From Reality to Data, an Introduction to Reality Capture Technology

From early cartographers to modern videographers, there has always been a push to repackage physical reality into consumable formats.  This could be a map, a photo, a land survey, or a documentary film, among many others.  All are instances of turning the world we live in, into actionable information.

A modern approach

For the modern definition of reality capture, we talk in terms of the digitization of space. There are a few key terms that are important to know before we start discussing the current state of Reality Capture technology:

  • LIDAR stands for Light Detection And Ranging. LIDAR is very similar to Radar in the sense that energy is sent out of a device, then the return of some of that energy is used to measure the distance between the LIDAR scanner and the object(s) being measured. This often involves very expensive, high fidelity equipment that can output massive amounts of point data to be consumed by powerful processors.  In basic terms, the scanner shoots out hundreds to millions of laser points.  These lasers then report the data of distances from the scanner. They can also capture data like color, light intensity, elevation, temperature, and movement. LIDAR scanners generate Point Clouds as a raw data source.
  • Photogrammetry is another way to create point clouds. In its current form, Photogrammetry is an algorithmic process of creating digital geometry by comparing pixels in photographs. Instead of using light reflected off the surface of an object, a set of digital images is used to determine the geometry of the areas in question. Photogrammetry, unlike LIDAR scanning, has the benefit of higher color resolution due to the nature of capturing reflected light off an object instead of a reflected laser beam in LIDAR.
  • Point Clouds are the foundation of 3D surveys. Just like the name sounds, a LIDAR scanner (or extrapolated Photogrammetry data) creates a large dataset describing the different points that make up an object that has been scanned. Visually, a point cloud looks like an outline of an object that has not been “stitched together.” Point clouds are typically captured in 3 ways:
    • Aerial or overhead surveys are used to generate lower fidelity data that is typically used for layouts and mapping.
    • Hand-Held by smaller devices, and typically for smaller areas or objects. Handheld scans are typically used to capture detail on smaller objects.
    • Terrestrial technology (ground-based) is the most common one you will encounter. A ground (usually based on a tripod) mounted scanner is used. This technology has become ubiquitous in the architecture, engineering, and construction industries.
  • Registration refers to the act of taking a point cloud and creating a model from it. Raw point cloud data points are converted into a vector giving a directional dimension to each point. All these vectors are compiled in a coordination system and then multiple scans can be indexed together for a complete representation of a space or object.

Today, you will most likely hear the term “Laser Scanning” used when someone is talking about quickly creating a 3D as-built model of a building. Accuracy is extremely important with buildings, as the model can be later tied to applications such as estimating materials needed where high fidelity measurements are needed. In areas such as landscape modeling, accuracy is less important, but the visual of the model must be appealing to communicate the richness of space to the viewer. These applications are better suited for photogrammetry as accurate capture of color is easier to achieve compared with LIDAR.

On a project, it is common to use multiple types of measurement technology to create all the needed models. For instance, when we measured and modeled 6500 houses for the Government of Argentina, we began with an Aerial Survey to create a large area model. Individual units were then scanned terrestrially with a tripod-mounted LIDAR scanner.

Apple recently introduced LIDAR scanning to its range of phones and some tablets. Photogrammetry scanners in smartphones have been a niche application for several years. For instance, scans could be used to give a semi-accurate measurement of an object in your house. The 2020 introduction of a consumer-level sensor is opening lots of interesting applications up for handheld scanning. This has allowed better accuracy and quicker generation times in scans.

Regardless of the technology chosen to create point clouds, the data can be used in many different ways to provide spatial insights.  The accuracy of this technology lends itself to highly engineered projects that require minimal tolerance and it even has applications in object-detection in self-driving vehicles. More and more software is natively including point-cloud functionality which means your applications will continue to grow.

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