TY - GEN
T1 - 3D Map Reconstruction from Single Satellite Image Using a Deep Monocular Depth Network
AU - Son, Changmin
AU - Park, Soon Yong
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - In this paper, we propose a 3D reconstruction scheme from single image with deep monocular depth estimation network, BTS (From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation) [1]. Furthermore, we expand it to height estimation focused building from remote sensing images. To address these issues, we substitute depth estimation loss function with height estimation loss function. Moreover, considering improving the quality of the building height map and looking as similar as possible to the ground-truth view, we apply building adaptive loss function.
AB - In this paper, we propose a 3D reconstruction scheme from single image with deep monocular depth estimation network, BTS (From Big to Small: Multi-Scale Local Planar Guidance for Monocular Depth Estimation) [1]. Furthermore, we expand it to height estimation focused building from remote sensing images. To address these issues, we substitute depth estimation loss function with height estimation loss function. Moreover, considering improving the quality of the building height map and looking as similar as possible to the ground-truth view, we apply building adaptive loss function.
KW - 3D map
KW - Deep learning
KW - Monocular height estimation
KW - Remote sensing image
UR - http://www.scopus.com/inward/record.url?scp=85135237786&partnerID=8YFLogxK
U2 - 10.1109/ICUFN55119.2022.9829688
DO - 10.1109/ICUFN55119.2022.9829688
M3 - Conference contribution
AN - SCOPUS:85135237786
T3 - International Conference on Ubiquitous and Future Networks, ICUFN
SP - 5
EP - 7
BT - ICUFN 2022 - 13th International Conference on Ubiquitous and Future Networks
PB - IEEE Computer Society
T2 - 13th International Conference on Ubiquitous and Future Networks, ICUFN 2022
Y2 - 5 July 2022 through 8 July 2022
ER -