@article{Maltezos2018,

title = {Plane detection of polyhedral cultural heritage monuments: The case of tower of winds in Athens},

author = {Evangelos Maltezos and Charalabos Ioannidis},

url = {https://linkinghub.elsevier.com/retrieve/pii/S2352409X17305126},

doi = {10.1016/j.jasrep.2018.03.036},

issn = {2352409X},

year  = {2018},

date = {2018-06-01},

journal = {J. Archaeol. Sci. Reports},

volume = {19},

pages = {562--574},

publisher = {Elsevier Ltd},

abstract = {This study introduces an efficient and easy to implement plane detection approach towards the extraction of high-level information from 3D point clouds associated with polyhedral cultural heritage monuments. An adapted version of the randomized Hough transform (RHT) called “adaptive point randomized Hough transform” (APRHT) and a multiscale framework in terms of Level of Detail 1 (LoD 1) and LoD 2 are proposed. A dense image matching point cloud of an octagonal tower called Tower of Winds, which is situated on the northern foot of the Acropolis hill in Athens was used. A pre-process is carried out to extract points associated with the vertical structural elements. Then a plane detection process is performed in terms of LoD 1 to calculate the plane parameters ($theta$, $phi$ and $rho$) of each of the eight planar surfaces using a coarse form of the entire monument, that is, a sparse point cloud extracted via subsampling process. A mask of one representative detected planar surface is used to clip the initial point cloud with the initial point density. Then, a second plane detection process in terms of LoD 2 at the clipped point cloud is implemented to calculate the corresponding accurate plane parameters. The results are useful for cultural heritage preservation purposes and illustrate the robustness, efficiency and the rapidity of the proposed framework.},

keywords = {Dense image matching, Plane detection, Point Cloud, Randomized Hough transform},

pubstate = {published},

tppubtype = {article}

}