Structured light scanning relies on various coding techniques for 3D shape measurement. The most widely used 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 dynamic objects. Binary and Gray coding offer reliable, fast scanning for static objects, while phase-shifting provides higher detail. Hybrid methods, such as binary defocusing and Fourier transform profilometry (FTP), balance speed and accuracy, enabling real-time scanning of moving 3D objects.
Gray coding 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 as well as to 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 introduce
artifacts in the measurement. 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 ==