Machine vision systems typically consist of industrial light sources, image acquisition units, image processing units, image processing software, and network communication devices. Both 2D and 3D technologies play crucial roles in automated industrial quality control and online inspection. To combine these two technologies and create a more reliable and efficient machine vision inspection system , it's essential to first understand their respective advantages and limitations.
2D vision technology is largely driven by contour-based pattern matching to identify the position, size, and orientation of parts. Technicians can use 2D to identify parts and create inspection tools that dynamically adapt to part position, angle, and size, enabling robust measurements of part movement. Today's pattern matching handles occlusion, clutter, distortion, contrast inversion, overlapping parts, and uneven lighting.
2D sensors do not support shape-related measurements. For example, 2D sensors cannot measure features such as object flatness ( phone frame flatness detection ) , surface angles, partial volume, or distinguish between objects of the same color, or differentiate between object positions with contact sides.
3D vision builds upon mature 2D capabilities by adding a second layer of data describing shape, which is crucial for designing highly reliable measurement systems.
3D vision can measure shape information that 2D systems cannot. Therefore, shape-related features such as the flatness of an object, surface angles, and volume can be measured.
Another benefit of 3D machine vision is its ability to stitch together 3D point clouds from a relatively small number of scanners using known artifacts, thereby calibrating them to a common coordinate system. For example, large objects such as truck frames can be scanned using multiple scanners. Positioning and aligning hundreds of 2D cameras and using photogrammetry to generate a 3D model is far more complex and inaccurate than using a small number of high-precision 3D scanners.
Why choose 3D machine vision ?
A constant contrast ratio is ideal for inspecting low-contrast objects.
Volume measurements (X , Y , and Z axes ) provide shape and position.
Insensitive to minor changes in lighting or ambient light
Integrated optics, illumination, and calibration features improve repeatability.
Setting up a multi-sensor system for large object detection is now simpler.
