]
US 20180293806A1 · Zhang et al. · 2018 [cited by applicant]
US 20180300576A1 · Dalyac et al. · 2018 [cited by applicant]
US 20190213563A1 · Zhang et al. · 2019 [cited by applicant]
US 20190213689A1 · Zhang et al. · 2019 [cited by applicant]
US 20190213804A1 · Zhang et al. · 2019 [cited by applicant]
US 20190259216A1 · Gambaretto et al. · 2019 [cited by applicant]
US 20200234451A1 · Holzer et al. · 2020 [cited by applicant]
US 20200236343A1 · Holzer et al. · 2020 [cited by applicant]
US 20200258309A1 · Holzer et al. · 2020 [cited by applicant]
US 20210375032A1 · Leise · 2021 [cited by examiner]
US 20220137701A1 · Bowman et al. · 2022 [cited by applicant]
US 20220262083A1 · Atanasoaei et al. · 2022 [cited by applicant]
US 20230012230A1 · Geyzersky · 2023 [cited by examiner]
US 20230266586A1 · Tavangar et al. · 2023 [cited by applicant]
US 20230351713A1 · Atanasoaei et al. · 2023 [cited by applicant]
US 20240005558A1 · Norris · 2024 [cited by examiner]
US 20240404229A1 · Atanasoaei · 2024 [cited by examiner]
US 20250166171A1 · Rombakh · 2025 [cited by examiner]
CN 1658559 · 2005 [cited by applicant]
CN 102376071 · 2012 [cited by applicant]
CN 103310223 · 2013 [cited by applicant]
CN 104268783 · 2015 [cited by applicant]
CN 104517442 · 2015 [cited by applicant]
CN 105488576 · 2016 [cited by applicant]
CN 105488789 · 2016 [cited by applicant]
CN 105678622 · 2016 [cited by applicant]
CN 105719188 · 2016 [cited by applicant]
CN 105956667 · 2016 [cited by applicant]
CN 106021548 · 2016 [cited by applicant]
CN 106022929 · 2016 [cited by applicant]
CN 106056451 · 2016 [cited by applicant]
CN 106127747 · 2016 [cited by applicant]
CN 106203644 · 2016 [cited by applicant]
CN 106250812 · 2016 [cited by applicant]
CN 106296118 · 2017 [cited by applicant]
CN 106296126 · 2017 [cited by applicant]
CN 106370128 · 2017 [cited by applicant]
CN 106372651 · 2017 [cited by applicant]
CN 106504248 · 2017 [cited by applicant]
CN 110147719A · 2019 [cited by applicant]
CN 110675453A · 2020 [cited by applicant]
JP H0778022 · 1995 [cited by applicant]
JP H0981739 · 1997 [cited by applicant]
JP H10134187A · 1998 [cited by applicant]
JP 2001344463 · 2001 [cited by applicant]
JP 2002183338 · 2002 [cited by applicant]
JP 2003132170 · 2003 [cited by applicant]
JP 2003170817 · 2003 [cited by applicant]
JP 2003196511 · 2003 [cited by applicant]
JP 2003226230 · 2003 [cited by applicant]
JP 2003346021 · 2003 [cited by applicant]
JP 2005107722 · 2005 [cited by applicant]
JP 2006065688A · 2006 [cited by applicant]
JP 2006164022 · 2006 [cited by applicant]
JP 3839822 · 2006 [cited by applicant]
JP 2008298685A · 2008 [cited by applicant]
JP 2011215973 · 2011 [cited by applicant]
JP 5321784 · 2013 [cited by applicant]
JP 2015184143 · 2015 [cited by applicant]
JP 2016038790A · 2016 [cited by applicant]
JP 2017062776 · 2017 [cited by applicant]
JP 2018112999 · 2018 [cited by applicant]
JP 2018537798 · 2018 [cited by applicant]
KR 20160018944 · 2016 [cited by applicant]
KR 20160019514 · 2016 [cited by applicant]
KR 20170016778 · 2017 [cited by applicant]
TW M478859 · 2014 [cited by applicant]
WO WO2005109263 · 2005 [cited by applicant]
WO WO2013093932 · 2013 [cited by applicant]
WO WO2017055878 · 2017 [cited by applicant]
WO WO2019198562A1 · 2019 [cited by applicant]
Feng et al., “Research on honeycomb sandwich composite structure damage detection based on matching pursuit method,” Chinese Journal of Scientific Instrument, Apr. 2012, 33(4):836-842 (with English abstract). [cited by applicant]
Github.com [online], “Detectron2,” Nov. 2020, retrieved on Mar. 5, 2021, retrieved from URL<https://github.com/facebookresearch/detectron2>, 78 pages. [cited by applicant]
International Preliminary Report on Patentability in International Appln. No. PCT/EP2022/051811, mailed on Aug. 31, 2023, 9 pages. [cited by applicant]
International Search Report and Written Opinion in International Appln. No. PCT/EP2022/051811, mailed on Jun. 3, 2022, 16 pages. [cited by applicant]
Jayawardena, “Image based automatic vehicle damage detection,” Thesis for the degree of Doctor of Philosophy, Australian National University, Nov. 2013, retrieved from URL<http://hdl.handle.net/1885/11072>, 199 pages. [cited by applicant]
Office Action in U.S. Appl. No. 18/091,460, mailed on Sep. 14, 2023, 6 pages. [cited by applicant]
Opencv.org [online], “Camera calibration and 3D reconstructions,” Jul. 18, 2020, retrieved from URL <https://docs.opency.org/4.4.0/d0/doc/group_calin3d.html#gas549c2075fac14829ff4a58bc931c033d>, 78 pages. [cited by applicant]
Opencv.org [online], “Pose Estimation,” Mar. 15, 2021, retrieved from URL <https://docs.opency.org/master/d7/d53/tutorial_py_pose.html>, 3 pages. [cited by applicant]
Ren et al., “Faster R-CNN: Towards Real-Time Object Detection With Region Proposal Networks,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Jun. 2017, 39(6):1137-1149. [cited by applicant]
Rodrigues et al., “Confidence factor and feature selection for semi-supervised multi-label classification methods,” International Joint Conference on Neural Networks (IJCNN), Beijing, China, Jul. 6-11, 2014, pp. 864-871. [cited by applicant]
Snavely et al., “Modeling the world from internet photo collections,” International Journal of Computer Vision, Nov. 2008, 80(2):189-210. [cited by applicant]
Wan, “Structural Damage Monitoring Based on Vibration Modal Analysis and Neural Network Technique,” China Master's Theses Full-Text Database Information Technology, Apr. 2003, 91 pages (with English abstract). [cited by applicant]
Wikipedia.org [online], “Harris Corner Detector,” Jan. 21, 2021, retrieved on Mar. 5, 2021, retrieved from URL <https://en.wikipedia.org/wiki/Harris_Corner_Detector>, 6 pages. [cited by applicant]
Wikipedia.org [online], “OPTICS algorithm,” Feb. 1, 2021, retrieved on Mar. 5, 2021, retrieved on URL <https://en.wikipedia.org/wiki/OPTICS_algorithm>, 5 pages. [cited by applicant]
Wikipedia.org [online], “Scale-invariant feature transform,” Feb. 15, 2021, retrieved on Mar. 5, 2021, retrieved on URL <https://en.wikipedia.org/wiki/Scale-invariant_feature_transform>, 16 pages. [cited by applicant]
Wikipedia.org [online}, “DBSCAN,” Feb. 13, 2021, retrieved on Mar. 5, 2021, retrieved from URL <https://en.wikipedia.org/wiki/DBSCAN>, 8 pages. [cited by applicant]
Office Action in Chinese Appln. No. 202280015691.5, mailed on Jun. 9, 2025, 29 pages (with English translation). [cited by applicant]
Office Action in Japanese Appln. No. 2023-548937, mailed on Sep. 2, 2025, 5 pages (with English translation). [cited by applicant]