Name
Abstract | ||
|---|---|---|
We introduce an algorithm that fuses information from aerial and terrestrial views for the automatic reconstruction of high-resolution building models within built-up areas. Calibrated aerial photography is commercially available for wide areas of coverage and has been shown to be a useful source of information about the location of buildings at the site, their 2D footprint [8,10], and their rooftop shape [1,6,9]. In contrast, terrestrial imagery is usually uncalibrated, not available commercially for most urban areas, and difficult to acquire. These ground-level images do, however, provide close-range, high-resolution views not normally available in aerial data. Our approach uses the pose information typically associated with aerial surveillance imagery to acquire an initial three-dimensional model of the buildings at the site. Uncontrolled, terrestrial imagery is then aligned to the model using a symbolic model matching and pose a refinement technique. Once aligned, ground-level views can be used to enhance the site model in a number of ways. High-resolution façade textures can be mapped onto the model geometry using the recovered pose information and standard texture mapping algorithms. The same algorithms allow explicit segmentation of building facades from terrestrial views as regions of pixels that project to vertical structures in the model. Context sensitive processing can be applied to these façade regions for the symbolic extraction of surface structures such as windows, doors, and pillars. |
Year | DOI | Venue |
|---|---|---|
1999 | 10.1007/3-540-46621-5_1 | Integrated Spatial Databases |
Keywords | Field | DocType |
aerial surveillance imagery,terrestrial view,symbolic model matching,model geometry,initial three-dimensional model,calibrated aerial photography,site model,view alignment,urban environments,terrestrial imagery,aerial data,high-resolution building model,surface structure,aerial photography,three dimensional,texture mapping,high resolution | Computer vision,Texture mapping,Aerial photography,Segmentation,Computer science,Image processing,Aerial image,Pixel,Footprint,Artificial intelligence,Facade | Conference |
Volume | ISSN | ISBN |
1737 | 0302-9743 | 3-540-66931-0 |
Citations | PageRank | References |
0 | 0.34 | 9 |
Authors | ||
1 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christopher O. Jaynes | 1 | 55 | 8.44 |