Automated ModelingSmart, Fast, Flexible

A 3.148Mb PDF of this article as it appeared in the magazine complete with images is available by clicking HERE

In 2000, a 3D laser scanner could collect roughly 1,000 points per second. Today, scanners can capture more than one million points per second with up to 1mm precision. This thousand-fold increase in measuring speed is a technological advancement that has created opportunities–and challenges–for geospatial professionals. It’s also a driving force behind some important advances in software technology.

Along with the massive data sets produced by today’s scanners, the geospatial industry is experiencing rising expectations on how the data is used. With projects ranging from forensics and architectural preservation to mining, construction and a plethora of industrial applications, scanning service providers need to produce actionable information quickly and with high accuracy.

Certainly, 3D scanning has delivered large gains in field productivity. But according to many geospatial professionals, the gains in the field are partially offset in the office, where processing scan data can be time-consuming.

For example, technicians often must first combine (or "register") multiple scans into a single point cloud. To do this they identify targets or common points in two or more scans as the basis for merging the scans. The resulting point cloud harbors valuable data but often requires further processing to produce information needed by downstream users. Work to create the deliverables is often performed manually by a technician using a desktop computer. It’s a painstaking job that can be slow and tedious. To streamline the process, software developers are working to automate key tasks.

Automated registration is a core piece in efforts to increase productivity in processing lidar and imagery. For example, automated registration in Trimble RealWorks software can automatically identify scanning targets and common features between multiple scans and use them in the registration computations. As a result, the need for scanning targets is reduced or eliminated, saving time in field and office. Freed from the high point density required to accurately capture targets, field crews can adjust scan densities to optimize scanning times.

Automated registration also reduces operator interaction, allowing office technicians to perform other tasks while the software handles the data processing. With point cloud registration complete, automated routines can also assume additional tasks formerly relegated to humans.

The next step typically involves locating outliers, ghosts and superfluous points as well as points that make up objects of interest. For example, a technician can manually select a group of points that define an object, classify it and assign it to a layer. On a large or complex scanning project, the process may be repeated hundreds of times.

Thanks to advanced computing software, automated classification is becoming the new standard. Features such as ground surfaces, vegetation, poles and building features can be automatically classified and layered with little human interaction. Likewise, software can isolate noise and unneeded points. No data is deleted in the process; the technique hides the points without removing them from the dataset–an important consideration in forensics and other applications where data integrity is vital.

Software manufacturers have enhanced the use of point clouds directly within their software, making it easier to use the data natively for as-built or clash inspection applications. However, for other applications the key task is to convert point cloud information into a 3D CAD model. In addition to classifying planes and ground surfaces, automated modeling software can create 3D entities for use in CAD and design applications. For example, office technicians can use Trimble EdgeWise software to extract features such as pipes and structural components and create 3D solid vector models based on libraries of standard component geometries and dimensions.

Tools for automated modeling are extending beyond traditional point cloud software. Technicians can use the Scan Explorer Extension for SketchUp software to access Trimble RealWorks point clouds. They can extract edges, walls, corners and points, place them into SketchUp and then create 3D models that relate precisely to the scanned objects.

The benefits of automated modeling and identification are significant. In scanning an industrial facility with thousands of pipes of different sizes and orientations, automatic modeling can save hundreds of operator hours. Once the piping is modeled, technicians can draw from libraries of pre-modeled equipment (such as pumps, valves and fittings) to create a comprehensive, detailed 3D model for design, building information modeling (BIM), construction and asset management.

Automated software can provide specialized functionality. For example, Trimble RealWorks can automatically register and process multiple scans of petroleum storage tanks. Engineers use the results to determine tank capacity and condition. Visualization tools help reveal bulges or irregularities that might affect capacity or safety. When performed manually, the work to produce an inspection report could take up to a week. By using 3D scanning and automated modeling methods, the job can often be completed in one day.

Software automation extends to imagery as well, where high dynamic range (HDR) photographs can supplement point clouds. Automated software can match photos with scanned data to produce colorized point clouds and highly realistic 3D models. And automated classification of aerial imagery can extract information on structures, vegetation and surfaces.

Like most computerized systems, the goal of automated modeling is to reduce the time humans spend on a task. While human judgment remains a vital part of data processing and analysis, automated tools make it faster and easier to provide high-value services and deliverables. Time previously spent on registration and modeling can be used for analysis and to focus on deliverables and quality control. Classification routines can produce "smarter" point clouds that provide more efficiency and can be especially helpful for additional analysis and modeling once the primary work has been completed.

Geospatial professionals can use scanning and imagery to deliver significant gains in productivity and flexibility for their clients. As software evolves and automated capabilities expand, classification and 3D models will become increasingly common.

Mr. Bisio joined Trimble in 1996 and has held several marketing, sales and general management positions prior to taking over worldwide responsibility for Trimble’s Geospatial Division. He holds a Master of Business Administration from the University of Denver; a Master of Regional Planning from the University of Massachusetts; and an undergraduate degree in Geographic Information Systems & Cartography from Salem State University in Salem, Massachusetts.

A 3.148Mb PDF of this article as it appeared in the magazine complete with images is available by clicking HERE