The importance of ray tracing by Stanford University

The importance of ray tracing by Stanford University Anyverse


The importance of ray tracing by Stanford University

Check out this interesting read by Stanford University on the importance of ray tracing and camera parameters for the generation of synthetic data used in machine learning!

Variations in specific camera parameters and image processing operations impact convolutional neural network (CNN) generalization.

The researchers come to the following conclusion:

The generalization between the ray-traced synthetic imagesand the camera images is encouraging. The performancelevel using synthetic images for the car detection applicationis already adequate to be helpful in advancing all threeobjectives (universal network design, specific camera sim-ulation, co-design of camera and network).

At Anyverse™ we place great focus on camera/sensor parameters for each dataset generated. We can customize each sensor model by using our proprietary unbiased spectral render engine. So if you are looking for a specific lens, we have it and we can tweak camera features and scenario specifics to match your exact model. Give it a try by designing your own dataset!

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.

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