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Drones at Work: Mapping Rio de Janeiro’s Famed Christ the Redeemer Statue

It’s easy to poke fun at that various mishaps drones are having in their inaugural package delivery missions, or to write them off as a techie play-toy. But drones are proving themselves to be capable assistants that can get places humans cannot when it comes to inspections of buildings and structures.

I stumbled across an interesting application on the Dodge Data & Analytics LinkedIn page, where Pix4D and some other business partners used drones to reconstruct Rio de Janeiro’s Cristo Redeemer in high-definition 3D.   Here’s  some of what the Pix4D had to say about the project:

“What can be achieved with drones and image processing technology when traditional scanning methods cannot be applied? Is it possible to reconstruct both an object and its surroundings (for example, the Christ the Redeemer statue of Rio de Janeiro and Corcovado peak) and combine everything into one model? The answer is yes: We can create accurate 3D reconstructions that have never been done before…until now.

 “Pix4D, in collaboration with PUC University of Rio de Janeiro and Canadian UAV manufacturer Aeryon labs, took on the challenge of creating the first ever, accurate high-resolution, 3D reconstruction of the Christ the Redeemer statue in Brazil. Pix4D supplied both the image processing software for the reconstruction and knowledge on how to best acquire the necessary data, Aeryon labs supplied a highly reliable UAV with enduring quad rotor aerial platform, and the university organized project logistics (which included gaining special permission to fly the UAV near a heritage site).

 Despite strong wind, fickle weather conditions, restricted hours for data acquisition, and inconsistent lighting, the project team acquired over 3,500 images. 2,090 of those images, 82 manual tie points and a linear measurement were used in Pix4Dmapper Pro to create an impressive 3D model of Christ the Redeemer and the surrounding area in high resolution. Results are in the format of a point cloud with 134.4 million points and a full textured 3D mesh with 2.5 million triangles.

If you want to learn more about the project, check out the Pix4D white paper on the project.


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