Developing an autonomous system with sensor-specific synthetic data — Wrapping up

If there is only one thing we would like you to take away from this camera sensor simulation insight series is that to do an accurate sensor simulation you need the light coming from the scene characterized as an electromagnetic wave. It’s the only way to simulate the physical phenomena happening in the optical system and the sensor. No light, no simulation, is that simple.

And no simulation means your synthetic data may not be that useful to train and test deep learning-based perception systems. It may be more difficult for neural networks to generalize to real-world images. Because at the end of the day, that is every perception system’s goal: understand the real world and interpret it.

That’s exactly what Anyverse™ wants to accomplish. And we do it by recreating the real world you need in realistic, varied datasets using our own physically-based render engine and the sensor simulation pipeline explained in the previous chapters of this insight series.

Imagine you can generate as many images as you need for your training and testing, automatically annotated with accurate ground truth data, faithfully simulating the exact cameras you rig in your real system. How powerful is that?

Say that you want to try new cameras on your system and the impact this change may have on the overall system. Doing this without sensor simulation means: rigging the new cameras on the real system and taking it out to collect data, curate it and annotate it, train the system and see what happens. What’s your flexibility to tweak camera parameters? Zero.

With Anyverse™ sensor simulation along with the data generation platform, you can run as many experiments as you want, changing camera parameters in every iteration to see how they affect neural network performance. Don’t let the data generation pipeline be an independent process in your system development.

Sensor simulation goes beyond data

But sensor simulation goes beyond data. If you are developing your own sensors you can make design decisions without the complexity and cost of prototyping on silicon.

You can develop the best “eye-brain” combination for your perception problem without leaving the lab.

It allows efficient agile practices from classic software development applied to software 2.0 development, a term coined by Andrej Karpathy in 2017 describing a paradigm change when developing deep learning-based systems[1].

We hope you enjoyed and learned reading this insight series based on our Camera Sensor Simulation eBook.

Stay tuned for more content from us. In the meantime follow us on our social networks and don’t hesitate to contact us if you have any questions about sensor simulation or anything else.

References

[1] Software 2.0, Andrej Karpathy. Medium article. https://karpathy.medium.com/software-2-0-a64152b37c35

About Anyverse™

Anyverse™ helps you continuously improve your deep learning perception models to reduce your system’s time to market applying new software 2.0 processes. Our synthetic data production platform allows us to provide high-fidelity accurate and balanced datasets. Along with a data-driven iterative process, we can help you reach the required model performance.

With Anyverse™, you can accurately simulate any camera sensor and help you decide which one will perform better with your perception system. No more complex and expensive experiments with real devices, thanks to our state-of-the-art photometric pipeline.

Need to know more?

Visit our website, anyverse.ai anytime, or our Linkedin, Instagram, and Twitter profiles.