3D Registration of Indoor Point Clouds for Augmented Reality

Bilawal Mahmood, SangUk Han

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

Abstract

For interactive visualization in AR devices, feature descriptors of point clouds (as-designed model and as-built model) are corresponded and registered. However, point cloud of indoor environment has lots of similar feature descriptors (e.g., indoor scene with similar doors and windows), which leads to many false correspondences and affect registration accuracy. This paper proposes a random sample consensus (RANSAC)-based false correspondence rejection to compute accurate transformation for the registration of such 3D point clouds. Point cloud data is collected from rooms and a hallway of a campus building, and transformation accuracy for the registration of those point clouds is tested. The results show that RANSAC-based false correspondence rejection gives transformation accuracy of 0.017 radians and 0.1924 meters in aligning two point cloud models, and hence the proposed registration approach of a model point cloud with scene point cloud may provide a foundation to accurately implement the AR on a construction jobsite.

Original languageEnglish
Title of host publicationComputing in Civil Engineering 2019
Subtitle of host publicationVisualization, Information Modeling, and Simulation - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019
EditorsChao Wang, Yong K. Cho, Fernanda Leite, Amir Behzadan
PublisherAmerican Society of Civil Engineers (ASCE)
Pages1-8
Number of pages8
ISBN (Electronic)9780784482421
DOIs
StatePublished - 2019 Jan 1
EventASCE International Conference on Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation, i3CE 2019 - Atlanta, United States
Duration: 2019 Jun 172019 Jun 19

Publication series

NameComputing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019

Conference

ConferenceASCE International Conference on Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation, i3CE 2019
CountryUnited States
CityAtlanta
Period19/06/1719/06/19

Fingerprint

Augmented reality
Visualization

Cite this

Mahmood, B., & Han, S. (2019). 3D Registration of Indoor Point Clouds for Augmented Reality. In C. Wang, Y. K. Cho, F. Leite, & A. Behzadan (Eds.), Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019 (pp. 1-8). (Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019). American Society of Civil Engineers (ASCE). https://doi.org/10.1061/9780784482421.001
Mahmood, Bilawal ; Han, SangUk. / 3D Registration of Indoor Point Clouds for Augmented Reality. Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019. editor / Chao Wang ; Yong K. Cho ; Fernanda Leite ; Amir Behzadan. American Society of Civil Engineers (ASCE), 2019. pp. 1-8 (Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019).
@inproceedings{ef9be54b4c9b46c2bf7ca2cbb0d4c687,
title = "3D Registration of Indoor Point Clouds for Augmented Reality",
abstract = "For interactive visualization in AR devices, feature descriptors of point clouds (as-designed model and as-built model) are corresponded and registered. However, point cloud of indoor environment has lots of similar feature descriptors (e.g., indoor scene with similar doors and windows), which leads to many false correspondences and affect registration accuracy. This paper proposes a random sample consensus (RANSAC)-based false correspondence rejection to compute accurate transformation for the registration of such 3D point clouds. Point cloud data is collected from rooms and a hallway of a campus building, and transformation accuracy for the registration of those point clouds is tested. The results show that RANSAC-based false correspondence rejection gives transformation accuracy of 0.017 radians and 0.1924 meters in aligning two point cloud models, and hence the proposed registration approach of a model point cloud with scene point cloud may provide a foundation to accurately implement the AR on a construction jobsite.",
author = "Bilawal Mahmood and SangUk Han",
year = "2019",
month = "1",
day = "1",
doi = "10.1061/9780784482421.001",
language = "English",
series = "Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019",
publisher = "American Society of Civil Engineers (ASCE)",
pages = "1--8",
editor = "Chao Wang and Cho, {Yong K.} and Fernanda Leite and Amir Behzadan",
booktitle = "Computing in Civil Engineering 2019",

}

Mahmood, B & Han, S 2019, 3D Registration of Indoor Point Clouds for Augmented Reality. in C Wang, YK Cho, F Leite & A Behzadan (eds), Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019. Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019, American Society of Civil Engineers (ASCE), pp. 1-8, ASCE International Conference on Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation, i3CE 2019, Atlanta, United States, 19/06/17. https://doi.org/10.1061/9780784482421.001

3D Registration of Indoor Point Clouds for Augmented Reality. / Mahmood, Bilawal; Han, SangUk.

Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019. ed. / Chao Wang; Yong K. Cho; Fernanda Leite; Amir Behzadan. American Society of Civil Engineers (ASCE), 2019. p. 1-8 (Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019).

Research output: Chapter in Book/Report/Conference proceedingConference contributionResearchpeer-review

TY - GEN

T1 - 3D Registration of Indoor Point Clouds for Augmented Reality

AU - Mahmood, Bilawal

AU - Han, SangUk

PY - 2019/1/1

Y1 - 2019/1/1

N2 - For interactive visualization in AR devices, feature descriptors of point clouds (as-designed model and as-built model) are corresponded and registered. However, point cloud of indoor environment has lots of similar feature descriptors (e.g., indoor scene with similar doors and windows), which leads to many false correspondences and affect registration accuracy. This paper proposes a random sample consensus (RANSAC)-based false correspondence rejection to compute accurate transformation for the registration of such 3D point clouds. Point cloud data is collected from rooms and a hallway of a campus building, and transformation accuracy for the registration of those point clouds is tested. The results show that RANSAC-based false correspondence rejection gives transformation accuracy of 0.017 radians and 0.1924 meters in aligning two point cloud models, and hence the proposed registration approach of a model point cloud with scene point cloud may provide a foundation to accurately implement the AR on a construction jobsite.

AB - For interactive visualization in AR devices, feature descriptors of point clouds (as-designed model and as-built model) are corresponded and registered. However, point cloud of indoor environment has lots of similar feature descriptors (e.g., indoor scene with similar doors and windows), which leads to many false correspondences and affect registration accuracy. This paper proposes a random sample consensus (RANSAC)-based false correspondence rejection to compute accurate transformation for the registration of such 3D point clouds. Point cloud data is collected from rooms and a hallway of a campus building, and transformation accuracy for the registration of those point clouds is tested. The results show that RANSAC-based false correspondence rejection gives transformation accuracy of 0.017 radians and 0.1924 meters in aligning two point cloud models, and hence the proposed registration approach of a model point cloud with scene point cloud may provide a foundation to accurately implement the AR on a construction jobsite.

UR - http://www.scopus.com/inward/record.url?scp=85068795553&partnerID=8YFLogxK

U2 - 10.1061/9780784482421.001

DO - 10.1061/9780784482421.001

M3 - Conference contribution

T3 - Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019

SP - 1

EP - 8

BT - Computing in Civil Engineering 2019

A2 - Wang, Chao

A2 - Cho, Yong K.

A2 - Leite, Fernanda

A2 - Behzadan, Amir

PB - American Society of Civil Engineers (ASCE)

ER -

Mahmood B, Han S. 3D Registration of Indoor Point Clouds for Augmented Reality. In Wang C, Cho YK, Leite F, Behzadan A, editors, Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019. American Society of Civil Engineers (ASCE). 2019. p. 1-8. (Computing in Civil Engineering 2019: Visualization, Information Modeling, and Simulation - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019). https://doi.org/10.1061/9780784482421.001