Structured light

A structured light pattern (left) designed for surface inspection

Structured light is a method used in 3D scanning and computer vision that measures the shape and depth of an object by projecting a predefined pattern of light—such as stripes, grids, or dots—onto its surface and analyzing the resulting distortions. These deformations reveal the object's three-dimensional geometry based on the principle of triangulation, enabling the creation of a detailed 3D model. The scanning process relies on coding techniques for accurate shape measurement. The most widely used ones are binary, Gray, and phase-shifting, each offering distinct advantages and drawbacks.

Structured light technology is applied across diverse fields, including industrial quality control, where it is used for precision inspection and dimensional analysis, and cultural heritage preservation, where it assists in the documentation and restoration of archaeological artifacts. In medical imaging, it facilitates non-invasive diagnostics and detailed surface mapping, particularly in applications such as dental scanning and orthotics. Consumer electronics integrate structured light technology, with applications ranging from facial recognition systems in smartphones to motion-tracking devices like Kinect. Some implementations, especially in facial recognition, utilize infrared structured light to enhance accuracy under varying lighting conditions.

Process

Structured light sources on display at the 2014 Machine Vision Show in Boston

An Automatix Seamtracker arc welding robot equipped with a camera and structured laser light source, enabling the robot to follow a welding seam automatically

Structured light measurement of a three-dimensional object is based on the principle of triangulation to determine the three-dimensional coordinates of points on the object's surface. This process involves analyzing distortions in a predefined light pattern projected onto the object. The system consists of a projector and a camera, positioned at a fixed distance from each other, referred to as the baseline, and oriented at specific angles relative to one another. The projector directs a structured light pattern—such as stripes, grids, or dots—onto the object's surface. The camera captures the resulting distortions in the pattern caused by the object's geometry. These distortions contain information about the surface shape, which is used to calculate depth values.

The measurement process relies on the baseline distance and the known angles of projection and image capture. Trigonometric calculations are applied to interpret the displacement of the projected pattern, yielding depth information. When the structured light encounters a non-planar surface, the pattern exhibits predictable distortions, enabling the reconstruction of the object's three-dimensional structure. Accurate reconstruction depends on precise calibration of the system and the use of pattern analysis algorithms. Calibration involves determining the exact spatial relationship between the projector and the camera, as any misalignment may result in errors in depth measurements and, subsequently, geometric distortions in the reconstructed model.

Types of coding

Structured light scanning relies on various coding techniques for three-dimensional shape measurement. The most common ones are binary, Gray, and phase-shifting. Each method presents distinct advantages and drawbacks in terms of accuracy, computational complexity, sensitivity to noise, and suitability for static or dynamic objects. Binary and Gray coding offer reliable, fast scanning for static objects, while phase-shifting provides high detail. Hybrid methods like binary defocusing and Fourier transform profilometry (FTP) balance speed and accuracy, enabling real-time applications.

Binary coding

Binary coding uses alternating black and white stripes, where each stripe represents a binary digit. This method is computationally efficient and widely employed due to its simplicity. However, it requires the projection of multiple patterns sequentially to achieve high spatial resolution. While this approach is effective for scanning static objects, it is less suitable for dynamic scenes due to the need for multiple image captures. In addition, the accuracy of binary coding is constrained by projector and camera pixel resolution, and it needs precise thresholding algorithms to distinguish projected stripes accurately.

Gray coding

Further information: Gray code

Gray coding, named after physicist Frank Gray, is a binary encoding scheme designed to minimize errors by ensuring that only one bit changes at a time between successive values. This reduces transition errors, making it particularly useful in applications such as analog-to-digital conversion and optical scanning. In structured light scanning, where Gray codes are used for pattern projection, a drawback arises as more patterns are projected: the stripes become progressively narrower, which can make them harder for cameras to detect accurately, especially in noisy environments or with limited resolution. To mitigate this issue, advanced variations such as complementary Gray codes and phase-shifted Gray code patterns have been developed. These techniques introduce opposite or phase-aligned patterns to enhance robustness, improve accuracy, and aid in error detection and correction in complex scanning environments.

Phase-shifting

Phase-shifting techniques use sinusoidal wave patterns that gradually shift across multiple frames to measure depth. Unlike binary and Gray coding, which provide depth in discrete steps, phase-shifting allows for smooth, continuous depth measurement, resulting in higher precision. The main challenges are that depth ambiguities can occur because the repeating wave patterns make it difficult to determine exact distances, which requires extra reference data or advanced processing to resolve, and, because multiple images are needed, this method is not ideal for moving objects—as motion can create distortions and errors in the final measurement.

Hybrid methods

To address the limitations of phase-shifting in dynamic environments, binary defocusing techniques have been developed, in which binary patterns are deliberately blurred to approximate sinusoidal waves. This approach integrates the efficiency of binary projection with the precision of phase-shifting, enabling high-speed 3D shape capture. Advances in high-speed digital light processing (DLP) projectors have further supported the adoption of these hybrid methods in applications requiring real-time scanning, including biomedical imaging and industrial inspection.

Fourier transform profilometry (FTP) measures the shape of an object using a single image of a projected pattern. It analyzes how the pattern deforms over the surface, enabling fast, full-field 3D shape measurement, even for moving objects. The process involves applying a Fourier transform to convert the image into frequency data, filtering out unwanted components, and performing an inverse transform to extract depth information. Although FTP is often used alone, hybrid systems sometimes combine it with phase-shifting profilometry (PSP) or dual-frequency techniques to improve accuracy while maintaining high speed.

See also

• Depth map
• Dual photography
• Laser Dynamic Range Imager
• Lidar
• Light stage
• Range imaging
• Stereoscopy
• Structured Illumination Microscopy (SIM)
• Structured-light 3D scanner – Sensor that can create 3D scans using visible light
• Time-of-flight camera

External links

• Projector-Camera Calibration Toolbox
• Tutorial on Coded Light Projection Techniques
• Structured light using pseudorandom codes
• High-accuracy stereo depth maps using structured light
• A comparative survey on invisible structured light
• A Real-Time Laser Range Finding Vision System
• Dual-frequency Pattern Scheme for High-speed 3-D Shape Measurement
• High-Contrast Color-Stripe Pattern for Rapid Structured-Light Range Imaging