acquiring and making sense of the larger datasets 3D imaging produces. ‘We are, in a sense, reductionists in machine


vision because most of the time we try and make the problem into a planar one,’ comments Ben Dawson, director of strategic development at Teledyne Dalsa. ‘We love things like semiconductors and machine parts that are essentially flat that we can dimension. Tis is both because the depth of field of most cameras is quite limited and that image processing is more difficult for 3D data. ‘Te problem in 3D is essentially calibration,


getting measurements to some calibrated standard, which requires in general a lot more information than a planar calibration. In 3D, you’ve got to compensate for optics and geometry, i.e. the position of the camera and lighting with respect to the object,’ he says. Tere are efforts to make


in terms of gain and other parameters. ‘It’s not only machine builders that have to overcome the complexities of 3D imaging when designing their systems and algorithms,’ Herrmann continues. ‘Camera manufacturers have to understand the requirements for using their products for making 3D measurements. Tere are more requirements for the camera in 3D imaging; to achieve the same 2D accuracy in 3D is hard to do.’ Adimec’s high-resolution 2D cameras are used


I would suggest 3D imaging is still very much a niche capability


the technology easier to use; LMI’s Gocator 3D sensor, for instance, is pre-calibrated and compensates for temperature variation. With changes in temperature, the mechanics and the laser projection can move slightly which causes inaccuracies. Companies like LMI are building in compensation for temperature variation so that it delivers real, calibrated measurements. With laser profiling, height increases cause the


magnification to change because of the optical angles and consequently it’s more complicated to calibrate the system. ‘Tere’s a push to make 3D simple and put it into a smart camera for measurement and inspection and we are now starting to see a choice in this market,’ states Williamson. Te constraints tend to be in terms of the


environment and the object under analysis, according to Lina of Matrox Imaging. For example, occlusions can be a problem with laser scanning, stereoscopy requires features in the scene to generate the data, while shape from focus requires a textured object, and so on. Even camera manufacturers are having


to engineer higher performance models to accommodate 3D vision. ‘A lot of our customers have been struggling with the kind of requirements for 3D imaging,’ says Jochem Herrmann, chief scientist at Adimec. Generating 3D data with a 2D camera, which


generally involves combining several 2D images to create one 3D image, needs more accurate illumination compared to taking a single 2D snapshot. It also requires a very stable and accurate camera to ensure each image is the same


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for 3D imaging. One of the company’s larger markets for machine vision is semiconductor and electronics manufacturing, in which, Herrmann says, all of its customers are moving from 2D to 3D, or already have done so. Many high-end


semiconductor metrology equipment now incorporates some sort of 3D imaging capability and checks are made on the volume of solder deposited on PCBs


or the thickness and flatness of silicon wafers. Chromasens’ tri-linear colour line scan system is particularly suitable for scanning large areas in 3D and provides the accuracy required for semiconductor inspection. Te system generates a 3D image based on the surface texture of the object, correlating a 3D image from two separate line scan images. GPUs are used for image processing, making it fast at up to 200 million measurements per second. Markus Schnitzlein, CEO of Chromasens, says the system has a resolution of 10µm per pixel, depending on the optics and the width of the object, and can measure to an accuracy of 1µm, which can be required in the semiconductor industry. Apart from ease of setup and calibration,


inspection speed is also a problem in some circumstances, according to Dr Wolfgang Eckstein, managing director of MVTec Soſtware. ‘In terms of speed, you’re always somewhat at a limit when working in 3D; data acquisition can be a couple of seconds, depending on the 3D imaging method. Tis isn’t so critical in robotics, but can be in inspection of wood or food. But this will change in the near future due to significant improvement in 3D sensor technologies,’ he says. Te other side of improvements in the


technology is in the soſtware. ‘Te difficulty is that whatever tools are available in 2D have to be made available in 3D,’ states Eckstein. Tools like blob analysis, edge inspection, feature extraction, filters, classification, object recognition, alignment, identification – all are common soſtware tools in 2D and similar tools are needed in 3D. ‘You have to acquire the 3D information,


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