![]() Intel Open Image Denoise exploits modern instruction sets like Intel SSE4, AVX2, and AVX-512 on CPUs, Intel® Xe Matrix Extensions (Intel® XMX) on Intel GPUs, and tensor cores on NVIDIA GPUs to achieve high denoising performance. It is efficient enough to be suitable not only for offline rendering, but, depending on the hardware used, also for interactive or even real-time ray tracing. It runs on most machines ranging from laptops to workstations and compute nodes in HPC systems. ![]() NVIDIA GPUs with Volta, Turing, Ampere, Ada Lovelace, and Hopper architecturesĪMD GPUs with RDNA2 (Navi 21 only) and RDNA3 (Navi 3x) architectures Intel Xe architecture GPUs, both dedicated and integrated, including Intel® Arc™ A-Series Graphics, Intel® Data Center GPU Flex Series (Xe-HPG microarchitecture), Intel® Data Center GPU Max Series (Xe-HPC), 11th-13th Gen Intel® Core™ processor graphics, and related Intel Pentium® and Celeron® processors (Xe-LP) Intel® 64 architecture compatible CPUs (with at least SSE4.1) Intel Open Image Denoise supports a wide variety of CPUs and GPUs from different vendors: To optimize a filter for a specific renderer, sample count, content type, scene, etc., it is possible to train the model using the included training toolkit and user-provided image datasets. Such buffers are supported by most renderers as arbitrary output variables (AOVs) or can be usually implemented with little effort.Īlthough the library ships with a set of pre-trained filter models, it is not mandatory to use these. The filters can denoise images either using only the noisy color ( beauty) buffer, or, to preserve as much detail as possible, can optionally utilize auxiliary feature buffers as well (e.g. albedo, normal). Thus it is suitable for both preview and final-frame rendering. A simple but flexible C/C++ API ensures that the library can be easily integrated into most existing or new rendering solutions.Īt the heart of the Intel Open Image Denoise library is a collection of efficient deep learning based denoising filters, which were trained to handle a wide range of samples per pixel (spp), from 1 spp to almost fully converged. It filters out the Monte Carlo noise inherent to stochastic ray tracing methods like path tracing, reducing the amount of necessary samples per pixel by even multiple orders of magnitude (depending on the desired closeness to the ground truth). The purpose of Intel Open Image Denoise is to provide an open, high-quality, efficient, and easy-to-use denoising library that allows one to significantly reduce rendering times in ray tracing based rendering applications. Intel Open Image Denoise is part of the Intel® oneAPI Rendering Toolkit and is released under the permissive Apache 2.0 license. Intel Open Image Denoise is an open source library of high-performance, high-quality denoising filters for images rendered with ray tracing. ![]() Hover over the image (or tap on it) to move the slider between the original and denoised versions. Evermotion 15th Anniversary Collection scene rendered with Chaos Corona and denoised with Intel® Open Image Denoise using prefiltered albedo and normal buffers.
0 Comments
Leave a Reply. |