If developing and validating autonomous driving systems wasn’t already hard enough… having inaccurate data could make your life even harder.
Tag Archives: Synthetic data
The need for Pixel-accurate, synthetic data for autonomous driving perception, development & validation
Talking about pixel-accurate, synthetic data is talking about safety enhancement and trustworthy data to develop accurate autonomous driving systems.
More complex deep learning models require more complex data
Many deep learning models struggle to see the relationships between objects in a scene, but the machine learning model MIT researchers have developed brings machines one step closer to understanding and interacting with the scene environment, just like humans would do…
Don’t let poor data become your perception system’s kryptonite
Poor data… The most dangerous villain advanced perception systems developers need to face and defeat if they want to develop an accurate deep learning model.
No spectral information, no faithful sensor simulation
Faithful sensor simulation and spectral information… What’s the connection between them? Why is this important? No light, no perception, hence, no light simulation, no sensor simulation, it is that simple.
How Intel enhanced photorealism using machine learning techniques
Enhancing photorealism has obsessed the computer graphics world for years which has allowed us to see numerous papers, experiments, and even tricks to improve it in recent times. Some of them achieved remarkable results, as Intel Labs did with its machine learning project Enhancing Photorealism Enhancement.
Crafting synthetic data to train top-performing in-cabin monitoring systems
DMS, OMS, HMI… We are heading 2022 and there is no doubt that interior sensing applications are rising exponentially, and they will continue to do so in the coming months.
Anyverse™ takeaways from AutoSens Brussels 2021
Last week, we had the pleasure to exhibit at AutoSens 2021. Don’t miss our key takeaways from AutoSens in Brussels!
Tesla bets on the physically correct synthetic data at its AI Day
A couple of weeks ago, Tesla celebrated its AI Day, led by Elon himself along with Tesla’s Head of AI,Andrej Karpathy and other engineers from the software and hardware teams. Their “sole goal” was persuading experts in the field of robotics and artificial intelligence to come work at Tesla. But this event was much more than that and many of us were amazed by how Tesla is solving computer vision problems, and more specifically, how they generate training data for their car’s autopilot system.
How simulating light and sensors help build better perception systems
Developing computer vision 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?