3D Machine Vision vs. 2D Machine Vision

This article will cover how 3D vision functions and why you should use it in your application.

22:06 28 June 2022

One of the fascinating new technologies in robotics is 3D machine vision. Most organizations are turning to 3D vision because it provides several advantages over traditional 2D, such as versatility, improved accuracy, and speed. This article will cover how 3D vision functions and why you should use it in your application.

 

The two-dimensional perspective

2D machine vision technologies have been in use for several decades, which attests to their value. Even though it has some deep flaws, 2D machine vision is a fantastic tool for many applications. Only flat images are visible to 2D cameras, indicating that you can't examine and measure objects as they are or as expected.

 

It also implies that your inspection software must use complex heuristics and algorithms to presume an object's appearance based on a single picture. Frequently, these assumptions fail, resulting in inaccuracies in your applications.

 

Because 2D vision creates the target object from its reflected light, differences in lighting in the visual field caused by alterations in artificial lighting or surroundings can reduce accuracy. In the factory ambient shadowing, too much and too little light can negatively impact the edges' visibility and features visible in the 2D plane.

 

 For instance, bright or dark surfaces will not appear vividly, resulting in a lack of detail. The lack of contrast is an issue. Mistakes due to target object motion in the Z-plane convey a further limitation because 2D machine vision does not handle height data. It will not be an issue for imaging accuracy if the objects are always positioned on a correctly flat surface at an accurate focal length from the sensor.

 

The brilliance of three dimensions

3D machine vision systems are resistant to the external factors that affect two-dimension systems. This is because they can accurately capture additional third dimension information. The mentioned aspects of lighting, distance to the object, and contrast are no longer concerns. 3D machine vision technologies now have more capability to maintain production line bandwidth requirements.

 

Due to this considerable capability, 3D machine vision applies to several tasks such as,

 

• Digitization and scanning of objects.
• Bin selection for packing, placement, and assembly.
• Verification using 3D CAD models and quality assurance
• Surface tracking and robot assistance
• Measurement of volume, thickness, and height
• Space and dimensioning administration
• Determination of assembly or surface flaws

 

Most people still consider random bin picking substantiation for machine vision systems. The robot must select one part and determine the best way to grasp it.

 

However, it must also account for size differences and different measurements. For this to happen, the robot must observe the part correctly. It must account for varying scene dynamics such as reflections, natural noise, and missing data if they are reflective, shiny, light-absorbing, or dark. You must repeat the process in an accurate and timely manner. 3D machine vision thrives here since it's the only method to process the data with high accuracy and in real-time, irrespective of an item's location in the bin.

 

We are delighted to provide you with this summary of the main differences between 3D and 2D. Nevertheless, you will need to conduct additional research to realize your vision-guided system's ability fully.