Practicing the geometric designation of sensor networks using the Crowdsource 3D models of cultural heritage objects

• Published in: Journal of cultural heritage Vol. 31; pp. 202 - 207
• Main Author: Alsadik, Bashar
• Format: Journal Article
• Language: English
• Published: Elsevier Masson SAS 01.05.2018
• Subjects: 3D model; Blender; Camera network; Point cloud; Simulation; Terrestrial laser scanner; 3D model; Point cloud; Camera network; Simulation; Terrestrial laser scanner; Blender
• ISSN: 1296-2074; 1778-3674
• DOI: 10.1016/j.culher.2017.11.001

Nowadays, there is a huge research work and literature about the use of laser scanning, computer vision and photogrammetry to turn cultural heritage objects into 3D digital models. These developed 3D models are used for cultural heritage documentation, conservation, restoration and virtual reality applications. Many 3D digital models of cultural heritage objects like buildings, statues and historical places are shared through the internet and can be downloaded for free use. These models either developed using graphical based software tools or through image based methods and active sensors like laser scanners. Normally, image based and laser scanned reality models are developed through capturing around or/and inside the cultural heritage site by professionals through specific guidelines and plans followed later by processing using a state of the art software. However, practically there is a significant challenge to ensure a complete coverage of the object during the data acquisition and to fulfill the required geometric conditions to meet the needed precision and complete coverage. Therefore, in this paper, we propose a procedure to help non-professionals and students to practice and improve their skills in planning either a camera network or a laser scanner network to finally create a valid 3D model of a cultural heritage site. Two experimental tests are applied to a Crowdsource 3D model of the Haydarkhana mosque in Iraq and the model of the Georgia State Capitol in USA. The scenarios for the sensor network planning is applied and a final 3D point cloud is re-created and lessons learned.