5 Ways to Automate Point Cloud Processing: The Latest Technologies Beginners Can Try with Confidence

What is point cloud processing?

A point cloud is data that represents an object or terrain as a collection of countless points. Each point contains three-dimensional position information (X, Y, Z coordinates), allowing the shape of the subject to be recorded in detail almost like a photograph. For example, when a building or terrain is measured with a laser scanner or photogrammetry, millions of points covering the surface are obtained. This is point cloud data. Because point clouds contain three-dimensional information that cannot be represented by traditional 2D drawings, they are used for site records, surveying, and verification of as-built conditions, among other applications.

Point cloud processing refers to the series of tasks that use point cloud data to obtain or utilize necessary information. Specifically, there are various processes ranging from registration—aligning multiple acquired point clouds into a single coordinate system—to noise removal and organization, such as deleting unnecessary points and compressing data, and further to analysis and utilization, such as measuring dimensions and volumes or creating drawings from point clouds. By performing point cloud processing, practical outcomes can be achieved from on-site 3D data, such as creating survey maps or models and checking construction quality by comparing with design data.

Why people get stuck with processing

Point cloud processing is convenient, but beginners often stumble at certain points. Here are the main challenges typically mentioned with conventional point cloud processing.

• Seems to require specialized knowledge: The term “point cloud” itself can sound difficult, and people tend to think that advanced surveying knowledge or CAD software experience is required to handle it. Traditionally, skills to operate dedicated point cloud processing software were demanded, posing a high hurdle for beginners.

• Worry about PC performance: Point cloud data often consist of millions of points, so file sizes are large and place a heavy load on computers. Many people worry, “Won’t I need a high-performance workstation?” and become anxious about whether they can handle it on their current PC.

• Processing takes time and effort: There are many steps from acquiring point clouds to obtaining results. Traditionally, registration required manual time, and unnecessary points had to be deleted one by one. It could take days to analyze on-site data and produce drawings, and busy users might feel they simply don’t have that time.

For these reasons, many beginners shy away thinking, “Point cloud processing looks difficult and arduous…” However, recently, automation technologies have emerged that solve these issues and significantly lower the barrier to point cloud processing.

What does automating point cloud processing mean?

So what exactly does automating point cloud processing refer to? Simply put, it means that software and devices perform the various steps of point cloud processing—steps that previously required human labor or specialized skills—automatically. The general flow of point cloud data utilization includes the following steps.

• Point cloud acquisition: Collect point cloud data on site using a laser scanner or camera.

• Point cloud integration and registration: Align point clouds obtained from multiple measurement positions/devices into a single coordinate system (map position).

• Data organization and analysis: Remove unnecessary noise, extract only the required parts, and calculate dimensions and volumes.

• Utilization of results: Create drawings from point clouds, compare with design data, or output reports.

Automation technologies reduce manual work across this flow and are designed to be easy for beginners to use. For example, point clouds can now be acquired automatically with a smartphone without special measurement devices, and cloud services can perform analysis automatically without difficult operations on a PC. The next chapter introduces five ways beginners can easily try automating point cloud processing and explains what can be automated and the benefits of each.

Five automation methods (for beginners)

Here are five cutting-edge technologies that automate difficult specialized tasks. The explanations focus on points that make beginners feel, “I can do this!”

• Automatic point cloud acquisition with smartphone scanning (LiDAR or photogrammetry) Even without expensive dedicated 3D scanners, recent smartphones allow easy point cloud acquisition. Some phones (for example, the latest iPhones or iPad Pro models) are equipped with small LiDAR sensors, and you can scan your surroundings in 3D just by pointing the camera and walking around. For phones without LiDAR, photogrammetry apps can automatically generate point clouds from multiple photos. The app handles the complex settings, so the user only needs to move the smartphone and take pictures. With automatic point cloud acquisition, beginners can try creating point clouds with their own phones without worrying about how to use measurement equipment. This eliminates the need to purchase costly devices or learn complicated operations, making point cloud technology much more accessible.

• Absolute-coordinate point clouds that remove the need for registration through GPS integration Aligning point cloud data to actual coordinates (map positions) is one of the more difficult steps for beginners. This is where GPS integration becomes useful. If you record the shooting position coordinates at the time of acquisition using a high-precision GPS (GNSS) receiver, the collected point clouds will have accurate absolute coordinates from the start. This eliminates the need to manually register and merge separate point clouds later. For example, using a high-precision GPS device that attaches to a smartphone allows point clouds collected while walking to be saved immediately with geographic coordinates. When registration is automated, data from multiple areas can be managed in a unified coordinate system, making it easy for beginners to combine data or overlay it on maps. Furthermore, if acquired point clouds are already aligned to public coordinate systems (survey coordinate systems), they can be easily overlaid with design drawings and map data, greatly speeding up comparisons between point cloud and design information.

• Automatic noise removal and point cloud organization within apps Point cloud data can include unwanted points called “noise” due to reflections or moving people and vehicles during measurement. Previously, these had to be removed manually one by one or filtered by specifying ranges. Now, apps increasingly offer features that automatically detect and remove noise. For example, software can automatically thin out obviously floating isolated points or classify and separate ground points from building points. There are also functions to automatically compress or downsample (sample) large point clouds to reduce data size, so beginners can get point clouds organized into manageable sizes and states without any effort. Moreover, these processes are often executed by the software immediately after measurement, so the user only needs to wait to receive a clean and lightweight point cloud. Automatic filtering removes concerns like “the data is too large to handle” or “it’s full of junk so I don’t know where to start.”

• Automatic cross-section and volume calculation and result output in the cloud Analysis work to extract desired information from point cloud data has also become automated. Modern cloud services can automatically generate cross-sections and perform volume calculations simply by uploading point cloud data. For example, when earthwork volume calculations are needed, cloud services can instantly compute cut-and-fill volumes from elevation differences relative to a reference surface. If you specify an arbitrary line, the service can automatically draw longitudinal and cross sections along that line and output them as PDFs or images at the press of a button. You don’t need to perform heavy point cloud processing on your own PC, and results can be viewed or downloaded online, so you don’t need specialized software on hand. Point cloud data can also be exported in common file formats when necessary, allowing import into CAD software or other tools. Because results are stored in the cloud, you can check outputs from the field as long as you have internet access, and it’s easy to share data with colleagues for joint review. By using cloud-generated reports and drawings, beginners can smoothly produce deliverables and apply them in their work.

• Visualize and compare with design on-site using AR Acquired point cloud data can be used not only for analysis but also for on-site visualization using AR (augmented reality). For example, by viewing the site through a smartphone or tablet screen and overlaying design drawings or 3D models on that view, you can visually confirm the difference between the current condition and the design plan on the spot. If point clouds were acquired with absolute coordinates, the design data will be projected in the correct positions in AR, allowing highly accurate checks while walking around. For beginners, this is more intuitive than examining numbers or drawings: it becomes immediately clear, “This is where the real object matches (or differs from) the design.” Also, if underground structures (buried objects) are captured in the point cloud and displayed in AR, you can “visualize the invisible” and work more safely in future construction. In addition, because everyone can view the screen together and intuitively grasp the gap between reality and the ideal, AR is also effective as a communication tool on site for explaining things to clients or supervisors. With on-site visualization automated via AR, the range of point cloud applications has expanded from mere data analysis to supporting on-site consensus building and decision-making.

Common concerns and how to address them

Here are typical concerns beginners have when trying point cloud processing for the first time, and how to resolve them.

• Is the accuracy okay? A primary concern is whether measurements taken by simple methods are truly accurate enough. The short answer is that the latest automated point cloud technologies can ensure sufficiently high accuracy. Smartphone LiDAR also offers cm level accuracy (half-inch accuracy), and GPS integration (RTK-capable) can further improve positional accuracy to centimeter-level accuracy (cm level accuracy (half-inch accuracy)). Simple point cloud measurement methods that meet accuracy standards for as-built management specified by the Ministry of Land, Infrastructure, Transport and Tourism have already appeared, so concerns about accuracy have diminished significantly. In important measurements, known control points (reference survey points) can be placed at key locations and used for software-based fine adjustment and verification. Such mechanisms make it possible to achieve practically adequate accuracy in field measurements.

• Seems difficult to operate While point cloud processing might evoke images of arcane specialized software, beginner-oriented tools pay attention to usability. With smartphone apps, scanning can be completed by following on-screen guidance, and cloud services often offer simple operations with Japanese menus and clickable buttons. Advanced settings are automatically optimized, so users can focus on basic operations like starting capture or analysis. If questions arise, plentiful tutorial videos and support make it easy for even those not confident with machines to start using these tools.

• Is data organization troublesome? Managing and storing large volumes of point cloud data is a concern, but automation addresses this as well. Cloud services automatically save captured point cloud data to online storage, removing the hassle of file transfer and backups. Features that organize data by project are available, automatically sorting by capture date or location, making management simple. You can download only the necessary part when needed or view and share point clouds directly in a browser. An environment where you can handle data without worrying about your PC’s disk capacity or complex file organization is therefore established.

• Worried about how to use the acquired results You might wonder, “I captured point clouds, but how should I use the data?” Thanks to automated analysis tools, utilizing point cloud data is easier than expected. For example, plan views and cross-sections required as surveying deliverables can be automatically created in the cloud, and lengths or areas you want to measure can be calculated with a click inside the software. With a point cloud viewer, you can overlay the as-built 3D model to check construction progress or perform on-site confirmation via AR—applications that directly support on-site decision-making. The system is set up so that data can be put to immediate practical use, not just collected and left unused.

Conclusion

Point cloud processing may sound difficult at first, but by taking advantage of the automated mechanisms introduced here, you can handle it smoothly even without specialized knowledge. In particular, smartphone-based measurement and cloud-based analysis have greatly lowered the barrier for beginners. If you previously thought, “Point clouds seem too hard…,” the latest technologies may have you thinking, “I can do this!”

Moreover, all-in-one products have appeared that enable high-precision positioning with small devices attached to smartphones, allowing a single person to handle point cloud acquisition, analysis, and AR-based utilization. For example, using a system like LRTK, which combines a smartphone-mounted high-precision GPS device with a dedicated app, you can acquire point clouds with centimeter-level accuracy (cm level accuracy (half-inch accuracy)) simply by walking around a site. After acquisition, noise removal and volume calculations can be performed automatically in the cloud, and the resulting 3D point cloud can be overlaid with design data in AR. This enables surveying, as-built verification, and reporting to be completed with remarkably simple operations. The era in which beginners can immediately introduce these workflows to on-site operations while still ensuring accuracy has already begun.

Point cloud processing automation technologies continue to evolve daily. Take this opportunity to try new methods of point cloud utilization—you’ll likely be surprised at how easy it is and come to rely on their convenience. Don’t hesitate because you’re a beginner; leverage the power of the latest technologies and take a step forward in on-site DX (digital transformation).