It’s an exciting time in machine vision, as 3D vision applications are becoming more robust. However, any machine vision engineer should look at intended applications of a system before making a decision on 2D versus 3D.
2D Machine Vision is Traditional and Reliable
2D systems were the first to be developed in sophisticated, cost-effective ways. They are now firmly embedded in countless industrial and retail automation applications: Barcode reading, label orientation, printing verification, and more.
2D vision cameras capture an image of an object using a two-dimensional map of reflected intensity. This can be expressed in X and Y coordinates. Once captured, the image is typically processed by comparing variations in contrast.
Naturally, there are some technical limitations:
- Depth of focus;
- Ambient light;
- Differences in contrast.
Accurate Dimensional Data is Here With 3D Imaging
With 3D imaging, it becomes possible to capture complete dimensional data for an object. This makes it ideal, for example, in logistics hubs and other environments where specific dimensions are essential.
3D machine vision scanners work by outputting a point cloud, a digitized model that includes both the location and the shape of objects.
Advanced 3D machine vision systems can synthesize point clouds from multiple scanners. This makes it possible to deliver highly accurate imagery of extremely large objects using compact devices.
3D Imaging and Industrial Robotics
To be effective, industrial robots must operate in a 3D world. Without robust machine vision capabilities, the system will be capable of only the most repetitive and structured tasks.
Utility, flexibility, and velocity are increased when a robot uses 3D machine vision to adapt to environmental changes. This facilities sophisticated, vision-guided robotics systems with novel applications, like automated butchering.
To learn more, visit Phase1Vision.com for 2D and 3D camera options from top manufacturers like Sony, Baumer, and Emergent.