How 3D profiling gives computer vision an extra dimension
Using computer vision in smart cities allows them to operate seamlessly. At the same time, considering how computer vision should be the catalyst for digitization in several sectors in the future, it is natural to expect technology to evolve over time. As a result, computer vision has undergone several evolutions to cover more fields of application in recent years.
3D profiling, a 3D vision strategy that enables the capture of three-dimensional images for a variety of purposes, can provide the aforementioned improvements in computer vision. Here’s how the inclusion of 3D profiling improves computer vision:
Greater dimensional accuracy
Despite the advancements, computer vision tools could still benefit from an extra dose of clarity and precision when capturing images or videos that will then be used in facial recognition systems or other technology-based systems. ‘IA. 3D profiling uses laser profiling to create 3D images. This allows images or videos to be captured with the highest degree of precision, with depth resolution often measurable in micrometers (μm). As a result, the input data into 3D computer vision systems contains almost no variation. This data can be put to good use by machine learning and AI tools.
The improved accuracy also allows researchers to get a better idea of the depth or height measurement. For example, it is possible to answer some questions about the height of certain objects in an image, as well as information about the objects around it.
Better data verification
Most of the time, AI, computer vision and other technologies are mainly used for the verification of data and images in various systems and devices. As many of us know, humans can make mistakes during the verification process. 3D profiling makes the verification process more reliable due to higher precision in the input data as shown above. As a result, the data verification process is performed, with better output results as well, if 3D profiling is used to deepen computer vision applications.
AI systems are generally less error prone than other non-AI machines as well as humans. If the algorithms are trained with a large number of diverse and culturally inclusive data sets, computer vision based operations can be performed smoothly. The depth provided by 3D profiling allows computer vision-based applications to have accurate information about the distance of an object to the lens as well as other objects in its environment.
The introduction of 3D profiling improves areas such as robotics and AR, which also use computer vision. Technologies like 3D profiling are here to stay and improve technologies like computer vision in the future.