Time-of-flight camera characterization with functional modeling for synthetic scene generation

Azure Depth Platform team

Staying at the forefront of 3D sensing technology development requires continuous investment and innovation. Microsoft’s paper : “Time-of-flight camera characterization with functional modeling for synthetic scene generation”, which was recently published in “Optics express” is a clear demonstration of the depth (pun intended) we go to at Microsoft, to make sure partners and customers get the best 3D sensing capabilities possible – click here to read.

In this manuscript, we design, describe, and present a functional model of Time-of-Flight (ToF) cameras. The model can be used to generate randomized scenes that incorporate depth scenarios with various objects at various depths with varied orientations and illumination intensity. It also includes ToF artifacts such as Signal Noise, Crosstalk and Multipath. Our work can be used to generate as many images as needed for neural network (NN) training and testing.

For the tech geeks out there – enjoy the read!

For those who are not experts in this technology, but know exactly what machine automation can do to your operational cost, efficiency and quality :  when you choose solutions based on Microsoft depth sensing technology, not only you get a market leading technology, but also the brain power of some of the most talented optics physicists and engineers in the world, to constantly work on improving your machines automation using 3D sensing & AI.

Kudos to Sergio Ortiz Egea, Mukhil Azhagan and Augustine Cha for the publication !


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