Stemmer Imaging
Olmec - Quality through innovation
Multipix Imaging

3D Machine Vision

One of the more recent developments in machine vision is the widely commercial availability of algorithms and software tools that can process and measure pixels in the third dimension. The most common applications work in 2 dimensions, X and Y. In the real world this translates to the accurate location of an object within the image - or the actual position of a product on a conveyor belt for example. In a manufacturing environment this works very well well when the product type and size is known - as when the height of the product is a fixed value - as long as the product outline can be recognised and measured, the height is assumed.

In a scenario where multiple product types on a conveyor belt are presented to the camera, this may be a problem for traditional 2D systems, as if a product height is not where it is expected to be, the system will fail. A 3D vision system can extrapolate a pixel’s position, not just in the X and Y, but also the Z.

3D machine vision is achieved using a variety of techniques, which include (but are not limited to) stereo vision, point clouds, or 3D triangulation. Taking stereo vision as an example, this works in the same way that the human brain does (and in fact any animal with two eyes). Images from each eye are processed by the brain and the difference in the images caused by the displacement between the two eyes is used by the brain to give us perspective. This is critical when we are judging distances.

3D Stereo machine Vision uses two cameras in an identical way. The software reads both images and can compare the differences between the two images. If the cameras are calibrated so that the relative position between each camera is known, then the vertical position (Z) of an object can be measured. In computing terms, this takes more processing time than an X and Y measurement. But with modern, multi-core processors now ubiquitous, 3D machine vision is no longer limited by processing time, meaning ‘real time’ systems can be improved with 3D machine vision.

An obvious real world benefit of this is 3D robot guidance and taking the initial example of known product dimensions on a conveyor belt, a 3D robot guidance system will deal with product variants even when the next product type and size is unknown. With a competent 3D Vision System, the robot will not only receive X, Y AND Z data but also the corresponding roll, pitch and yaw angle of each pixel in the combined image.