TY - GEN
T1 - Rapid image-based localization using clustered 3D point cloud models with geo-location data for AEC/FM mobile augmented reality applications
AU - Bae, Hyojoon
AU - Golparvar-Fard, Mani
AU - White, Jules
N1 - Publisher Copyright:
© ASCE 2014.
PY - 2014
Y1 - 2014
N2 - In this paper, we present a new method for supporting onsite construction and facility management tasks by allowing field personnel to automatically have access to the latest project information in the form of Augmented Reality (AR) overlays - visually document onsite issues/progress, and communicate information with other personnel on or off site. Our near real-time and marker-less mobile augmented reality solution builds on top of a new image-based localization method for 3D point clouds that have been reconstructed using a Structure-from-Motion (SfM) pipeline and are clustered based on already available geo-location data. By using images captured from commodity smartphones/tablets, our method computes a precise 6-DOF pose for the camera and delivers relevant project information in the form of AR overlays. Our main contributions lie in efficient clustering of 3D point clouds and rapid computation of camera pose by detecting an appropriate cluster of 3D points. Compared to our previous work for AEC/FM mobile augmented reality applications, the experimental results demonstrate that the proposed clustering approach accelerates image-based localization using 3D point clouds, taking 1-2 seconds for a single localization.
AB - In this paper, we present a new method for supporting onsite construction and facility management tasks by allowing field personnel to automatically have access to the latest project information in the form of Augmented Reality (AR) overlays - visually document onsite issues/progress, and communicate information with other personnel on or off site. Our near real-time and marker-less mobile augmented reality solution builds on top of a new image-based localization method for 3D point clouds that have been reconstructed using a Structure-from-Motion (SfM) pipeline and are clustered based on already available geo-location data. By using images captured from commodity smartphones/tablets, our method computes a precise 6-DOF pose for the camera and delivers relevant project information in the form of AR overlays. Our main contributions lie in efficient clustering of 3D point clouds and rapid computation of camera pose by detecting an appropriate cluster of 3D points. Compared to our previous work for AEC/FM mobile augmented reality applications, the experimental results demonstrate that the proposed clustering approach accelerates image-based localization using 3D point clouds, taking 1-2 seconds for a single localization.
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U2 - 10.1061/9780784413616.105
DO - 10.1061/9780784413616.105
M3 - Conference contribution
AN - SCOPUS:84934324515
T3 - Computing in Civil and Building Engineering - Proceedings of the 2014 International Conference on Computing in Civil and Building Engineering
SP - 841
EP - 849
BT - Computing in Civil and Building Engineering - Proceedings of the 2014 International Conference on Computing in Civil and Building Engineering
A2 - Issa, R. Raymond
A2 - Flood, Ian
PB - American Society of Civil Engineers
T2 - 2014 International Conference on Computing in Civil and Building Engineering
Y2 - 23 June 2014 through 25 June 2014
ER -