Editorial Feature

What is 3D Machine Vision?

3D machine vision is used to automatically extract information from an image, with many applications in several fields and differs from technologies such as image processing. This article will provide an overview of 3D machine vision technology and its current applications.

What is 3D Machine Vision?

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3D Machine Vision: An Overview

3D machine vision is a recent field in automation that has rapidly become a central technology in many industrial processes. Essentially, this technology is the “eyes” of industrial robots and related technologies.

Using 3D machine vision, a 3D visual representation of a product is built. This allows machines to make automated process decisions based on factors such as the size, shape, orientation, location, or color of an object.

Models produced by machine vision techniques are comprised of discrete data points and can be modified using specialized software. This allows operators to examine models for damage and abnormalities. Furthermore, operators can use the model to interact with objects, allowing for further modification and the retrieval of pertinent information.

Definitions of the term vary, but it includes all the methods and technologies used to automatically extract information from images. This contrasts with methods such as image processing, where the output is another image. The field encompasses technologies, integrated systems, actions, expertise, methods, hardware, and software.

As a systems engineering discipline, machine vision is distinct from computer vision. This field attempts to integrate multiple current technologies in novel ways and use them to solve real-world problems in industrial automation and related areas.

Methods and Equipment

Image acquisition is the first step in the automated process. This step uses equipment, including cameras, lighting, and lenses. Equipment is designed to provide the necessary differentiation required to accurately construct an image. The required information is extracted by digital processing software and decisions can be made based on specific parameters.

Imaging devices such as cameras can either be used separately or as part of an integrated system. This latter machine vision equipment variant is referred to as a smart camera. Another option is to include the device’s full processing function into the camera: this is referred to as embedded processing. Several implementations use multi-function digital cameras.

Aside from conventional imaging, machine vision equipment can include hyperspectral imaging, multispectral imaging, X-ray imaging, 3D surface imaging, and infrared band imaging. Amongst the various 3D machine vision imaging techniques, scanning-based triangulation is one of the most utilized. Lasers are used to scan the surface of an object.

Other methods utilized in 3D machine vision include time-of-flight, grid-based, and stereoscopic vision techniques. Processes utilized in image processing include filtering, pattern recognition, edge detection, color analysis, segmentation, thresholding, and deep learning/neural net processing. Pass/fail detections are commonly utilized as outputs.

Recent advances in deep learning methods have proven beneficial for the machine learning field. These techniques provide automated processes and computers to image objects comparable to human operators. Deep learning algorithms are trained with thousands of existing images to improve them.


The main advantage of 3D machine vision is the ability to reduce human intervention in operational processes. Typically difficult and dangerous industrial procedures such as heavy lifting and operating in extreme environments can be fully automated, providing robots with smarter and more efficient automated capabilities.

Another distinct benefit of 3D machine vision is the ability to fully integrate automated assets with human operators, allowing for a collaborative work approach between humans and robots. The main aim of 3D machine vision is to provide automated systems with enhanced “visual sense” capabilities, giving them smart functionalities and the ability to adapt to environments.


In recent years, research has increased into 3D machine vision, with technologies now being used in several fields. 3D machine vision has been integrated into several key industrial and commercial processes.

Applications for 3D machine vision include manufacturing, archaeology, architecture, sculpture, fashion design and production, medical applications, entertainment, visual computing, and feature identification, evaluation, and comparison. Techniques have also been applied in virtual environments for military, aviation industry training, and many areas where real-life training may be dangerous.

In Summary

3D machine vision has the potential to be a game-changing technology in multiple industries, giving robots and automated systems comparable image identification and processing abilities to human operators. Several applications have been explored by researchers in recent years, bringing truly “smart” visual functionalities to automated processes.

Several challenges still exist with the technology, but with recent advances in deep learning, machine learning, and neural networks, noteworthy progress has been made. Limitations exist with software currently, which must possess the ability to enable quick and precise real-time 3D detection and enhance the accuracy and efficiency of automated decision-making.

This innovative field of automation and software engineering is likely to become a more central part of future industrial processes, helping realize the full potential of Industry 4.0 and several related disciplines.

Continue reading: Applications of Machine Vision in Robotics

References and Further Reading

Optecks (2016) What is 3D Machine Vision? [online] optecks.com. Available at: https://www.optecks.com/Portal/index.php/knowledge-center/3d-machine-vision-root/3d-machine-1

Stemmer Imaging (2022) 3D machine vision – technical basics and challenges [online] stemmer-imaging.com. Available at: https://www.stemmer-imaging.com/en/knowledge-base/3d-machine-vision/

Vision Systems Design (2013) Explore the Fundamentals of Machine Vision: Part I [online] vision-systems.com. Available at: https://www.vision-systems.com/cameras-accessories/article/16736053/explore-the-fundamentals-of-machine-vision-part-i

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Reginald Davey

Written by

Reginald Davey

Reg Davey is a freelance copywriter and editor based in Nottingham in the United Kingdom. Writing for AZoNetwork represents the coming together of various interests and fields he has been interested and involved in over the years, including Microbiology, Biomedical Sciences, and Environmental Science.


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