3D Information Acquisition Based on Structured Light

Structured light-based 3D information acquisition is an efficient and high-precision measurement technique widely applied in industrial inspection, reverse engineering, medical imaging, and other fields. This method projects specific grating or encoded patterns onto object surfaces, captures deformed light spot images using cameras, and reconstructs 3D shapes through algorithmic analysis of deformation information.

### 1. 3D Scanning Using Plane Erasure Method The plane erasure method is an early 3D measurement technique that moves a known plane (e.g., grating plate) to progressively erase projected patterns on object surfaces while recording image changes at different positions. By combining geometric relationships and triangulation principles, depth information of the object surface can be calculated. This approach offers simplicity in equipment setup but suffers from lower accuracy and efficiency, making it suitable mainly for static object measurements.

### 2. Structured Light-Based 3D Scanning Modern structured light 3D scanning typically employs encoded patterns (such as stripes, Gray codes, sinusoidal gratings) with phase-shifting or temporal coding techniques to enhance measurement accuracy. The key implementation workflow includes: Pattern Projection: Projecting structured light patterns onto target objects using programmable projectors (e.g., DLP controllers). Image Acquisition: Capturing deformed light spots from multiple angles through synchronized camera systems (using OpenCV or MATLAB image acquisition toolboxes). Phase Decoding: Extracting absolute phase information through phase unwrapping algorithms (e.g., quality-guided or multi-frequency phase unwrapping methods). 3D Reconstruction: Calculating 3D coordinates of surface points via triangulation using calibrated camera-projector parameters (implemented with OpenCV's solvePnP function or custom calibration algorithms).

Structured light scanning offers advantages in speed and precision, making it ideal for dynamic or complex surface measurements. Recent integration with deep learning techniques (e.g., CNN-based phase decoding or adversarial networks for noise reduction) has further improved anti-interference capability and reconstruction efficiency, establishing it as a crucial technology in 3D vision research.