A web-based 3D point cloud viewer and analysis tool.
Load, visualize, and analyze LAS/LAZ point cloud files directly in your browser — no plugins, no desktop app required.
Drag & Drop Loading
Post-Processing Filters (EDL / SSAO)
View Controls & Color Modes
ICP Registration
- GPU-Accelerated 3D Rendering — Powered by Three.js with custom WebGL shaders
- Color Modes — Intensity, Height (turbo colormap), RGB
- Post-Processing — Eye-Dome Lighting (EDL), Screen-Space Ambient Occlusion (SSAO)
- Measurement Tools — Distance measurement, polygon selection, point info
- Map Comparison — Overlay two point clouds with transform controls (offset + rotation)
- ICP Registration — Iterative Closest Point alignment with initial pose support
- Large Coordinate Support — UTM / survey coordinates handled with automatic float64 centering to prevent precision loss
- Clipping Planes — X/Y/Z axis clipping
- Camera Bookmarks — Save and restore camera positions
- Statistics — Point count, bounding box, density, height distribution histogram
- SOR Filter — Statistical Outlier Removal (k-nearest neighbors)
- Cross Section — Extract slices along any axis
- Volume Estimation — 2.5D grid-based volume computation
- C2C Distance — Cloud-to-Cloud distance between two point clouds
- Drag & Drop — Drop point cloud files directly into the viewer
- File Management — Browse, rename, delete saved point clouds
- Screenshot — Export the current view as PNG
- Dark / Light Theme
- Responsive UI — Works on desktop and tablets
git clone https://github.com/warhammer50K/WebPointCloud.git
cd WebPointCloud
pip install -r requirements.txt
python app.pyOpen http://localhost:5000 in your browser, then drag & drop a point cloud file or click Load Map > Upload File.
A sample point cloud is included at sample/building_scan.las (100k points) — drag it into the viewer to try it out.
| Format | Extension | Notes |
|---|---|---|
| LAS | .las |
ASPRS LAS 1.2 - 1.4 |
| LAZ | .laz |
Compressed LAS (via lazrs) |
| PLY | .ply |
ASCII and binary (little/big endian) |
| XYZ | .xyz .txt .csv |
Whitespace or comma delimited |
| PCD | .pcd |
Point Cloud Library format (ASCII and binary) |
| PTS | .pts |
Leica / common scanner ASCII format |
Point clouds from surveying or GIS workflows often use UTM or other projected coordinate systems with large absolute values (e.g., X=712345, Y=7034567). Storing these directly in float32 causes visible precision loss (jitter, staircase artifacts).
WebPointCloud handles this automatically:
- Read in float64 — raw coordinates are loaded at full double precision
- Compute center offset — bounding-box midpoint is extracted as a float64 offset
(ox, oy, oz) - Center & convert — coordinates are centered (
x - ox) in float64, then cast to float32 for GPU rendering - Reconstruct on display — point info, legend, and analysis results add the offset back to show original coordinates
When two clouds are loaded for comparison, the offset difference is applied automatically so they align correctly in the scene even if they come from different UTM zones or reference frames.
No configuration is required — just load the file and coordinates are preserved to sub-millimeter precision regardless of their absolute magnitude.
| Environment Variable | Default | Description |
|---|---|---|
WEB_PORT |
5000 |
HTTP server port |
WPC_DATA_DIR |
~/webpointcloud |
Data directory (maps + logs) |
WPC_MAPS_DIR |
$WPC_DATA_DIR/maps |
Point cloud storage directory |
FLASK_DEBUG |
0 |
Enable Flask debug mode |
FLASK_SECRET_KEY |
auto-generated | Flask session secret key |
WPC_PUBLIC |
0 |
Set to 1 to disable IP whitelist (for public deployments) |
WPC_MAX_UPLOAD_MB |
5120 |
Maximum upload size in MB |
Browser ──── HTTP ────── Flask (Python)
│ │
├─ Three.js 3D viewer ├─ REST API (/api/*)
├─ WebGL shaders ├─ LAS read/write (laspy)
├─ Analysis UI ├─ Analysis (numpy, scipy)
└─ Web Workers └─ File management
| Library | License | Usage |
|---|---|---|
| Three.js | MIT | 3D rendering |
| pako | MIT + Zlib | Zlib decompression |
| Flask | BSD-3 | Web framework |
| laspy | BSD-2 | LAS file I/O |
| NumPy | BSD-3 | Numerical computing |
| SciPy | BSD-3 | Spatial algorithms (KDTree, SOR) |
Contributions are welcome! Please open an issue or submit a pull request.
- Fork the repository
- Create your feature branch (
git checkout -b feature/my-feature) - Commit your changes (
git commit -m 'Add my feature') - Push to the branch (
git push origin feature/my-feature) - Open a Pull Request
This project is licensed under the MIT License.