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3D machine vision is a powerful technology capable of providing higher accuracy for specific positioning, identification, and detection tasks where traditional 2D machine vision systems often fail to succeed reliably. It offers an alternative to 2D vision technology that can resolve complex problems that 2D systems cannot face with very high accuracy.
3D systems are inherently more complex than 2D systems. 3D machine vision can be used in applications requiring more precise analysis of object size, texture, and depth, such as agriculture, manufacturing, inspection, and quality control. These can all benefit from 3D vision, but deciding between 2D and 3D technology will ultimately depend on the required level of precision, measurement speed, whether objects are fixed or moving, and the lighting characteristics of the object and its environment.
There are mainly 3 forms of 4D imaging technology used in machine vision systems: stereo vision, Time of Flight (ToF), laser triangulation (3D profiling), and structured light.
Stereo vision utilizes two or more calibrated 2D cameras focused on the same object. They can provide complete FoV 3D measurements in dynamic environments based on triangulation of light from multiple angles.
Alternatively, laser triangulation uses a camera perpendicular to the beam to measure variations in a laser beam projected onto an object. This method requires continuous linear motion, such as a conveyor belt, but provides very high imaging accuracy.
Time of Flight (ToF) measures the time required for light from a modulated infrared illumination source to reach an object and return to the ToF sensor, then generates point clouds based on these measurements.
Combining 2D machine vision cameras with imaging library software is a proven strategy. However, when taking measurements, changes in lighting can adversely affect accuracy. Excessive light can produce overexposed photos, causing light bleeding or blurred object edges, and insufficient lighting can adversely affect the clarity of edges and features appearing on 2D images.
In applications where lighting is not easily controlled and therefore cannot be changed to fix the lens, 2D machine vision systems may struggle to generate reliable images.
3D machine vision cameras can address these issues by recording accurate depth information. Point clouds and depth maps are two types of 3D images with highly accurate and useful data. Each pixel of an object is considered in space, providing users with X, Y, and Z plane data as well as corresponding rotation data for each axis.
Compared to 3D, this makes 2D machine vision a specialized choice for applications involving dimensional measurement, spatial management, thickness measurement, Z-axis surface detection, and depth-related quality control. Traditional 2D image processing can still be used with collected images, creating implementable solutions for many machine vision problems.
