What is Point Cloud Data?

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Point cloud data refers to data that represents the shape of an object or terrain using a collection of numerous points in 3D space. Each point contains coordinates (X, Y, Z) that represent its location, and may also include additional information such as color (R, G, B), depending on the context.

For example, when a building or terrain is converted into point cloud data, the countless measurement points on its surface are recreated on a computer, displayed as a three-dimensional collection of points that looks similar to a photograph. Point cloud data can be acquired using tools like laser scanners or photogrammetry, and the key feature of this data is that it allows for the highly accurate digital preservation of the physical space at the time of acquisition.

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An example of visualized point cloud data obtained by surveying an urban intersection with a LiDAR scanner is shown. Buildings, roads, and street trees are represented in 3D space using numerous points. This ability to digitally capture real-world space with high accuracy makes point cloud data increasingly valuable in fields such as civil engineering and construction.

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The reason point cloud data is highly valued is its speed and accuracy. In traditional surveying methods, the shape of objects had to be measured manually, but with point cloud data, large areas can be covered in a short amount of time. Moreover, the amount of information that can be acquired is vastly greater, making it easy to measure required dimensions in software later or create design drawings and 3D models. For this reason, in the civil engineering and construction industries, 3D utilization of point cloud data is being promoted across surveying, design, construction management, and maintenance, especially with initiatives like the Ministry of Land, Infrastructure, Transport and Tourism’s "i-Construction" program. For example, if the as-built condition of a construction site is recorded entirely in point cloud data, accurate 3D models or cross-sectional drawings can be created even without the original drawings after the completion, which aids in quality management and future renovation planning.

Thus, point cloud data is also gaining attention as a foundational technology for achieving the digital twin of a site (a digital model that mirrors the physical space).

*As-built management refers to the process of confirming and recording whether the constructed structures or terrain match the original design after construction.

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Use of Point Cloud Data in the Civil Engineering Industry

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In the civil engineering industry, point cloud data is bringing transformation to traditional operations. Here, we will introduce its applications and specific examples, focusing on four key areas: Surveying, Construction Management, As-Built Management, and Maintenance Management.

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Application in Surveying

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Point cloud data is widely used as a means to conduct detailed surveys of terrain and structures. In traditional ground surveying, surveyors had to measure key points one by one using total stations or GPS surveying equipment. However, with point cloud technology, laser scanners and drones can measure the surface of the ground in a continuous and expansive manner, enabling the acquisition of detailed terrain data in a short amount of time.

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For example, in large-scale land development projects, drones can be used to capture aerial images of the site before construction begins, turning them into point clouds to create a broad terrain model. This allows for efficient calculation of earthworks and design planning. Moreover, point cloud surveying enables safe and accurate site analysis even in steep or hazardous areas where human access is impossible.

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The advantage of point cloud data is that it can create highly accurate 3D models of both large terrains, such as forests or rivers, and localized structures, such as bridges and roads. The image below shows an example of point cloud data obtained by surveying an area around a highway interchange from the air, illustrating how a vast area can be recorded as a dense collection of points.

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Furthermore, recent developments have made smartphone-based point cloud surveying accessible. As described later, by combining the LiDAR sensor and high-precision GPS built into smartphones, even non-experts can easily perform 3D surveying of the site. With these technological innovations, surveying tasks are becoming increasingly efficient and labor-saving.

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Application in Construction Management

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Point cloud data is also playing a key role in construction management. By regularly scanning construction sites and converting them into point clouds, progress tracking and as-built checks can be carried out efficiently. For example, when constructing large concrete structures, after the concrete is poured, point cloud measurements can be taken and compared with the design model's BIM data to quickly verify if the structure's position and shape match the drawings. If any issues are identified, they can be detected and corrected early, preventing rework and ensuring quality control.

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Additionally, by sharing point cloud data on the cloud, remote construction management becomes possible. In one large construction company’s site, point clouds and 360-degree photos obtained using smartphone LiDAR were consolidated in the cloud, and the site was virtually inspected from the head office using VR. As a result, it was reported that site managers could monitor construction progress without physically visiting the site, significantly reducing travel time.

In this way, point cloud data is also useful as an information foundation for "seeing" the site remotely. In the future, real-time point cloud monitoring and AI analysis are expected to further enhance the concept of "construction management without being on-site."

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Application in As-Built Management

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As-built management is the process of verifying whether the completed structures and terrain match the design after the construction is finished. The introduction of point cloud data has significantly streamlined and enhanced this process.

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For example, in road construction, point clouds of the finished roadway surface and embankments are obtained using drone photogrammetry or ground-based laser scanning, and compared with the 3D model of the design. By analyzing the differences in thickness or height on the point cloud, flatness and compliance with specified thickness can be evaluated across the entire area. Unlike traditional methods, where only limited measurement points could be checked, point cloud data allows for a comprehensive assessment of the entire area, improving the accuracy of quality control. Additionally, systems that automatically generate inspection reports from point cloud data have emerged, reducing the time needed to create inspection documents.

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Furthermore, an important benefit of point cloud data is its ability to be stored as a future reference (baseline). The Ministry of Land, Infrastructure, Transport and Tourism (MLIT) recommends using point cloud data to create final plan views and longitudinal sections. In practice, for older bridges, it is common that past drawings have been discarded, so accurate current conditions are captured by 3D scanning and used to create restoration drawings (as-built plans) during regular inspections or repair design. Point cloud-based restoration drawings have high consistency in shape and dimensions, making them reliable foundational data. Thus, point clouds obtained during as-built management not only serve as completion records but also support future maintenance and management.

Application in Maintenance and Management

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The use of point cloud data is also advancing in the field of infrastructure maintenance and management. For public infrastructure such as bridges, tunnels, and dams, regular inspections and repairs are essential to ensure long-term safety. Point cloud data helps in quantitatively tracking structural changes over time and in detecting areas of deterioration early.

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For example, in bridge inspections, scanning the entire bridge with ground-based laser scanners or drone-mounted LiDAR allows for comparison with point clouds from the next inspection, capturing any changes in displacement or deflection. Similarly, for concrete surface crack investigations, attaching high-resolution photo textures to point cloud data and analyzing them ensures that even small cracks are not missed. Recently, research on using AI to automatically detect signs of deterioration from point cloud data has been progressing, and this is expected to contribute to the streamlining and advancement of maintenance and management.

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Point cloud data is also useful in disaster management. After earthquakes or heavy rainfall, quickly surveying damaged infrastructure with drones can provide a rapid assessment of the volume of collapsed slopes and the extent of the damage. Based on the acquired 3D data, recovery methods can be planned, and areas at risk of secondary disasters can be identified, aiding in decision-making. In this way, point cloud data plays a crucial role in both infrastructure maintenance and disaster prevention, and its use is expected to continue expanding in the future.

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Methods of Acquiring Point Cloud Data

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Next, we will explain the main methods for acquiring point cloud data. The three most common techniques are LiDAR scanning, drone surveying, and 3D photogrammetry. Let’s take a look at the features and benefits of each.

Measurement with LiDAR Scanners

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LiDAR (Light Detection and Ranging) scanners are devices that use laser light to measure distances to objects and acquire point cloud data. There are various types, including ground-based tripod-mounted 3D laser scanners, mobile mapping systems mounted on vehicles, and airborne laser survey systems on aircraft. The advantage of LiDAR is its ability to quickly and accurately capture large amounts of data. The latest models can measure hundreds of thousands of points per second, allowing dense point clouds to be obtained in a short amount of time. Additionally, because LiDAR uses laser light, measurements can be taken at night or in dark areas, and the data is stable regardless of sunlight.

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In civil engineering, ground-based laser scanners are often used for measuring tunnel and bridge shapes. For example, scanning the interior of a tunnel with a laser scanner can allow for the detection of distortions in the tunnel cross-section and deformations in the concrete lining. Mobile mapping vehicles equipped with LiDAR are also practical for scanning road tunnels, enabling data collection without road closures.

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Airborne LiDAR is also used in river basin hazard mapping and forest management planning. By measuring the ground surface from the air with a laser, even terrain covered by trees can be accurately modeled (since LiDAR's specific wavelengths can pass through gaps in the trees and reach the ground). LiDAR scanners are widely used for high-quality point cloud acquisition across various fields, but it is important to note that the equipment and operational costs can be high.

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Point Cloud Acquisition via Drone Surveying

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Drone surveying involves using drones (unmanned aerial vehicles) equipped with cameras or LiDAR to conduct surveys from the air. A particularly common method is aerial photogrammetry, where photos taken by the drone are used to generate point cloud data via photogrammetry techniques (SfM). The main advantage of drone surveying is its ability to survey large areas in a short amount of time. A task that would take an entire day to survey on foot can be completed in just a few minutes using a drone. Additionally, because the drone provides a bird’s-eye view, data can be collected evenly across complex terrains or vast construction sites.

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In civil engineering, drone surveying is often used for volume management of embankments or excavations. By capturing point cloud data before and after construction, accurate calculations of soil volumes can be made from the differences between the two datasets. The Geospatial Information Authority of Japan and local governments are also incorporating drones into public surveying, allowing for more efficient creation of topographic maps and as-built verification. Additionally, in disaster response, drones are used for rapid assessment of disaster areas, capturing point cloud data to quickly determine soil volumes or the extent of damage.

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However, drone surveying does have its limitations, such as being subject to weather conditions and flight permission restrictions. Drones cannot fly in strong winds or rain, and depending on the situation, airspace permission and approvals may be required. Nevertheless, recent advancements in drone performance and autonomous flight technology are making it safer and more reliable for data collection. In the future, drone surveying will continue to evolve as one of the main methods for acquiring point cloud data.

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3D Photogrammetry (Photogrammetry)

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3D photogrammetry is a method of reconstructing the 3D shape of an object from multiple overlapping photographs and converting them into point cloud data. A major advantage of this technique is that it doesn’t require specialized 3D scanners; point clouds can be obtained using regular cameras. For example, in building surveys, numerous photos are taken from different angles, and these images are processed by software to reconstruct the point cloud of the building's surface.

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The benefits of photogrammetry are that it provides low-cost point clouds with color information. Since photos are used, each point can be assigned color (RGB values), making it easy to create 3D models that are visually understandable. This technique can be applied to objects of any size, from small mechanical parts to city-scale models. Modern software is highly efficient, automatically matching features in photos and generating precise point clouds.

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However, photogrammetry requires preparation and processing time. To achieve high-precision results, a sufficient number of photos must be taken, capturing the subject from all angles, making the planning of the photography process important. Additionally, data processing (photo alignment and point cloud calculations) can take time. That being said, recent advances in computer performance and the spread of cloud services have reduced processing times, and easy-to-use photogrammetry services have become available.

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In civil engineering, photogrammetry is primarily used in combination with drone aerial photography. However, solutions have emerged where a smartphone camera and a dedicated app can instantly generate point clouds by simply taking photos of structures. In this way, photogrammetry, which creates point clouds from photographs, is becoming an increasingly accessible and powerful method for 3D data acquisition.

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Benefits of Using Point Cloud Data

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Finally, let's summarize the main benefits of using point cloud data. Considering the differences from traditional methods, we will explain how it contributes to improved work efficiency, cost reduction, enhanced accuracy, and increased safety.

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Significant Improvement in Work Efficiency and Cost Reduction

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The biggest advantage of point cloud data is the significant improvement in surveying and measurement efficiency. As mentioned earlier, large areas can be surveyed in a short amount of time, and highly detailed data that would be impossible to gather manually can be collected at once. As a result, personnel and labor costs on-site are reduced, directly leading to cost savings. For example, one company reported a 40% reduction in work time and success in reducing project costs by replacing part of the on-site surveying with point cloud data. In this way, the use of point cloud data can be a tool for business reform, yielding significant results with minimal effort.

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Furthermore, with point cloud data, once the site is scanned, subsequent measurements can be freely taken as needed, reducing the need for additional on-site surveys. For example, when creating drawings after construction, traditionally, if any areas were missed, re-surveying would be required. But with point cloud data, missing measurements can be checked in the office using the available data. The concept of "bringing the site back" with point cloud data helps reduce rework.

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Point cloud data also holds promise as a solution to labor shortages. Even with a decrease in skilled surveyors, point cloud measurement equipment and software can still produce reliable results, helping to compensate for the shortage of future workers. Overall, the use of point cloud data brings significant benefits in work efficiency and labor reduction, leading to cost savings.

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Improved Data Accuracy and Quality Assurance

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Point cloud data contains very high-density information, which enhances the accuracy of deliverables. For example, traditional survey drawings and design plans were created based on a limited number of measurement points, but by using point cloud data, highly accurate drawings and 3D models can be made that fully reflect the shape of the object. In fact, when creating plan views and cross-sections from 3D models generated from point cloud data, higher reliability is achieved compared to traditional methods. This is especially useful during renovation and modification projects, as it allows for design changes and material orders based on accurate current conditions.

Point cloud data also has advantages in quality control. If point cloud measurements are taken during construction, errors in the as-built condition can be detected in detail, reducing variation in construction quality. Additionally, since point cloud data provides detailed records after construction, areas that might have been overlooked during inspections can be checked in the data. In this way, point cloud data contributes to standardizing and improving the quality of construction work.

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Additionally, by combining point cloud data with color image data, it becomes a visually intuitive document. For example, using 3D views or videos of point cloud data as explanatory materials for stakeholders allows them to intuitively understand the site conditions that are often difficult to convey in plan views. This reduces communication loss and improves overall project quality.

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Improved Safety and New Application Possibilities

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The use of point cloud data also contributes to improving worker safety. The need for workers to survey dangerous areas is greatly reduced, as data can be collected remotely. High areas and locations prone to collapse can be addressed with drones or long-range LiDAR, reducing risks. Furthermore, in post-disaster surveys, point cloud data from drones is being used to assess damage without the need for on-site personnel, helping prevent secondary disasters.

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Moreover, point cloud data can be combined with virtual reality (VR) or augmented reality (AR) technologies for new applications. By displaying the acquired point cloud data on VR headsets, project managers can experience the site remotely as if they were walking on-site. Advanced trials have begun where AR technology overlays the design BIM model and point clouds with real-world objects on the construction site to check for alignment. In this way, point cloud data is highly compatible with digital technologies, becoming a key component in achieving remote presence and advanced simulations.

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In summary, the use of point cloud data enables "faster, cheaper, safer, and higher-quality" work execution. While challenges such as handling large data volumes and mastering specialized software exist, the benefits gained far outweigh these challenges.

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The Future of Point Cloud Data

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Point cloud data technology is constantly advancing, and its range of applications is expected to expand even further in the future. Below are some potential future developments:

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First, with the evolution of acquisition technology, point cloud measurement will become increasingly easier and faster. New devices such as GPS-linked drones, vehicle-mounted mobile mapping systems, and smartphones with built-in LiDAR are rapidly emerging. In the future, smaller and more high-performance 3D sensors may become widespread, allowing anyone to conduct 3D scanning on a daily basis. In fact, the latest smartphones already come with built-in LiDAR, and applications using this technology are being developed one after another. With the advancement of technology, the processes of point cloud acquisition and 3D modeling will become even more efficient, leading to cost reductions and improved quality.

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Next, integration with cloud services and AI will advance. Services that store and share large point cloud datasets on the cloud, and analyze them using high-speed computational resources, will become more common. For example, automatic detection of terrain changes or the use of AI to diagnose signs of deterioration in structures are potential applications. Although there are currently software tools for removing or classifying unwanted objects from point clouds, the introduction of deep learning will further improve accuracy and automation. Additionally, when the latest point cloud data can be accessed remotely by all stakeholders through the cloud, collaboration across distant locations will become even smoother.

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Finally, the realization of digital twins. A digital twin refers to the concept of fully recreating real-world facilities or cities in cyberspace, linking them in real-time. Point cloud data is an essential element in creating digital twins, where the most up-to-date point clouds continuously collected by sensors mirror the real world in a virtual space. This allows for the remote verification and manipulation of real-world situations in the virtual environment. In civil engineering and construction, point cloud data is expected to become an important tool for increasing project success rates and creating more efficient and creative project management practices.

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Overall, the future of point cloud data is bright. In the future, workflows might be built around point clouds, and 3D scanning and data utilization could become the norm. While engineers will need to acquire new skills, the value gained from these technologies will be immense, and point cloud data will become an indispensable foundational technology in the digital era for civil engineering and construction.

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Introduction to LRTK (High-Precision Point Cloud Measurement Solution)

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Finally, we introduce LRTK, an exciting solution that allows even beginners to easily acquire high-precision point cloud data. LRTK is a smartphone-based point cloud measurement system provided by Lefixea Inc., consisting of the high-precision GNSS receiver LRTK Phone, a dedicated app, and cloud services.

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The LRTK Phone device attaches to a smartphone. By combining the smartphone’s built-in LiDAR sensor with the LRTK device, users can easily acquire high-precision point cloud data with simple operation.

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The LRTK Phone is a compact device that attaches to the back of a smartphone and enables RTK (Real-Time Kinematic) positioning via satellite. This allows for centimeter-level high-precision positioning on the smartphone and the ability to add absolute coordinates (global coordinates) to the point cloud scanned with the smartphone's built-in LiDAR. Traditionally, LiDAR scans from a smartphone alone were handled in local coordinate systems, making it difficult to align with maps or design coordinates, requiring adjustments for practical use. However, with LRTK, the acquired point cloud directly matches on-site survey coordinates and existing drawings, significantly reducing post-processing time. For example, by setting up reference points, you can instantly obtain point clouds with latitude, longitude, and elevation information just by scanning the terrain or structure. The design is intuitive, making it easy for beginners to operate without needing specialized surveying knowledge.

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With LRTK, point cloud measurement, which traditionally required specialized equipment, becomes incredibly easy. The LRTK app includes many convenient features that can be accessed with just one tap. By simply moving the smartphone, point clouds are displayed in real-time, and users can verify that scanning is being performed correctly without missing any data.

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Once acquired, users can immediately measure the distance between two points or calculate volumes on the smartphone. For example, if an embankment is scanned, the volume can be instantly calculated and displayed on-site. This offers revolutionary time-saving benefits for volume management. Additionally, the photos and point cloud data collected are automatically synchronized to the cloud, allowing for easy detailed analysis on office computers and smooth sharing with stakeholders.

LRTK also excels in accuracy. The LRTK Phone is compatible with Japan’s Quasi-Zenith Satellite System (QZSS) and the "Centimeter-Level Positioning Augmentation Service (CLAS)", which allows for high-precision positioning in remote areas such as mountainous regions or places without mobile signal coverage (when using the optional antenna). This means it can be used anywhere from urban areas to remote locations. Positioning accuracy is typically within a few centimeters, which is comparable to traditional stationary GNSS surveying equipment, but it is achieved with just a smartphone and small device, which is revolutionary.

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Overall, LRTK is an all-in-one point cloud solution that handles everything from coordinate guidance to point cloud measurement and volume calculation with just a smartphone. It has made 3D measurements, which traditionally required expensive equipment and specialized skills, more accessible, and it is strongly supporting the digital transformation (DX) of construction sites. Even beginners can intuitively and accurately acquire 3D data without worrying about complex coordinate transformations or equipment handling. The day when "anyone can perform 3D scanning" on construction sites may be closer than we think.

In summary, we have discussed the basics of point cloud data, its applications, and the introduction of the latest solution. While point cloud technology is still developing, it is already transforming operations. For beginners, we encourage you to start exploring 3D point cloud data from a local perspective and experience its usefulness firsthand. It will surely reveal opportunities for business improvements and new project possibilities.