3D Laser scanning services & As-Buit Surveys
We rebuild the geometry of elements and infrastructures using 3D laser scanner technology
Generation and use of point clouds
A Point Cloud is a great help to identify the existing elements of a site and can provide very detailed plans with the click of a button, pointing out where the elements are, as well as their sizes and heights, which we can be check in a construction modeling software (Autodesk Revit, Graphisoft ArchiCAD, Navisworks, Allplan, Tekla, Solidworks ...) that we are using.
In a cloud of points any view on the screen can always be referenced. This means that, from a plan view, elevation, section or 3D, there will always be a clear detail about what exists in that particular part of the building.
It also allows us to model proposals against what exists by combining them. which is very useful to find problems and detect conflicts with existing components. This is not only useful for architects and builders, but also for service contractors, since the level of detail that a point cloud can provide is extremely accurate. For example, being able to clearly represent where existing pipelines and pipelines are located could be extremely useful.
What is a Point Cloud?
Technically, the Point Cloud is a database that contains the points in a three-dimensional coordinate system of the element or installation to be studied. However, from the perspective of the typical workflow, the point cloud is a very precise digital record of an object or space, which contains a large number of points covering the surfaces of an object obtained by laser scanner or photogrammetry.
Points in a cloud of points are ALWAYS located on the outer surfaces of visible objects, because these are the points where the scanner's beam of light is reflected from an object.
If the size of individual points is large enough in a certain view or zoom configuration, the point cloud could be perceived as a continuous surface. If the distance between the points is slightly larger, then we can clearly see that this image is made up of individual points, but even so, our brain can take shapes of an object from that image with relative ease.
It is essential to understand that the point cloud is a set of individual, unrelated points with defined position and color. This makes dot clouds quite easy to edit, show and filter.
The usefulness of point clouds originates, because points are easy objects to handle in large quantities. A computer does not have to worry about scale, rotation and relationships with other objects. Only position and color are things that matter for the calculation. This makes dot clouds quite easy to edit, show and filter data.
Why use point clouds?
The detail and precision of the mapping system of the point cloud obtained by means of a 3D laser scanner makes it an extremely useful tool to digitally represent existing conditions. It allows creating a representation of the "real world" of the workplace, so it is no longer necessary to make conjectures (assumptions), make site visits and manual measurements that consume time and effort.
For example, if we are designing a building that connects to an existing one, we will essentially have the existing and accurate representation, but in 3D!
Point clouds support our design by allowing us to design around the actual configuration of the site, which allows us to detail and accurately model existing buildings. This will help us in eliminating tasks, planning costs and elaborating the details of the construction. Likewise, it will provide the precise configurations for the prefabrication and verification of the construction installation.
Use of point clouds in new construction projects
It may sound strange at first, but 3D laser scanning can also play an important role in new construction projects. It is known that many construction projects have to deal with high error costs, for example, because the new elements that are placed already adjust because the predecessor has made an error in the dimensions. In other words, it can happen that the building deviates from the design during construction. This type of problem can be avoided by verifying the dimensions of the elements already built in the meantime. Point clouds offer the possibility of quickly making a divergence detection between the theoretical 3D model (the design) and the current situation at a particular moment in the construction process.
Acquisition of point clouds
The key factor in acquiring data from the point cloud is access / visibility to the scanned surfaces. It is important to remember that the point cloud is obtained through visible access to real objects. Regardless of the acquisition method (laser scanner or photogrammetry) it is impossible to obtain points on surfaces that are not visible from the position from which we collect data. This means that to cover all the objects you have to combine many scan positions.
The density is used to describe the resolution in the collected data set, this usually means the distance from one point to another. The clouds of less dense points are obviously much faster to capture but of less detail.
Most data in the point cloud contains not only the position of a point, but also a description of the visual properties, such as the color of an object or its reflectivity.
Types of point cloud files
The biggest difference between the types of point cloud files is the use of ASCII and binary. The ASCII (American Standard Code for Information Exchange) is based on binary (as are all computer languages) but transmits information using text. The standard ASCII represents each character as a 7-bit binary number. The types of ASCII common point cloud files are XYZ, OBJ (with some proprietary binary exceptions), PTX (Leica) and ASC. Binary systems store data directly in binary code. Binary common point cloud formats include FLS (Faro), PCD and LAS. Several other types of files used regularly are capable of ASCII and binary formats. These include PLY, FBX. E57 stores data in binary and ASCII, gathering many of the benefits of both in a single file type.
Each line of text within an ASCII file represents a laser return record as spatial coordinates (x, y, z). Additional information such as color and intensity may be included in some formats. The main benefit of ASCII files is a degree of universality in accessibility provided by the standardized text abstraction used to transmit the data. ASCII files, for example, can be opened in text editors. This is one reason why ASCII file types are recommended for long-term archiving.
However, the daily utility of this type of access is minimal, and the use of text to transmit data entails costs. The files are larger, contain less metadata and must be read line by line, which decreases reading speeds. Binary file formats are more compact and can carry more information. It is possible to include file signatures, software information and metadata within each coordinate. It also takes less time to process and view binary files because they can be indexed spatially, allowing them to be read in parts instead of sequentially. However, there are major restrictions on how binary files can be accessed.
Georeferencing point clouds is a critical step to accurately position and contextualize the data within a known coordinate system. Here are the common methods used for georeferencing point clouds:
Ground Control Points (GCPs):
If the point cloud data isn't initially georeferenced, GCPs with known real-world coordinates are placed within the point cloud. These could be physical markers or identifiable features.
Survey-grade instruments are used to accurately measure the coordinates of these GCPs.
The point cloud is then aligned and georeferenced based on the known coordinates of these GCPs.
Register with Existing Models or Plans:
If there are existing georeferenced models, plans, or datasets for the same area, the point cloud can be aligned to these references.
Utilize identifiable features in the point cloud that correspond to features in the existing models or plans. Alignment can be done manually or with specialized software.
This method indirectly georeferences the point cloud based on the known georeferenced data.
Registration in a Previously Georeferenced Point Cloud:
If there's another point cloud in the same area that has already been accurately georeferenced, the new point cloud can be registered to it.
Common features or points in both point clouds are identified and used to align the new point cloud with the already georeferenced one.
This cloud-to-cloud registration process allows the new point cloud to inherit the georeferencing from the existing accurately referenced point cloud.
Each of these methods has its own advantages and considerations. The choice of method depends on the availability of existing georeferenced data, accuracy requirements, and the specific project's needs. Georeferencing point clouds is essential for a wide range of applications such as surveying, mapping, construction, infrastructure development, and more, where accurate spatial positioning is crucial.