The technology behind the Vercator approach

Terrestrial laser scanning has become a powerful way of capturing measured data about the built environment. However, the process to traditionally register the point clouds and extract useful information is often user-intensive and time-consuming.

Point clouds are created by laser scanners and can be used to create visual models of everything from geographic terrain to the built environment, or any object within them. Vercator software greatly improves the alignment of 3D point cloud data by making it a faster, easier and more robust process. Vercator software lowers the barriers to onsite data capture, speeds up scan alignment and makes downstream analysis more convenient.

Users have reported the following key benefits of operating inside the Vercator Cloud:

  • FAST; Significantly reducing data registration and processing time
  • AFFORDABLE; Flexible Pay-As-You-Go pricing
  • COMPATIBLE; All major industry standard data formats supported
  • TRANSPARENT; Enhancing accuracy with traceable, repeatable reporting
  • EFFICIENT; Eliminating the need for artificial targets during scanning

These features deliver substantial user benefits and deliver a highly accurate, robust and reliable dataset ready for downstream data processing.

Why use point cloud utilities in the cloud

Processing point clouds in the VERCATOR Cloud brings automation at a time where cost efficiency is high on the stakeholder agenda. The Registration utility tool automatically aligns point cloud data created by laser scanners without the need of artificial scan targets. Automating the process of registration will save you time in data processing and provide you with a more reliable and quantifiable registration result.

The power of cloud processing offers vast improvements to the BIM community by delivering faster, reliable, more accurate and affordable point cloud registration. With no need for hard- and software updates, computer performance issues and user concerns are eliminated. Plus, with the prospect of 5G imminent, the convenience of working and sharing from the cloud signals a clear path forward for the laser scanning community.

The registration process

Aligning scan data, or registration as it is commonly known, is a key requirement of the increasingly popular reality capture process used by construction professionals in building projects.

Once site data capture is complete the scan operative is typically tasked with the daunting prospect of aligning the scan files. The alignment of data is well known to be a laborious and often unpredictable process. Users might often find misaligned scans or worse, complete registration failures, meaning that the whole process would need to be carried out again, consuming valuable time and resources where efficiency is critical to meeting project specifications.

The registration process in greater detail

In the approach adopted by Vercator, vectors in overlapping scans are matched and brought into alignment, first to perform rotation alignment, and then translation alignment in the horizontal plane, followed by translation alignment in the vertical plane. In fact, such vectors are calculated and found at every point.

Typically, there are 10’s of millions of natural targets in each scan compared to the 10's of artificial targets or natural targets marked by eye in other approaches, resulting in fewer misalignments. The advantage of the Vercator approach is that the process utilises natural features in the 3D environment as natural targets which are automatically recognised, their location and orientation determined, then represented by feature vectors. A detailed description of the four methods can be seen below.

1. Details of the Method

Consider the natural features and targets to be small flat elemental areas, because many 3D environments being scanned have such features. Each flat element is represented by a vector direction, which is either normal or at right angles to each small flat plane, but its length is normalised to one. We can then draw these 'surface normal vectors' as small arrows starting at each point and pointing away from it. as shown in figure 1.

Figure 1 - Surface Normal Vectors

Figure 1: Surface normal vectors

2. Rotational Alignment

Now imagine that each vector is lifted away from where it is but maintains its direction, it is moved so that its tail lies at the origin of a new space. All surface normal vectors have the same length so their arrow tips will lie on the surface of a sphere, as shown in figure 2. A pattern is created on the surface of the sphere.

For example, a wall will have many surface normal vectors all of which will be represented on the surface of the sphere. These normals will lie roughly at the same place since the surface normals will be generally parallel.

The normals are defined for each overlapping scan, giving the sphere a unique pattern for each scan. If the scans have sufficient overlap the resulting patterns will have many similar features. So, by moving the origin of the sphere for one scan to coincide with the origin of the sphere for an adjacent overlapping scan, we nest two spheres inside one another. Then we only have to rotate one sphere relative to the other until the two patterns match in order to obtain the angles; we need to rotate only one scan to bring it into alignment with the other scan. If the horizontal is known, it is only necessary to perform a rotation about a vertical axis until the two patterns match.

Figure 2 - Vectors Represented on the Surface of a Sphere

Figure 2: Vectors represented on the surface of a sphere

3. Horizontal Translation Alignment

To determine how much horizontal movement the scans require, the scans are projected, collapsed or squashed onto the horizontal plane to form a 2D plan view, figure 3. When the points collapse onto the plane, vertical walls which have millions of points on them will collapse to form a line on the plane. This creates a point density image. These 2D plan view images have already been rotated to have the same angular rotational alignment; all that is necessary now is to slide the image for one scan over that of the adjacent scan, then to calculate the degree of match to find the position of best alignment. The degree of match calculation considers the density of points available.

Figure 3 - Horizontal Alignment on a Horizontal Plain

Figure 3: Horizontal alignment on the horizontal plane

4. Vertical Translation Alignment

To find the vertical shift or translation, the point clouds of the two scans are separately projected, collapsed or squashed onto a vertical rod, figure 4. Flat floors with millions of points on them collapse to very high densities of points on the rod, and similarly with flat ceilings. The point density pattern of one scan is slid over the point density pattern of the other scan to obtain the position of best match, which indicates how far one scan must be moved to bring it into alignment with the other scan. To ensure a flat horizontal floor in one scan aligns to a flat floor in the overlapping scan and not to a flat horizontal ceiling, the surface normal direction is retained in the process of collapse. Since all floor points have surface normal vectors pointing up and all ceiling points have surface normal vectors pointing down, the floor can be distinguished from the ceiling and only matched to points with surface normal vectors pointing in the same direction.

Figure 4 - Vertical Alignment on a Vertical Plane

Figure 4: Vertical alignment on the vertical plane

Results and performance indicators

With its ground-breaking technology advance, VERCATOR Cloud users achieve state-of-the-art measurement accuracy and benefit through significantly improved cost and process efficiencies. This means less time staging a scan, less time extrapolating the data, yet still yielding reliable and accurate results.

Take a large building such as an office block made up of over 170 laser scans. Vercator Desktop software automatically registered the same datasets in 16 hours and only 3 hours on the cloud, a significant reduction in processing time over conventional methods without the need for operator supervision.

Large building (medium density) 172 e57 16 hours 3 hours
Large shopping venue (high density) 130 e57 100 hours 5 hours
Building and surrounding streets, London 45 fls 2 hours 1 hour
Museum 35 e57 1 hour 30 mins
Concrete building core, 1 floor 16 e57 20 mins 10 mins

Data compatibility

Compatibility allows Vercator software to process most data created by static laser scanners. The software is compatible with major industry formats with more being added as it is developed.

INPUT: ptx / pts / e57 / fls / zfs / rdbx
OUTPUT: xyz / ply / pts / e57 / las / laz / rcs

Vercator software is compatible with most major industry formats with more being added as the software is developed.

Sign-up to your FREE trial of Vercator software and start registering point clouds automatically!