6.4 Aerial-Ground Fusion
Aerial-Ground Fusion combines drone imagery with ground-level scan data to produce a complete model covering both perspectives. The handheld scanner captures ground-level geometry and detail that the drone cannot see. The drone covers building exteriors, rooftops, and large outdoor areas that ground scanning cannot efficiently reach.
What Aerial-Ground Fusion Is and When It Applies
Supported ground devices. LCC Studio Aerial-Ground Fusion works with every XGRIDS scanner as the ground component: L2 Pro, K2, and PortalCam (see Module 7 for PortalCam-specific field procedure and its per-point photo folder upload). The aerial component is always photographic imagery from a separate mapping drone (such as a DJI Matrice with a Zenmuse P1 payload); the handheld scanners never serve as the aerial component in this workflow. For the LixelStudio point cloud pipeline on this page, the documented ground device is the L2 Pro.
Aerial-Ground Fusion is not the same workflow as L2 Pro Drone Mode covered in section 6.3. In Drone Mode, the L2 Pro is physically mounted on the drone and collects LiDAR data from the air. In Aerial-Ground Fusion, the scanner stays in your hands as a ground scanner and a separate mapping drone collects oblique photographic imagery from the air. The 2 datasets are aligned in software after capture.
The output format also differs depending on which software processes the data. LCC Studio produces a 3D Gaussian Splat from the combined dataset. LixelStudio produces a merged point cloud. The choice between them depends on your deliverable, not on the field collection method. Both pipelines require the same field data collected the same way.
LCC Studio Pipeline
Output: 3D Gaussian Splat (3DGS). Photorealistic rendered model viewable in LCC Viewer, suitable for walkthroughs, client delivery, and web publishing.
Ground device: L2 Pro or K2 (PortalCam: see Module 7).
Ground data input: Raw scanning data only. The output of a completed Map Fusion project cannot be used as ground data. Up to 10 segments under a 200-minute total (the Map Fusion segment ceiling); plan each segment at 20 to 30 minutes, sized to the workstation (Section 4).
Aerial data: 100 to 10,000 drone images. JPG/JPEG only. Minimum resolution 1024×768, consistent across all images. One focal length for the whole set, distortion correction disabled, RTK Fixed on every photo.
Fusion method: Single aerial image set uploaded alongside the ground scan.
Processing time: About 20 to 30 minutes of processing per minute of ground capture, plus the aerial imagery. Hardware: 16-core CPU, 64 GB (2 x 32 GB) minimum with 128 GB (2 x 64 GB) for large datasets, RTX 3090 minimum with RTX 4090 recommended.
LixelStudio Pipeline
Output: Georeferenced merged point cloud in E57, LAS/LAZ, or RCP format. Suitable for BIM, survey deliverables, measurement, and coordination with other point cloud datasets.
Ground device: L2 Pro.
Aerial data: Drone imagery organized in a drone/ subfolder alongside the lixel/ project subfolder. XGRIDS Pose tool (extract_preview_poses) must be run on the drone data before LixelStudio import.
Fusion method: Both the drone/ and lixel/ subfolders are imported into LixelStudio as a single project. LixelStudio merges the point clouds using the RTK tracks from both datasets.
Processing time: Varies by data volume. Typically longer than a standard ground-only scan due to the aerial data processing overhead.
LCC Studio Aerial-Ground Fusion accepts raw scanning data only as ground input, and Map Fusion cannot be combined with it. You cannot process a Map Fusion project first and then feed the fused output into an Aerial-Ground Fusion project, and you cannot run Map Fusion as part of an aerial-ground job. The ground data slots accept raw, unprocessed scan segments directly from the device: up to 10 segments under a 200-minute total, the same segment ceiling as Map Fusion. In practice, plan each scan segment at 20 to 30 minutes, sized to the workstation that will process the job (Section 4).
The field collection procedure is largely the same regardless of which software pipeline you use. The difference is what you do with the drone images after collection: one combined image folder for LCC Studio, or the drone/lixel folder structure with the Pose tool for LixelStudio. Plan your folder organization before the flight. Reorganizing a large drone image set after the fact is tedious and error-prone.
L2 Pro Drone Mode LiDAR data cannot be used as the aerial component in aerial-ground fusion. No workflow exists in LixelStudio or LCC Studio that accepts both an aerial L2 Pro drone mode point cloud and a ground L2 Pro point cloud as inputs for a combined project. The aerial-ground fusion pipelines in both applications require photographic drone imagery as the aerial component, not aerial LiDAR data. If your project requires both aerial coverage types, plan for 2 separate drone configurations on the same platform. The first flight uses a mapping camera such as the Zenmuse P1 to collect the photographic imagery that the aerial-ground fusion pipeline requires. That image set is what gets combined with your ground scan data in LCC Studio or LixelStudio to produce the fused model. The second flight swaps the camera payload for the L2 Pro drone mounting bracket and collects an aerial LiDAR scan. That scan processes independently in LixelStudio as its own point cloud in E57, LAS/LAZ, or RCP format, suitable for elevation modeling, roof geometry measurement, terrain surfaces, and coordination with other point cloud datasets. The 2 deliverables serve different purposes and do not combine with each other in processing.
Project Types That Benefit from Aerial-Ground Fusion
- Campus facilities and multi-building complexes where exterior context and interior detail are both required
- Construction sites needing periodic full-site documentation including ground-level progress and aerial site overview
- Heritage documentation of buildings with significant exterior architectural detail that cannot be fully captured from the ground
- Large outdoor areas with connected interior spaces, such as industrial yards leading into warehouses
- Any project where a roof, upper facade, or terrain above the scanner's reach is part of the deliverable
Hard Requirements Before You Start
Aerial-Ground Fusion has non-negotiable prerequisites that apply to both the ground and aerial data collection. A project that does not meet these requirements will fail during processing. There is no workaround in the software for missing RTK data or incorrect coordinate system configuration.
Drone Recommendations
The best aerial platform for Aerial-Ground Fusion is a DJI Matrice with a Zenmuse P1 camera payload. The Zenmuse P1 is a full-frame mapping camera, not a drone itself. It mounts to the Matrice platform and provides significantly higher image quality and geometric accuracy than lighter consumer-grade alternatives.
If your operation runs a Matrice 300 or 350 for L2 Pro Drone Mode, you can swap the L2 Pro mounting bracket for the Zenmuse P1 payload and use the same drone for the aerial photography phase of a fusion project. The L2 Pro drone attachment fits only the Matrice 300 and 350; the Matrice 400 does not carry it. That has no bearing on this workflow, where the aerial component is always the mapping camera, never the L2 Pro, so the Matrice 400 with a Zenmuse P1 is a fully capable aerial platform here.
| Category | Platform and Notes |
|---|---|
| Recommended | DJI Matrice 350 RTK or Matrice 400 with Zenmuse P1 payload. Superior image quality; the Matrice 300 RTK remains fully capable. The Matrice 300 and 350 double as the L2 Pro Drone Mode platform; the Matrice 400 does not take the L2 Pro attachment, which does not matter for this workflow. |
| Acceptable | DJI M3E, DJI M4E. Capable mapping drones for aerial photography only. Cannot carry the L2 Pro and produce lower image quality than the Zenmuse P1. Adequate for lower-fidelity fusion projects. XGRIDS documents its takeoff/landing capture technique on the M4E (Section 6). |
| Not recommended | DJI P4R. No longer recommended for mapping workflows. |
Site Planning and Fusion Point Selection
Before arriving on site, identify the fusion point locations using satellite imagery or aerial photos. These are the physical locations where the drone will take off and land, and where the ground scanner will perform its fusion-specific collection routine. These points are what LCC Studio uses to align the 2 datasets in three-dimensional space.
How many points, and how far apart. Plan no fewer than 4 takeoff and landing points, together covering approaches from 8 directions, with each point allowing shooting from multiple angles. Keep adjacent points no more than 165 ft apart, and on dense sites plan roughly one point per quarter acre. On most mid-size jobs this works out to 4 to 5 points; large or complex sites need more. Distribute them evenly: points clustered at one end of a large site will not provide the geometric constraint needed for the far end of the dataset to align correctly.
Takeoff and landing point distribution. Every point doubles as an aerial-ground fusion point.
What Makes a Good Fusion Point
A fusion point location must be accessible to both the drone (for takeoff and landing) and the scanner (for ground-level scanning). Beyond that, it needs to meet all 4 of these criteria:
- Open area: The drone needs a clear vertical path for takeoff and landing, and both the aerial photos and the ground LiDAR need clear visibility of the point. Overhead obstructions like trees, power lines, or building overhangs will prevent the required vertical image sequence from being captured correctly.
- Rich surface features, with a fixed target 65 to 130 ft out: The fusion algorithm matches geometry visible in both the drone images and the ground scan. A blank pavement square with no features provides almost nothing to match against. A paved area with planters, signage, bollards, or building corners is much more useful. Each point also needs a fixed, stable object 65 to 130 ft away, visible from both the air and the ground, for the takeoff/landing photo sets to aim at.
- Stable and unchanging between visits: If the ground scan happens on Tuesday and the drone flight happens on Thursday, the fusion point area must look the same in both datasets. Parked vehicles, staging materials, or any moveable objects that might not be there both days will degrade the fusion result.
- Accessible to the scanner from multiple angles: The scanner needs to circle the fusion point area to provide 360-degree ground-level coverage that the drone can match against from overhead.
Workload Estimation
Quote and schedule aerial-ground jobs from a workload estimate, not a guess. XGRIDS' estimation logic:
Workload = minimum workload for coverage completeness + additional workload for detailed reconstruction + travel time + contingencies.
In open environments, the coverage minimum is far smaller than the detail workload; the detail passes dominate the day. In complex environments, the two converge: simply covering the site completely already takes as long as the detail work.
Ground Capture Planning: Fit the Capture to the Processing Ceiling
Plan the ground capture backward from what LCC Studio can actually process, not from how long the site takes to walk. The field rule: plan each scan segment at 20 to 30 minutes, sized to the workstation that will process the job. The governing limits behind that rule:
| Limit | Value |
|---|---|
| Practical segment length | 20 to 30 minutes per scan segment, depending on the size of the processing workstation. Smaller machine, shorter segments. |
| 64 GB workstation | Stably processes up to 30 minutes of capture data. Past 45 minutes, reconstruction failure risk rises. |
| 128 GB workstation | Stably processes up to 60 minutes of capture data. Past 90 minutes, failure risk rises. |
| Project ceiling | Up to 10 raw segments under a 200-minute total, the same segment ceiling as Map Fusion. |
| K2 single scene | Maximum single-scene capture duration 90 minutes. |
| Memory check before starting | After importing data, watch the estimated memory bar. Red means capacity is exceeded and the reconstruction is likely to fail. |
In practice: split the ground capture into 20-to-30-minute segments per standard scan-splitting practice (section 6.2) and stay under the project ceiling. A site that genuinely needs more capture than that is more than one aerial-ground project, or a conversation with XGRIDS about their server-class private deployment, not a longer scan.
Aerial Capture Times at 1 cm GSD
Smart oblique capture at the guide's 1 cm GSD spec flies at roughly 85 ft. XGRIDS' published collection times:
| Survey area | M4E | M3E |
|---|---|---|
| 2.5 acres | 28 min | 36 min |
| 12.5 acres | 1 h 55 min | 2 h 22 min |
| 25 acres | 3 h 43 min | 4 h 33 min |
Halving the GSD to 0.5 cm roughly quadruples the flight time (an M4E covers 2.5 acres in 2 h 8 min at 0.5 cm). Coarsening it to 2 cm or beyond cuts the flight time but visibly degrades the reconstruction; XGRIDS' own comparisons show fine structure collapsing at 2 cm. Hold the 1 cm spec and budget the time. Add the takeoff/landing photo sequences on top, roughly 90 to 120 photos at each point (Section 6), and keep the total image set inside the 100 to 10,000 count that LCC Studio accepts.
XGRIDS' large-project capacity figures are server figures, not workstation figures. The 500-minute, 10,000-photo class of aerial-ground job that appears in XGRIDS training material runs on the LCC Studio Linux Server version with multi-GPU parallel processing, a private-deployment product available through XGRIDS sales. The Windows version does not support multi-GPU processing. A Windows workstation job is bounded by the memory table above and the 10-segment, 200-minute ground ceiling. Quote clients from the workstation limits unless a server deployment is actually part of the project.
Ground Collection: L2 Pro and K2 Method
For Aerial-Ground Fusion, the ground scan follows a standard RTK-enabled scan procedure with one specific addition at each fusion point: the scanner must complete 2 loop closures within 6.5 to 10 ft of the takeoff and landing location. This gives the SLAM algorithm dense, well-constrained ground-level geometry precisely at the area the drone will be photographing from directly above.
Verify RTK is Fixed before starting the scan
In the companion app, confirm satellite status is Fixed (not Float, not Single Point) and that you have at least 10 valid satellites. Set the coordinate system to WGS84. The drone must also be set to WGS84. Do not begin scanning until RTK is Fixed. L2 Pro RTK uses the optional add-on module.
Scan the site following standard technique, watching the live cloud
Scan the full project area normally. Maintain RTK Fixed status throughout as much of the scan as possible, particularly in and around the fusion point areas. If RTK signal is lost under cover or inside structures, the fusion point areas must be covered with Fixed RTK.
Monitor the real-time point cloud as you go. Drift, misalignment, layering, or large-scale noise in the live view means that stretch needs a rescan before you leave the site.
At each fusion point, complete 2 loop closures within 6.5 to 10 ft of the location
When your scanning path brings you to each fusion point location, center the scan on the point and make 2 distinct loops around it, each staying within 6.5 to 10 ft of the takeoff/landing spot. Walk the loops at different heights if the area allows. These loops create the dense, geometrically constrained ground data at the exact location the drone will capture from above.
If you arrive at the fusion point from one direction and leave in the same direction without completing the loops, the scan data at that point will not be sufficiently constrained for reliable fusion alignment.
Continue through all fusion points and complete the full scan route
Visit all planned fusion point locations during the scan. Complete the full site coverage and close the overall scan loop before stopping. Stop the scan in the app and wait for the solid green LED before powering off.
Aerial-Ground Fusion is not Map Fusion, and the two do not combine. You cannot feed a completed Map Fusion model in as ground data, and you cannot run Map Fusion as part of an aerial-ground project. The ground input is raw scan segments only: up to 10 of them under a 200-minute total, the same segment ceiling as Map Fusion. Plan each segment at 20 to 30 minutes, sized to the workstation (Section 4), split per standard scan-splitting practice (section 6.2), and hold RTK Fixed in every segment. Segments containing fusion points must include the loop passes at those points.
Drone Flight and Aerial Data Collection
The drone flight has 2 distinct phases that must both be completed: the main grid mission over the site, and the takeoff/landing image sequences at each fusion point. The grid mission provides the broad aerial coverage. The takeoff/landing sequences provide the visual bridge between the aerial and ground perspectives. Without both, the fusion will fail or produce degraded results.
Main Grid Mission
Set the drone coordinate system to WGS84 and configure the camera
On the DJI remote controller, confirm the coordinate system is WGS84 before setting up the flight mission. This must match the ground scanner's coordinate system setting.
In camera settings, select the wide-angle lens and disable the camera's distortion correction. These apply to the grid mission and the takeoff/landing sequences alike, and to every image in the set.
Plan a smart grid flight pattern over the survey area
Use the DJI controller's smart grid function. Frame the survey area on the controller map to define the flight boundary, extending coverage 6.5 to 10 ft beyond the target area. A tic-tac-toe pattern (2 perpendicular sets of parallel flight lines) provides the most complete coverage for Aerial-Ground Fusion.
Set capture mode, GSD, gimbal angle, altitude, and overlap
Use oblique capture mode with the gimbal angle set to 45 degrees; the taller the buildings and the more facade detail required, the larger the oblique angle. Set GSD (ground sampling distance) to 1 centimeter or below. Set flight height above the tallest building in the survey area to ensure clearance. In advanced settings, set both side overlap and forward overlap to 85 percent.
For large areas with complex structures, plan 1 to 2 additional altitude passes above the main altitude. The height difference between adjacent passes must not exceed twice the lower altitude.
Execute the grid mission and monitor continuously
Tap Start. The drone will fly the planned route automatically. Monitor the drone throughout the flight, verify RTK holds a Fixed solution for the entire capture, and be prepared to take manual control whenever necessary. If conditions change (wind, unexpected obstacles, airspace alerts), pause the mission and resume when it is safe to continue. Do not leave the drone unmonitored during the mission.
Takeoff and Landing Image Sequences
After the main grid mission, the drone must visit each fusion point location and capture a continuous image sequence from just above ground level to above flight height. This is the most critical and most frequently skipped step in Aerial-Ground Fusion. A grid flight without these sequences will not fuse reliably with the ground scan.
One sequence, seen from the side. Every photo aims at the same fixed target. Shoot from at least 3 directions at every point.
Plan sequences at every fusion point per the Section 3 distribution: no fewer than 4 points covering 8 directions, adjacent points within 165 ft. Each sequence must provide continuous coverage from 1.5 to 3 ft above the ground to above the maximum oblique flight altitude, with adjacent image overlap of 85 percent or greater. Insufficient sequences or overlap causes LCC Studio to fail alignment between the ground scan and aerial imagery. The resulting model will have missing sections or gross misalignment that cannot be corrected without recollecting the data.
Fly to the first fusion point and descend to 1.5 to 3 ft above ground
After the grid mission is complete, navigate the drone to the first fusion point. Descend to between 1.5 and 3 ft above the ground and aim the camera at the point's fixed target: a stable object 65 to 130 ft away, in a texture-rich direction, that was also visible to the ground scanner at that location. Every photo in the set aims at that same target.
Capture 30 to 40 photos while ascending steadily past the flight altitude
Enable timed photo capture at 0.5-second intervals and slowly, steadily ascend from the starting position until the drone is above the maximum altitude flown during the grid mission. Control the ascent speed by hand so adjacent shots keep their overlap. The sequence must be continuous with no large position jumps. The lowest images must overlap spatially with the ground scanner's trajectory. The highest images must overlap with the grid flight imagery.
Shoot from at least 3 different horizontal directions at each fusion point to ensure sufficient angular coverage for the fusion algorithm. On the M4E and similar drones, set the fixed target as a Point of Interest and enable the Orbit function; the drone holds the aim while you manage the climb and the interval timer does the shooting.
Keep adjacent image overlap at or above 85 percent throughout the sequence
The value of the takeoff/landing sequence is the visual continuity between ground level and flight altitude. If there are large gaps between images (the drone moved too fast, or too few images were captured), the transition between the ground and aerial perspectives will be broken and the fusion alignment will fail at that point.
Photos taken during a continuous ascent or a continuous descent are all valid, so a climb-and-descend pass at one point can cover two of the three directions.
Repeat at every fusion point before landing
Visit each planned fusion point location in sequence and capture the full takeoff/landing image sequence at each one, budgeting roughly 90 to 120 photos per point. Preserve the DJI-generated folder structure on the SD card; do not mix images from different fusion point locations into a single flight folder.
Folder Organization Before Processing
How you organize the drone images after the flight determines how you will import them in LCC Studio or LixelStudio. Organize the folders before you sit down to process. Doing it during an active processing session introduces errors.
XGRIDS' data storage requirement is the same structure its quality inspection tool expects: all drone photos and all scanner data in one parent folder, with the scanner data in a subfolder named lixel and the drone photos in a subfolder named drone.
├─ drone/ # all drone image data goes here
│ └─ (all drone images, organized by flight folder)
└─ lixel/ # scanner project folder(s) go here
└─ (project folder from the device)
Do not rename the project folder suffixes. The prefix of a scanner project folder name may be modified, but the suffix must be left alone; changing it causes project matching errors during processing.
Place the scanner project folder in the lixel subfolder
Copy the project folder from the device into the lixel/ subfolder of your project directory. Do not alter the internal structure of the project folder.
Place all drone images in the drone subfolder
Copy all images from the drone's SD card into the drone/ subfolder, preserving the DJI-generated folder structure. This includes the grid flight folders and the takeoff/landing sequence folders.
For LixelStudio: run the XGRIDS Pose tool on the drone data
The XGRIDS Pose tool (extract_preview_poses application, provided by XGRIDS) processes the drone images and generates the camera pose data that LixelStudio needs to align the aerial imagery with the LiDAR point cloud. Run this tool before opening LixelStudio. You can use the included visualization tool to verify that all camera poses have been correctly extracted before proceeding.
For LixelStudio: import the parent folder and configure processing
Open LixelStudio and navigate to the Aerial-Ground Map Fusion project type. Point it to the parent folder containing both the drone/ and lixel/ subfolders. LixelStudio will read the project structure and configure the merge parameters. Verify the coordinate system and run processing.
For LCC Studio, the same parent folder feeds the two inputs directly: the scanner project file under Ground Data and the drone image folder under Aerial Data. See 9.4 in the LCC Studio module for the run procedure.
Data Quality Inspection Before Processing
Aerial-ground processing runs are measured in hours or days; the quality inspection takes minutes. XGRIDS provides a quality inspection tool that reads the assembled project folder and reports exactly the things that make reconstructions fail: RTK status, coordinate systems, trajectory overlap, photo distribution, and cumulative totals. Run it on every job before committing the processing run.
Running the Quality Inspection Tool
Place the tool next to the data
Put the quality inspection tool's .exe file and its associated .dll files at the same directory level as the lixel and drone folders (inside the parent project folder).
Run it against the parent folder
Drag the parent directory onto the tool's .exe file to start it, or double-click the .exe from its position alongside the two folders. The tool writes its report files next to the data.
Review the output files
The tool produces the five files below. Together they answer every item on the pre-processing check list.
Quality Inspection Output Files
| File | Contents | How to check it |
|---|---|---|
drone_exif.dat |
Drone photo metadata list. The RTK status field must read 50 (Fixed) on every photo; photos with any other value are excluded from processing. The camera type flag reads 0 for the wide-angle camera and 1 for the zoom camera. | Open as text or in a spreadsheet; scan the RTK status column for values other than 50 and the camera flag for stray zoom shots. |
merge.nvm |
Drone photo poses and scanner trajectory poses in one file. Shows the distribution of photos and trajectories and the device orientation during capture. | Open in VisualSFM (VisualSFM_windows_cuda_64bit). Right-click to rotate, left-click to pan, Ctrl + scroll to zoom. Look for evenly distributed vertical photo columns and full trajectory coverage. |
drone_pose.csv |
Absolute coordinates of every drone photo: northing, easting, ellipsoidal height, latitude, longitude, and the photo name with its parent folder path. | Spot-check heights: the lowest takeoff/landing photos should sit within 3 ft of the ground and the highest above the oblique flight altitude. |
lixel_pose.csv |
Absolute trajectory poses of the handheld scan, with RGB color values per project and the numeric folder suffix for each trajectory point. | Load in CloudCompare, set the display color mode to the RGB fields, and confirm the trajectory covers the site and loops each takeoff/landing point. If a stretch looks wrong, use the point-picking tool to read its suffix and identify the project. |
timer.csv |
Per-segment scan durations, the cumulative scan time (lixel sum timer), and the total photo count (drone image number). | Compare the totals against the Section 4 limits: under 200 minutes and 10 segments of ground data, inside the memory-stable window for the workstation, and 100 to 10,000 photos. |
The Nine Checks That Decide Reconstruction Success
Adapted from XGRIDS' own pre-processing check list. Every failed reconstruction traces back to one of these.
| Check | How to verify |
|---|---|
| 1. Both aerial and ground coordinate systems are WGS84, with a consistent vertical (height) datum | Check gnss.csv under project_data in the original scanner project folder; confirm WGS84 in the controller's RTK settings. |
| 2. Photos captured with a mapping-grade wide-angle lens, distortion correction disabled | Uncorrected photos show visible distortion and vignetting toward the corners; that is what you want. Corrected photos with clean corners mean the setting was left on. |
| 3. All photos contain valid RTK data | RTK status field in drone_exif.dat reads 50 on every row. |
| 4. The ground trajectory covers the site, and on multi-segment jobs every segment is present and complete | lixel_pose.csv in CloudCompare, RGB color mode: each segment's trajectory shows in its own color, covers its area, and no segment is missing. Use the point-picking tool to read the folder suffix of any anomalous stretch and identify the project. |
| 5. Takeoff/landing points are evenly distributed across the survey area | merge.nvm in VisualSFM: vertical photo columns spread across the whole site, not clustered at one end. |
| 6. Takeoff/landing photos aim at texture-rich objects 65 to 100 ft away, visible from ground and air, with adjacent overlap of at least 85% | Review each point's photo sets: same fixed target in every frame, no sets aimed at distant horizons or blank surfaces. |
| 7. The operator circled the takeoff/landing points during the ground scan | Trajectory in lixel_pose.csv or merge.nvm shows loops around each point, not a straight pass-through. |
| 8. Lowest takeoff/landing photo is within 3 ft of the ground; highest exceeds the maximum oblique flight altitude | Photo heights in drone_pose.csv, or the vertical extent of each column in merge.nvm. |
| 9. Ground totals, photo count, and point cloud quality are within limits: 10 segments maximum, under 200 minutes total, inside the memory-stable window, 100 to 10,000 photos, and a clean cloud with no layering, misalignment, drift, or weather noise | lixel sum timer and drone image number in timer.csv against the Section 4 limits; review the cloud, where weather noise and drift are visible directly. |
Processing: LCC Studio vs LixelStudio in Detail
Both pipelines take the same field data and produce different outputs. The decision between them is a deliverable decision, not a quality decision. A Gaussian Splat from LCC Studio is not better or worse than a point cloud from LixelStudio. They serve different purposes.
| Factor | LCC Studio | LixelStudio |
|---|---|---|
| Output format | 3D Gaussian Splat (3DGS). Photorealistic rendered model. | Merged point cloud. E57, LAS/LAZ, RCP, or PLY. |
| Compatible ground devices | L2 Pro, K2 (PortalCam: see Module 7) | L2 Pro |
| Ground data input | Raw scan segments only. Up to 10 segments under a 200-minute total; plan segments at 20 to 30 minutes. Completed Map Fusion output is not accepted. | Raw scan project folder in the lixel/ subfolder. |
| Aerial image count | 100 to 10,000 images. JPG/JPEG only. Min 1024×768. | No fixed limit documented; determined by Pose tool output. |
| Pre-processing requirement | Folder organization plus the quality inspection tool (Section 8). | XGRIDS Pose tool on the drone data before import; quality inspection tool (Section 8). |
| Suitable deliverables | Walkthroughs, client presentations, web publishing, virtual tours, marketing. | BIM coordination, survey deliverables, measurement, CAD integration, dimension-verified documentation. |
| Hardware requirements | 16-core CPU, 64 GB (2 x 32 GB) minimum with 128 GB (2 x 64 GB) for large datasets, RTX 3090 minimum with RTX 4090 recommended for full-site aerial-ground projects. | Standard LixelStudio hardware requirements. Less GPU-intensive than LCC Studio. |
| Processing time | About 20 to 30 minutes per minute of ground capture, plus the aerial imagery. Multi-day runs are normal for large sites. | Variable. Typically faster than LCC Studio for equivalent coverage. |
You can run both pipelines on the same field data. If you collect the ground scan and organize the drone data in the shared drone/lixel structure, you can process in both applications and deliver either a Gaussian Splat or a point cloud (or both) from a single field session. This requires slightly more organized file management but no additional field time. Do not run both processing jobs simultaneously on the same machine. Process one pipeline to completion before starting the other, or use two separate workstations.
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