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Lastly, she can view the spherical images to gain a better understanding of the building’s environment, and then pull the 3D data into Revit and develop her project in context.“The context can be used to develop a coherent design intent,” Gupta says, “as well as adding life and reality to her orthographic, axonometric, and 3D perspective drawings.” Site Intelligence has a lot more on the way. point clouds) are an essential but often unavailable ingredient for the construction of 3D city models.We investigate in this paper to what extent can 3D city models be generated solely from 2D data without elevation measurements.
The company promises to “do for roads what googlebot did for the web” by providing regularly updated, accurate, indexed 3D data for autonomous vehicles, but also architects, city planners, construction companies, and really any “other built environment planners” you can name.
Some of the scenarios resulted in surprisingly good performance (given the circumstances): we have achieved a mean absolute error of 0.8m in the inferred heights, which satisfies the accuracy recommendations of City GML for LOD1 models and the needs of several GIS analyses.
We show that our method can be used in practice to generate 3D city models where there are no elevation data, and to supplement existing datasets with 3D models of newly constructed buildings to facilitate rapid update and maintenance of data.
Gupta offered the example of an architect submitting a proposal for a new building in New York City.
This architect, he says, needs to have a good starting point for understanding the design possibilities for the building, and she also needs to iterate her design quickly and cheaply.