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 – Anyverse


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!

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