![]() ![]() When rays are cast through a scene (think of them as laser beams), the brunt of the work is figuring out if they hit something or not. ![]() To do so, we can leverage RT Cores, which accelerate Bounding Volume Hierarchy (BVH) traversal and ray/triangle intersection testing (ray casting) functions. Now that we have reduced the number of rays required to obtain a clean image, we can accelerate the processing of these rays. This time-consuming process can be interrupted early through the use of a trained Deep Neural Network (DNN) that can interpret and smooth out the speckled result at a very early stage, massively cutting down on render time. All these rays create a speckle effect that converges to a clean image by adding the results of millions or billions of rays. ![]() Tensor Cores powering the AI denoiser cut down on the number of ray calculations needed to provide a clean image, significantly reducing render times.Īs a render progresses, each ray cast delivers its lighting energy to the finished image. Tensor Cores are dedicated to accelerating Artificial Intelligence calculations, providing a massive performance boost with deep learning applications such as OptiX AI-accelerated denoiser, which is already shipping in many popular applications like Autodesk Arnold, Chaos Group V-Ray, The Foundry’s Modo and much more. Image courtesy of Dušan Ković (rendered with Autodesk Arnold) Reduced Render Times with Turing Tensor Cores and RT Cores ![]()
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