Slide 002: Laser Triangulation and Photogrammetry¶
Slide Visual¶
Slide Overview¶
This slide covers two additional 3D scanning approaches: laser triangulation scanning and photogrammetry. Students learn how each method captures geometry, their respective strengths and limitations, and when to choose one technology over another for a given application.
Instruction Notes¶
Laser Triangulation Scanning¶
Laser triangulation projects a laser line (or point) onto the object surface. A camera sensor, positioned at a known angle from the laser source, observes where the laser line falls on the sensor. As the surface depth changes, the laser line shifts position on the sensor — this displacement is converted to distance through trigonometric calculation.
The scanner moves relative to the object (or the object rotates on a turntable) to sweep the laser across the entire surface, building a 3D point cloud line-by-line. This line-scanning approach is inherently slower than area-based structured light but offers advantages in specific scenarios:
- High accuracy on edges and sharp features: The narrow laser line resolves fine edges better than projected patterns
- Less sensitive to ambient light: Laser wavelength filters on the camera reject most ambient interference
- Longer working distances: Laser scanners can operate at 100 mm to several meters, depending on configuration
- Single-line simplicity: Calibration is straightforward compared to multi-pattern structured light
Typical laser triangulation accuracy ranges from 0.01 mm (metrology-grade) to 0.1 mm (desktop), with point spacing of 0.02–0.5 mm.
Photogrammetry¶
Photogrammetry reconstructs 3D geometry from multiple 2D photographs. The process involves:
- Image capture: 30–200+ overlapping photos from different angles (60-80% overlap recommended)
- Feature detection: Software identifies matching features (SIFT, ORB algorithms) across images
- Bundle adjustment: Camera positions and 3D point locations are simultaneously optimized
- Dense reconstruction: A dense point cloud is generated from the sparse feature matches
- Mesh generation: Points are connected into a textured 3D mesh
Photogrammetry requires no specialized hardware — a DSLR camera, smartphone, or even a drone can serve as the capture device. However, results depend heavily on lighting consistency, image quality, sufficient overlap, and surface texture. Featureless, glossy, or transparent surfaces fail because the software cannot find matching features.
Technology Comparison¶
| Factor | Structured Light | Laser Triangulation | Photogrammetry |
|---|---|---|---|
| Speed | Fast (area scan) | Moderate (line scan) | Slow (manual capture + processing) |
| Accuracy | 0.01–0.1 mm | 0.01–0.1 mm | 0.1–1.0 mm (scene dependent) |
| Equipment cost | $500–$50,000 | $1,000–$100,000 | Camera + software ($0–$5,000) |
| Best for | Lab scanning, organic shapes | Industrial inspection, edges | Large objects, outdoor scenes, texture capture |
Key Talking Points¶
- Laser triangulation scans line-by-line — slower but excels at sharp edges and long range
- Photogrammetry uses only photographs — lowest hardware cost but highest skill requirement
- Ambient light affects structured light more than laser triangulation
- Photogrammetry captures excellent color/texture but lower geometric accuracy
- Technology selection depends on object size, required accuracy, budget, and surface properties
Learning Objectives (Concept Check)¶
- Explain how laser triangulation differs from structured light scanning
- Describe the photogrammetry pipeline from image capture to 3D model
- Compare accuracy, speed, and cost across the three scanning technologies
- Identify which technology is best suited for a given application scenario
Last Updated: 2026-03-19