TY - GEN
T1 - 3D Modeling Beneath Ground
T2 - 22nd IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022
AU - Lu, Yawen
AU - Lu, Guoyu
N1 - Publisher Copyright: © 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - 3D object reconstruction based on deep neural networks has been gaining attention in recent years. However, recovering 3D shapes of hidden and buried objects remains to be a challenge. Ground Penetrating Radar (GPR) is among the most powerful and widely used instruments for detecting and locating underground objects such as plant roots and pipes, with affordable prices and continually evolving technology. This paper first proposes a deep convolution neural network-based anchor-free GPR curve signal detection net- work utilizing B-scans from a GPR sensor. The detection results can help obtain precisely fitted parabola curves. Furthermore, a graph neural network-based root shape reconstruction network is designated in order to progressively recover major taproot and then fine root branches' geometry. Our results on the gprMax simulated root data as well as the real-world GPR data collected from apple orchards demonstrate the potential of using the proposed framework as a new approach for fine-grained underground object shape reconstruction in a non-destructive way.
AB - 3D object reconstruction based on deep neural networks has been gaining attention in recent years. However, recovering 3D shapes of hidden and buried objects remains to be a challenge. Ground Penetrating Radar (GPR) is among the most powerful and widely used instruments for detecting and locating underground objects such as plant roots and pipes, with affordable prices and continually evolving technology. This paper first proposes a deep convolution neural network-based anchor-free GPR curve signal detection net- work utilizing B-scans from a GPR sensor. The detection results can help obtain precisely fitted parabola curves. Furthermore, a graph neural network-based root shape reconstruction network is designated in order to progressively recover major taproot and then fine root branches' geometry. Our results on the gprMax simulated root data as well as the real-world GPR data collected from apple orchards demonstrate the potential of using the proposed framework as a new approach for fine-grained underground object shape reconstruction in a non-destructive way.
KW - 3D Computer Vision
KW - Remote Sensing
KW - Vision for Aerial/Drone/Underwater/Ground Vehicles
UR - https://www.scopus.com/pages/publications/85126148173
U2 - 10.1109/WACV51458.2022.00077
DO - 10.1109/WACV51458.2022.00077
M3 - Conference contribution
T3 - Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022
SP - 697
EP - 706
BT - Proceedings - 2022 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2022
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 4 January 2022 through 8 January 2022
ER -