@misc{10481/82417,
year = {2023},
month = {3},
url = {https://hdl.handle.net/10481/82417},
abstract = {The reconstruction of 3D point clouds from image datasets is a time-consuming task that has been frequently
solved by performing photogrammetric techniques on every data source. This work presents an approach to
efficiently build large and dense point clouds from co-acquired images. In our case study, the sensors coacquire
visible as well as thermal and multispectral imagery. Hence, RGB point clouds are reconstructed
with traditional methods, whereas the rest of the data sources with lower resolution and less identifiable
features are projected into the first one, i.e., the most complete and dense. To this end, the mapping process
is accelerated using the Graphics Processing Unit (GPU) and multi-threading in the CPU (Central Processing
Unit). The accurate colour aggregation in 3D points is guaranteed by taking into account the occlusion of
foreground surfaces. Accordingly, our solution is shown to reconstruct much more dense point clouds than
notable commercial software (286% on average), e.g., Pix4Dmapper and Agisoft Metashape, in much less time
(−70% on average with respect to the best alternative).},
organization = {Spanish Ministry of
Science, Innovation and Universities via a doctoral grant to the first
author (FPU19/00100)},
organization = {Project TED2021-
132120B-I00 funded by MCIN/AEI/10.13039/501100011033/ and
ERDF funds ‘‘A way of doing Europe’’},
publisher = {Elsevier},
keywords = {Multispectral},
keywords = {Thermography},
keywords = {3D point cloud},
keywords = {GPGPU},
keywords = {UAV},
title = {Efficient generation of occlusion-aware multispectral and thermographic point clouds},
doi = {10.1016/j.compag.2023.107712},
author = {López, Alfonso and Ogaya, Carlos J. and Jurado Rodríguez, Juan Manuel and Feito Higueruela, Francisco},
}