The latest technology developments clearly show an improvement (resolution and accuracy) of 3D data collection techniques: aerial and close range photogrammetry, airborne or ground-based laser scanning, mobile mapping and GPS surveying.A lot of research has been conducted toward the automation of 3D object reconstruction.There are a variety of approaches with varying resolution, accuracy, turn around time and cost.The major approaches toward 3D data collection and object reconstruction using photogrammetry, laser scanning, mobile mapping, and map-based reconstruction will be discussed.We will compare these technologies from an AEC application view point.
3D data acquisition and object reconstruction is conventionally performed using stereo image pairs. Stereo photogrammetry or photogrammetry based on a block of overlapped images is the primary approach for 3D mapping and object reconstruction. The employment of close-range photogrammetry has also matured to the level where cameras or digital cameras can be used to capture the close-look images of objects, e.g., buildings, and reconstruct them using the very same theory as the aerial photogrammetry.Photogrammetry is a classic and dominant approach for 3D data acquisition.It is proven stable, accurate and operational.With the development of digital imaging technology, photogrammetry and particularly close-range photogrammetry process is being improved in its turn-around time, and cost effectiveness.The current trend is to improve the automation of object recognition and reconstruction from images.
For stereo images, Zlatanova presented a semi-automatic method for acquiring 3D topologically structured data from aerial stereo images.The process involves the manual digitizing of a minimum number of points necessary for automatically reconstructing the objects of interest.Validation of each reconstructed object is done by superimposition of its wire frame graphics in the stereo model.The 3D topologically structured data are stored in a database and are also used for visualization of the objects.[3D Object Reconstruction from Aerial Stereo Images.]
With multiple images, Zeng presents an approach to surface reconstruction from multiple images.The central idea is to explore the integration of both 3D stereo data and 2D calibrated images.This is motivated by the fact that only robust and accurate feature points that survived the geometry scrutiny of multiple images are reconstructed in space.The density insufficiency and the inevitable holes in the stereo data should be filled in by using information from multiple images.The idea is therefore to first construct small surface patches from stereo points, then to progressively propagate only reliable patches in their neighborhood from images into the whole surface using a best-first strategy. The problem reduces to searching for an optimal local surface patch going through a given set of stereo points from images.[Surface Reconstruction by Propagating 3D Stereo Data in Multiple 2D Images.]
Some new measurement techniques are designed to obtain measurements of and between objects from single images by using the projection, or the shadow as well as their combination. This technology is gaining attention given its fast processing time, and far lower cost than stereo measurements.SilverEye technology developed by GeoTango, Toronto, is the first of this kind commercial product that can produce very realistic city models and buildings from single satellite and aerial images.
Point Clouds-based Approach
LIDAR or laser scanning technology offers a fast and automatic way to collect height or distance information.With the inception of this technology, use of LIDAR or laser for height measurement of buildings becomes very promising.The commercial applications of both airborne LIDAR and ground laser scanning technology have proven to be fast and accurate methods for building height extraction. The task of building extraction is to determine building locations, ground elevation, orientations, building size, rooftop heights, etc.Most buildings can be described to sufficient details in terms of general polyhedra, i.e., their boundaries can be represented by a set of planar surfaces and straight lines.Further processing such as expressing building footprints as polygons is preferable for storing in GIS databases.
Like most feature extraction tasks, building extraction can be implemented in either semiautomatic or automatic strategies, and data-driven and model-driven techniques are commonly used.Some algorithms process the raw LIDAR point clouds directly or grid-based images converted from LIDAR data; others algorithms use these two data structures at different processing stages.The semi-automatic building extraction approaches often prepare a set of building primitives for typical house types and roof shapes.Human operators place appropriate primitives and combine them to model complex structures.Various automated methods could assist operators in measuring and refining 3D wire frame models. Some models measure building primitives using several (monoscopic view) aerial images and 2-D ground plans.While someone uses stereo measurement of points in aerial images for the generation of roof and wall faces.
In Rottensteiner, a method for semi-automatic building extraction together with a concept for storing building models alongside terrain and other topographic data in a topographical information system is presented.His approach is based on the integration of building parameter estimation into the photogrammetric process applying a hybrid modeling scheme.A building is decomposed into a set of simple primitives that are reconstructed individually and are then combined by Boolean operators.The internal data structure of both the primitives and the compound building models is based on the boundary representation methods.[Semi-automatic extraction of buildings based on hybrid adjustment using 3D surface models and management of building data in a TIS.]
There are approaches to automatic building extraction and reconstruction [Hu, Y., Tao, C.V., 2004.Automatic extraction of prismatic and polyhedral building models using airborne LIDAR data, Photogrammetric Engineering & Remote Sensing, Vol.70, 1 p.(in review)].First a digital surface model (DSM) is generated from LIDAR data and then the objects higher than the ground are automatically detected from the DSM. Based on general knowledge about buildings, geometric characteristics such as size, height and shape information are used to separate buildings from other objects.The extracted building outlines are simplified using an orthogonal algorithm to obtain better cartographic quality.Watershed analysis is conducted to extract the ridgelines of building roofs.The ridgelines as well as slope information are used to classify building types.The buildings are reconstructed using three parametric building models (flat, gabled, hipped).[Automated Building Extraction and Reconstruction from LIDAR Data]
In Rottensteiner, not only LIDAR data, but also multi-spectral images are used for 3D building detection. Meanwhile the first and last pulse data and the normalized difference vegetation index are used in the process.[Building Detection Using LIDAR Data and Multi-spectral Images]
To construct virtual 3D city models, Fruh and Zakhor present an approach to automatically creating textured 3D city models using laser scans and images taken from ground level and a bird's-eye perspective. This approach is to register and merge the detailed facade models with a complementary airborne model.The airborne modeling process provides a half-meter resolution model with a bird's-eye view of the entire area, containing terrain profile and building tops.The ground-based modeling process results in a detailed model of the building facades.Using the DSM obtained from airborne laser scans, they localize the acquisition vehicle and register the ground-based facades to the airborne model by means of Monte Carlo localization (MCL).Finally they merge the two models with different resolutions to obtain a 3D model.[Constructing 3D City Models by Merging Aerial and Ground Views]
Researchers are developing mobile mapping systems - some containing calibrated laser range finders and cameras that can collect ground level details at unprecedented resolution. [Tao, C.V.and N.El-Sheimy (2000), Highway Mobile Mapping, Geo-Informatics, Vol.14.No.10, pp.81-85.2000]
By integrating knowledge from the images and the GIS map, the complexity of the building reconstruction process can be reduced.Generally buildings are localized in the images based on information about the ground planes of the buildings contained in the GIS map.The following two methods are typical for utilizing maps for 3D object reconstruction.
In Surveg and Vosselman, their strategy for 3D reconstruction of buildings combines pairs of stereo images with large-scale GIS maps and domain knowledge as additional information sources.The 2D GIS map contains the outline of footprints of the buildings.The knowledge about the problem domain is represented by a building library containing primitive building models. Although, buildings reveal a high variability in shape, even complex buildings can be generated by combining simple building models with flat, gable or hip roof.[3D Building Reconstruction by Map Based Generation and Evaluation of Hypotheses and 3D Reconstruction of Building Models]
With the laser altimeter, Haala, Brenner and Anders presented height data provided by airborne laser scanning and existing ground plans of buildings are combined in order to enable an automatic data capture by the integration of these different types of information.Afterwards virtual reality city models are generated by texture processing, e.g.by mapping of terrestrial images.Thus the rapid acquisition of 3D urban GIS is feasible.Ground plans of buildings have already been acquired and are represented either in analog form by maps and plans or digitally in a 2D GIS.These ground plans are another very important source of information for 3D building reconstruction.Compared to results of automatic procedures these ground plans are very reliable since they contain aggregated information which has been made explicit by human interpretation.For this reason constraints, which are derived from ground plans, can considerably reduce the search space when looking for a proper reconstruction and thereby reduce costs to attain a solution.An example for existing ground truth data relevant for building reconstruction is the digital cadastral map, which provides information on the distribution of property, including the borders of all agricultural areas and the ground plans of existing buildings.Additionally information on the names of streets and the usage of buildings (e.g.garage, residential building, office block, industrial building, church) is provided in the form of text symbols.At the moment the digital cadastral map is build up as an area covering data base, mainly by digitizing existing maps or plans.[3D Urban GIS from Laser Altimeter and 3D Map Data]
Integration for AEC/CAD
We have entered an era where the acquisition of 3D data is ubiquitous, continuous, and massive.These data come from multiple sources including high resolution imagery from aerial photography and satellites; ground-based close-range imaging; 3D point clouds from airborne laser range-finding systems, such as LIDAR; imagery from synthetic aperture radar; and other sources.To make these data really useful, they should be employed to model the real world, and the model should then be available for interactive exploration and analysis.
However, the modeling aspect is not straightforward since almost all the collected data has holes (due to obstructions or poor acquisition conditions), and no single acquisition mode is likely to produce complete models.The overall modeling problem is then one of fusing multi-source data consistently and accurately.As acquisition modes are automated and models are produced, there will be an exponential explosion in the amount of data available for analysis and exploration.The models will ultimately include buildings and everything associated with the environment, such as trees, shrubs, lampposts, sidewalks, streets, and so on.We must develop data organizations to efficiently handle all these aspects and that scale to cover whole cities with tens of thousands of buildings and an uncountable number of other structures.Since the automated acquisition mechanisms will permit repeated collection over time, both the models and the database should be dynamic.The main mode of exploration for this massive collection will be through interactive visualization. The database must support interactive visualization, both in terms of hierarchical structure and multi-resolution models.[3D Reconstruction and Visualization].Conventionally, many of these post-editing work is done in a CAD or a viz system.This is a natural integration of 3D data acquisition with the AEC/CAD environment.We will see the development of more of such integrated environments in the future to take full advantages of 3D data.
Other installments published
to date in this series:
Introduction: Large-scale 3D data integration - An Introduction to the Challenges for CAD and GIS Integration
Chapter 1: Bridging the Worlds of CAD and GIS
Chapter 2: 3D Data Acquisition and Object Reconstruction for AEC/CAD
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