Developing perception systems is not an easy task. We are talking about systems that need to understand what they see in the real world and react accordingly. But, How do they see the world? How do you teach a machine what the real world is and interpret it?
New-generation systems hungry for accurate sensor-specific data
Modern perception systems use all kinds of new-generation sensors to see the world like radar, lidar, and of course, optical cameras that are the ones that most faithfully mimic the human eye, which coupled with the brain, form the most advanced perception system that exists to date. On the other hand, to implement the perception system “understanding” they use deep neural networks trained for different purposes, like object detection, object segmentation, or depth estimation.
No matter what the problem is, neural networks need data, lots and lots of data. But not any data, it has to be as varied as possible to avoid bias in the system and other domain shift problems.
Sensor-specific synthetic data to accelerate autonomous systems deployment
When you train deep neural networks with synthetic data you have to make sure that they will be able to perform when facing the real world and understand it as well as the synthetic data. How well your network generalizes real-world images from synthetic images, is key to your system’s success.
For that, you need to faithfully simulate the behavior of real cameras when generating synthetic images.
Anyverse™, the hyperspectral synthetic data platform that introduces deep sensor and ISP sensor simulation
The engine uses an accurate light transport model and provides a physics description of lights, cameras, and materials. This allows for a very detailed simulation of the amount of light that is reaching the camera sensor.
Equally important is the simulation of the camera sensor itself, i.e. how the light coming from the scene is converted into the final color image. Different academic papers demonstrate that a machine learning model, based on deep neural networks, trained on a synthetic dataset generated considering camera sensor effects, performs in general better than if the effects are not present. You can check these papers on the subject .
Don’t miss the next chapter
In this series, we will introduce Anyverse camera sensor simulation, what you need to implement it, what parameters and knobs you can have under your control, what are their effects on the final images, and of course what are the benefits of having sensor simulation as part of your perception system development process.
We hope you enjoy this new series of original content and don’t forget to come back to our blog next week to discover the second chapter around the camera sensor pipeline!
Read chapter 2 now>>>
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.
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