Volume Calculation Point Cloud Viewer Use Case: Reducing Excavated Soil Measurement from 7 Days to Same Day

Table of Contents

• The relationship between soil volume calculation and point cloud data

• Features and challenges of field-acquired photogrammetry

• The end-to-end flow from point cloud generation to volume calculation and required tools

• Comparison with conventional methods (TS / GNSS alone) (accuracy, personnel, time, reporting)

• Time-series comparison of soil volume changes (visualizing fills, excavations, and design differences)

• Smartphone-based point cloud acquisition using LRTK and its benefits (shooting assistance, positioning, cloud sync)

• Field use examples and integration with daily reports and reports

• FAQ

The relationship between soil volume calculation and point cloud data

In civil engineering sites, calculating soil volumes for excavations and fills (soil volume calculation) is an essential task for as-built management. Traditionally, cross sections were created from surveyed terrain and volumes were calculated using the average end-area method or grid methods. In recent years, however, using point cloud data composed of countless coordinate points has made it possible to perform these volume calculations more efficiently and with higher accuracy than before. Point clouds obtained by 3D laser scanners or photogrammetry can record fine terrain undulations, allowing the surface shape to be reproduced almost as-is. By comparing point cloud data before and after construction, the volumes of fills and excavations can be directly calculated.

The principle of volume calculation using point clouds is simple: compute the volume difference between the surface models before and after construction. For example, in excavation work, overlay the point cloud of the pre-excavation ground surface with the point cloud after excavation to calculate the volume of soil that was between them. Because point clouds can reconstruct the terrain as surfaces from many measured points, there is no need to interpolate between measured points as in conventional methods, enabling accurate quantity calculations that fully reflect terrain undulations. Also, once point cloud data are acquired, you can change the calculation area or reference plane and recompute volumes any number of times, so you can flexibly respond to recalculations or scenario analyses without additional surveying. Thanks to these advantages, soil volume calculation using point cloud data is becoming a foundational technology supporting the digitalization of construction management, offering both accuracy and efficiency.

Of course, ensuring the quality of the point cloud data itself is important for high-accuracy volume calculations. Achieving a dense point cloud with no measurement gaps in the shooting area, correctly aligning coordinates to the reference coordinate system, and properly removing extraneous objects (machinery, trees, etc.) from the point cloud—meeting these conditions enables low-error volume calculations. In field validations, examples have been reported where volumes derived from point clouds differed from traditional manual survey results by about 1%, confirming that with appropriate procedures the reliability of point cloud–based volume calculation is sufficiently high for practical use.

Features and challenges of field-acquired photogrammetry

Now that we know point cloud data are useful for soil volume management, the challenge at sites is how to obtain those point clouds easily. Traditionally, measurements required specialized equipment such as terrestrial laser scanners or survey drones and a survey crew, but recent advances in photogrammetry have enabled site staff themselves to use smartphones or drones to acquire point clouds. “Field-acquired photogrammetry” refers precisely to the method of casually taking photos on site and generating 3D models (point clouds) from those images.

The biggest feature of photogrammetry is the ease of measuring large areas with familiar devices. For example, drone aerial photography can capture photographic data of a vast site from above in a short time, allowing surveying of steep or dangerous slopes without people entering the area. With a smartphone, walking around the object and taking a sufficient number of photos from all directions allows dedicated software to generate a high-density point cloud model. The ability to create point clouds using only a general camera rather than an expensive laser scanner is a major advantage, and photogrammetry is attracting attention as a method to promote on-site DX (digital transformation).

However, photogrammetry-based point cloud acquisition also has several challenges. First, measurement accuracy depends on shooting conditions. If photo resolution or exposure is insufficient, or the subject has strong shadows or reflections, errors or data loss can occur during point cloud generation. If unwanted objects such as vegetation or waste are included in the shooting range, extra work is required to extract ground surface data from the generated point cloud. In addition, there are processes that are difficult to complete on site. Even if shooting itself takes little time, the subsequent image processing (point cloud generation) often requires hours on a high-performance PC or cloud service, causing a time lag between surveying → data processing → volume calculation. With conventional photogrammetry workflows, it has been difficult to calculate volumes in real time on site.

Furthermore, using a drone often requires flight permission applications and a skilled operator, and smartphone-only photogrammetry can suffer from GPS location errors that cause the generated model’s position or scale to deviate from the real coordinate system. Even if you can generate a point cloud from smartphone photos, it is unusable for as-built quantities if the height datum or absolute position is unclear. Therefore, to use photogrammetry on site you typically needed to take measures to ensure accuracy—such as placing ground control points (GCPs) or ensuring sufficient photo overlap. Although field-acquired photogrammetry has high potential, conventional techniques alone still left gaps in realizing the ideal of “anyone can easily complete high-accuracy point cloud measurement on site.”

The end-to-end flow from point cloud generation to volume calculation and required tools

So, what steps and tools are required to actually generate point cloud data and calculate soil volumes? Let’s look at the typical workflow step by step.

• Data acquisition (measurement): First, record the site terrain and soil conditions by shooting the site with a smartphone camera, LiDAR scanner, or a drone-mounted camera. For photogrammetry, it is important to take sufficient photos from every direction of the subject and ensure overlap between photos. With a drone, use an automatic flight program to take photos at regular intervals; with a smartphone, walk around the subject to capture photos without gaps. Devices like the latest iPhone with built-in LiDAR can, in some cases, complete point cloud generation on site in real time.

• Point cloud generation (processing): Next, generate a point cloud model from the photos. When photos are fed into dedicated photogrammetry processing software or a cloud service, feature matching and image analysis reconstruct camera positions and 3D point clouds, outputting point clouds of millions to tens of millions of points. This processing requires computational resources and time, but high-performance PCs and GPUs enable high-accuracy and fast processing. Cloud services that automatically create point clouds when photos are uploaded have recently emerged, enabling workflows where point cloud generation is complete by the time the team returns to the office.

• Georeferencing (alignment): To use the generated point cloud for volume calculation, it must be properly placed into the real surveying coordinate system (georeferenced). This step assigns scale, orientation, and height references to the model to align it with real-world coordinates. For drone photos, pre-installed ground control points (GCPs) are captured in photos so the model can be aligned to known coordinates. For smartphone photos, you can photograph known points and match them in post-processing, or—discussed later—use high-accuracy positioning to acquire point clouds with absolute coordinates from the start. In any case, comparing point clouds from different times or overlaying them with design data requires that all data share the same coordinate base. Proper alignment makes point clouds useful measurement data that can be handled in GIS or CAD.

• Volume calculation: With aligned point clouds, specify the area for volume calculation and compute. Typically, a TIN (triangulated irregular network) terrain model generated from the point cloud is used. Specifically, compare against a reference height to integrate volumes of fills and cuts, or overlay two time-stamped terrain point clouds to compute the volume difference. Civil-focused 3D software or point cloud processing tools perform these calculations; once the procedure is set, the software automatically computes volumes. For example, to know the volume of fill in a particular area, simply specify the polygon, and the software quantifies the fill volume within. Open-source point cloud tools now include volume calculation functions, and web-browser–based point cloud viewer cloud services have appeared that can load point cloud models and display volumes. Even without specialized desktop software, environments to check as-built quantities via a browser are being established.

• Sharing and reporting results: Finally, share calculated volumes with stakeholders and compile reports. Traditionally, calculation results were organized in Excel and cross sections drawn on plans for explanation. Using point clouds enables deeper sharing: attach screen captures of color-coded differential volumes from a 3D point cloud viewer to reports, or send cloud point cloud sharing links so stakeholders can interactively inspect site conditions. Being able to share the entire 3D dataset that underlies results is a unique advantage of point cloud workflows. Tools that support this flow include capture devices (smartphones, drones), photogrammetry conversion software, point cloud processing tools or cloud services, and viewer/share platforms. Whereas workflows previously combined separate tools, integrated solutions that provide all functions together have recently appeared.

Comparison with conventional methods (TS / GNSS alone) (accuracy, personnel, time, reporting)

The approach using point clouds differs substantially from the conventional methods that rely only on total stations (TS) or high-precision GNSS. Let’s compare them in terms of accuracy, required personnel, time required, and reporting.

First, on accuracy: the positional accuracy of single points is better with TS or high-precision GNSS. TS can acquire point coordinates with millimeter accuracy via prism distance measurement, and RTK-GNSS with a base station can achieve horizontal positions within several centimeters (several in) of error. However, in soil volume calculation, “accuracy” is not only about low error at individual points but also about how well the overall terrain shape is captured. TS/GNSS surveys typically sample the terrain on a grid with several-meter (several ft) spacing or set cross-section lines at key locations. Even if individually measured points are precise, detailed undulations between points must be interpolated and can be missed. Point clouds, in contrast, are a dense collection of points that continuously cover the terrain as a surface, capturing the ground everywhere. Thus, small depressions or bumps on the ground that point clouds can capture might be missed by coarse meshes from conventional surveys. The overall volume calculation error is influenced by such undetected features, so under comparable conditions point-cloud–based calculations can achieve accuracy comparable to conventional methods. In fact, there are reports from large embankment as-built verifications where volumes from point clouds and from traditional average end-area methods differed by about 1%, confirming that with proper procedures point clouds can achieve sufficient accuracy.

Next, personnel and time. Conventional methods require many manual steps from surveying to drawing and volume calculation. For example, on a large development site, a survey team of four people might spend an entire week (20–30 person-days total) on terrain surveying, cross-section creation, and volume calculation. Switching to photogrammetry plus point cloud processing, there are cases where two people finished in one day (2 person-days). A drone can shoot from the air in about 15 minutes and the same day’s point cloud generation and volume calculation can be completed. Thus, point cloud use dramatically reduces the personnel and time required for surveying. A single operator can operate a smartphone or drone, allowing other staff to be assigned elsewhere. Given the current shortage of skilled surveyors, digital measurement tools that anyone can operate are valuable for addressing manpower constraints.

Reporting efficiency also differs. Traditionally, survey results were manually compiled into drawings and tables, and preparing documentation to certify as-built quantities took time. Large sites often involved cumbersome internal checks and client-attended confirmations after surveying. Point clouds, as noted, can themselves serve as evidence. Showing stakeholders “this point cloud data” allows intuitive understanding of site conditions and visual explanation of the basis for quantity calculations. Sharing differential heatmaps or point cloud views overlaid with volume values instantly communicates where and how much earth is in surplus or deficit. In report preparation, cross sections and plan views extracted from point clouds can be auto-generated and pasted into reports, reducing staff workload. In short, reports that used to be laboriously prepared can be semi-automated using point clouds. Point cloud use speeds up the entire cycle from surveying to reporting, accelerating on-site decision-making.

Time-series comparison of soil volume changes (visualizing fills, excavations, and design differences)

A strength of soil volume calculation with point clouds is that it enables time-series tracking of terrain changes. Construction sites change daily, but if you regularly acquire point clouds you can quantitatively track the progress of fills and excavations. For example, if you perform weekly drone aerial scans and save point cloud models for a fill project, you can graph weekly increases in fill volume or create color-coded maps showing which areas increased compared to the previous week. Visualizing volume changes over time allows objective progress management based on data. If progress lags, it becomes easier to arrange additional machinery promptly, improving the PDCA cycle for construction management.

From an as-built control perspective, comparing with design data is also important. If you have point clouds and the designed final surface model (design grade), you can check whether the current state meets the design overall. For excavation, you can identify areas that have been excavated to the specified depth and areas where soil remains; for fill, you can check for overfill above specified heights. Difference results can be visualized as a heatmap (color distribution) for intuitive understanding. For example, coloring areas above the design surface red and areas below blue makes it easy to spot where rework is needed. Spatially showing cut/fill surpluses and deficits streamlines issuing corrective instructions on site and helps prevent rework and material waste.

Moreover, accumulating such terrain data is useful not only during construction but also for later maintenance and disaster response. If you store as-built point clouds at completion, you can quantitatively evaluate long-term changes by comparing newly acquired point clouds at inspection. For example, if an embankment or fill structure gradually subsides over years, the subsidence amount can be computed from differences with previous point clouds; after a major slope failure, pre- and post-event point clouds can estimate the collapsed soil volume. Previously, disaster sites needed manual field surveys to calculate displaced soil, but remote measurements can quickly and accurately determine quantities even in dangerous areas, aiding restoration planning.

Smartphone-based point cloud acquisition using LRTK and its benefits (shooting assistance, positioning, cloud sync)

As noted above, field photogrammetry had challenges, but recent technologies have addressed many of them, making it increasingly possible for anyone to easily acquire high-accuracy point clouds. A representative example is LRTK. LRTK is a smartphone-integrated high-precision positioning system provided by Refixia Inc., which uses a small dedicated antenna attached to an iPhone or other smartphone and network RTK corrections to improve smartphone positioning to centimeter-level accuracy. This allows each point in point clouds acquired by the phone’s camera or LiDAR to be assigned precise coordinates, achieving survey-grade accuracy with a smartphone. Previously, high-accuracy 3D surveying required drone + GNSS base station setups or expensive laser scanners, but LRTK can replace these with a single handheld smartphone, which is revolutionary. It does not require specialized equipment operation knowledge; site technicians can use it as an extension of their daily workflow. Compared to other 3D measurement methods, it has low introduction cost and requires no vehicle or power arrangements, offering agility to measure immediately when needed, making it suitable for routine frequent measurements.

Let’s summarize LRTK benefits from the perspectives of “shooting assistance,” “high-precision positioning,” and “cloud synchronization.”

• Shooting assistance: LRTK smartphone apps generate point clouds in real time and display them on the screen during shooting, so operators can preview the point cloud and avoid missing areas. For example, when scanning a slope, if a blind spot is not captured in the point cloud, the operator can easily take additional photos to fill the gap. Multiple scans taken separately are automatically aligned, enabling high-quality point clouds without specialized post-processing. The app can also display shooting guides to navigate optimal routes and has features to retake photos from the same position and angle for fixed-point monitoring, making it easy for anyone on site to measure without mistakes.

• High-precision positioning: LRTK’s main feature is improving smartphone GNSS positioning accuracy. With a network RTK–compatible LRTK antenna, smartphone GPS, which typically has meter-level errors, can achieve accuracy within a few centimeters (within a few in). This means that all acquired point clouds and photos receive global geodetic coordinates and can be used immediately for volume calculations or plan comparisons without GCP corrections. The height accuracy is also high, so data can be used directly for reference plane comparisons and cross-section creation. For example, LiDAR scans from an iPhone Pro combined with LRTK yield point clouds with high-accuracy coordinates from the start, allowing measurements to be used as as-built management deliverables. Eliminating the cumbersome alignment work that plagued traditional photogrammetry greatly simplifies the overall workflow.

• Cloud synchronization: LRTK integrates a field app with cloud services, enabling automatic data sharing and storage. Point clouds and coordinate-tagged photos captured in the app are uploaded to the cloud immediately after shooting, eliminating the need to copy via USB or perform format conversion back at the office. Uploaded point clouds can be displayed in a 3D viewer on the cloud, and browser-based analysis functions such as volume calculation and plotting are available. The workflow “scan on site with smartphone → automatic cloud processing → instantly check volume results” can be realized in one stop, reducing the time lag from measurement to analysis to nearly zero. Accumulating data in the cloud also centralizes terrain changes across the project, making time-series comparison straightforward. Past point clouds can be retrieved and shared with stakeholders for collaborative checking. Instant sharing of site information, which is difficult with paper reports, strongly supports faster and more efficient construction management.

LRTK thus implements an integrated system: “easy smartphone point cloud measurement → immediate high-accuracy volume calculation on site → cloud data sharing.” It solves on-site photogrammetry barriers—shooting skill, positioning accuracy, and data processing environment—in one package and is a solution that contributes to reduced manpower and improved efficiency at construction sites.

Field use examples and integration with daily reports and reports

Finally, let’s look at practical use cases of point cloud soil volume measurement on construction sites and how to leverage them in daily reports and as-built reporting.

• Progress management and daily reports: On a certain development site, the site supervisor scanned soil stockpiles with a smartphone + LRTK every evening and immediately grasped the day’s removed soil volume. These figures were recorded in daily reports and used to decide the next day’s machinery allocation and dump truck scheduling. Previously, daily soil volumes were estimated from the number of dump trucks and load sizes, but recording measured values has improved the reliability of daily reports and facilitated smooth information sharing among contractors. Reviewing accumulated weekly or monthly point clouds provides objective evidence of progress and quantities for internal and external reporting, supporting revision of construction plans and schedule control.

• As-built verification and report creation: When fill work is completed, compare LRTK-acquired point clouds with the design surface to verify as-built quantities (cut/fill surpluses and deficits). Automatically computed cut/fill volumes can be used directly as as-built quantity tables, and cross sections and 3D view images extracted from point clouds can be attached to reports for client explanation. Because point cloud data serve as immutable evidence, clients can immediately confirm quantities without the double work of on-site re-measurements with tapes or instruments. The LRTK cloud provides a one-button report output in a prescribed format (PDF) from acquired data, enabling easy generation of measurement reports with photos, coordinates, and notes. Using this, you can quickly create an original as-built document combining site photos and point cloud data, improving reporting efficiency and quality.

• Safety management and special cases: Point cloud technology is also powerful for measuring dangerous areas that people cannot enter. For slopes at risk of collapse or disaster sites, remote methods like drones or LRTK can accurately determine soil quantities that previously could only be approximated from a distance. There are cases where LRTK was used to estimate debris volumes after heavy rain disasters, aiding rapid restoration planning. For post-construction structure management, repeatedly acquiring point clouds at fixed points records long-term changes like an electronic medical record. Comparing 3D data taken from the same position and angle over time quantitatively and visually shows trends in settlement or deformation, strengthening maintenance reports. These applications go beyond daily construction management, and are possible because on-site 3D scanning has become easy.

By introducing simple surveying with LRTK on site, you can measure immediately when needed and instantly share and report results. This enables as-built management that saves manpower and time while ensuring accuracy and reliability, contributing to increased productivity, cost reduction, and improved safety across the project. Delegating measurement tasks that previously required specialists to in-house staff speeds up construction management substantially. This is precisely the effect of “completing soil volume calculation and point cloud measurement on site.” In future civil works, smartphone-based point cloud measurement and real-time as-built management are becoming standard processes. We encourage you to try smartphone scanning and LRTK-enabled labor-saving soil volume management at your site.

FAQ

Q1. Is the accuracy of soil volume calculation using point cloud data sufficient? A1. Yes. If point cloud data are properly acquired and processed, accurate soil volume calculations are achievable. In general, point clouds derived from photogrammetry or laser scanners, when calibrated with control points and acquired with sufficient density, produce volume results whose errors fall within the same range (within a few percent) as traditional survey calculations. Field comparison tests have reported minor differences (about 1%) between point cloud–derived quantities and those from conventional methods. However, ensuring high accuracy requires no gaps in the point cloud coverage, removal of non-ground points, and correct coordinate alignment. When these conditions are met, point cloud–based soil volume calculation is robust enough for field use.

Q2. Do I need specialized skills to use photogrammetry or LRTK? A2. Traditional photogrammetry required specialized knowledge and experience in some parts, but recent solutions have made operations simpler. Smartphone app–based scans are intuitive: follow on-screen instructions to shoot and point clouds are automatically generated, so no special shooting technique is necessary. LRTK involves attaching an antenna to a smartphone, launching the app, and following the guide to perform positioning, point cloud acquisition, and volume calculation automatically. You don’t need to understand specialist terms or complex settings; the system is designed so site staff can start using it after short training. Data processing and analysis are often automated on the cloud, so users only need to check results. Modern photogrammetry tools and LRTK are designed for ease of use, enabling site-level operation.

Q3. What equipment and environment are required to perform point cloud measurements on site? A3. Basically, a device with a high-quality camera (smartphone, tablet, drone, etc.) and supporting software/services are enough for point cloud measurement. For smartphones, recent iPhone or iPad models with LiDAR are preferable, but photogrammetry can be done with ordinary cameras as well. If using LRTK, you need a compatible smartphone, the LRTK antenna, and a mobile network environment to receive RTK correction information. Drone use requires a GPS-equipped drone and camera plus flight permissions and a qualified pilot. In any case, cloud services or PC-based point cloud software are needed to process acquired data. However, all-in-one services like LRTK can complete capture-to-cloud storage on site with just a smartphone and antenna, eliminating the need for a special PC. Environmental considerations include ensuring clear lines of sight and sufficient shooting positions for large areas. Photogrammetry is affected by weather and lighting, so shooting in fair weather with front lighting improves accuracy. For safety, avoid entering dangerous heights or steep slopes; use drones or extend devices with monopods or poles in unstable ground conditions.

Q4. Should I use smartphone scanning or drone surveying for soil volume measurement? A4. Choose based on site scale and purpose. For large sites or those with many high areas, drone surveying efficiently acquires an overhead point cloud in a short time. For confined sites, interiors, or detailed work, smartphone scanning offers mobility. Smartphones require no takeoff/landing space and can operate in no-fly zones. For frequent routine measurements, smartphones are easier to use regularly. In practice, both are often used together: drones first survey the whole site to create an overall base map, and smartphone + LRTK then supplement and track fine details or change points. Point clouds from both sources can be integrated on a common coordinate system, so you can choose the optimal method based on site conditions, accuracy, and measurement frequency. In any case, both yield point clouds from which soil volumes can be calculated, so use the method that best meets your site’s needs.