RTX ON – The NVIDIA TURING GPU

Burgess, J. Peter

doi:10.1109/hotchips.2019.8875651

Cited by 30 publications

(17 citation statements)

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“…In order to better exploit the computation power provided by the GPU [5], the tests have been run with single-precision floating-point types: np.float32 for PyWavelets, tf.float32 for WaveTF and TF-Wavelets, and pypwt compiled to use 32-bit floats.…”

Section: Performance Resultsmentioning

confidence: 99%

WaveTF: A Fast 2D Wavelet Transform for Machine Learning in Keras

Versaci

2021

Pattern Recognition. ICPR International Workshops and Challenges

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The wavelet transform is a powerful tool for performing multiscale analysis and it is a key subroutine in countless applications, from image processing to astronomy. Recently, it has extended its range of users to include the ever growing machine learning community. For a wavelet library to be efficiently adopted in this context, it needs to provide transformations which can be integrated seamlessly in already existing machine learning workflows and neural networks, being able to leverage the same libraries and run on the same hardware (e.g., CPU vs GPU) as the rest of the machine learning pipeline, without impacting training and evaluation performance. In this paper we present WaveTF, a wavelet library available as a Keras layer, which leverages TensorFlow to exploit GPU parallelism and can be used to enrich already existing machine learning workflows. To demonstrate its efficiency we compare its raw performance against other alternative libraries and finally measure the overhead it causes to the learning process when it is integrated in an already existing Convolutional Neural Network.

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Section: Performance Resultsmentioning

confidence: 99%

WaveTF: A Fast 2D Wavelet Transform for Machine Learning in Keras

Versaci

2021

Pattern Recognition. ICPR International Workshops and Challenges

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“…If required, both, the used deep learning network and the path tracing core map naturally to parallel hardware like GPUs, which has been shown to achieve real-time performance for raytracing [44,45] and denoising [46]. Recent developments like Nvidia's RTX technology [47] opens up further avenues to exploit deep neural networks deeper in image generation algorithms.…”

Section: Discussionmentioning

confidence: 99%

Deep radiance caching: Convolutional autoencoders deeper in ray tracing

Jiang

Kainz

2021

Computers & Graphics

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Rendering realistic images with global illumination is a computationally demanding task and often requires dedicated hardware for feasible runtime. Recent research uses Deep Neural Networks to predict indirect lighting on image level, but such methods are commonly limited to diffuse materials and require training on each scene. We present Deep Radiance Caching (DRC), an efficient variant of Radiance Caching utilizing Convolutional Autoencoders for rendering global illumination. DRC employs a denoising neural network with Radiance Caching to support a wide range of material types, without the requirement of offline pre-computation or training for each scene. This offers high performance CPU rendering for maximum accessibility. Our method has been evaluated on interior scenes, and is able to produce high-quality images within 180 seconds on a single CPU.

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“…For a long time, Real-Time Ray Tracing has been considered a far to reach dream where physically-based rendering would be calculated fast enough so that we would be able to interact with the scene and perceive the changes in real-time. Now that Nvidia RTX technology (Burgess, 2020) is available, the dream has become feasible more than ever before. Typical production renderers deal with offline image synthesis by using a large number of samples per pixel to render the best-looking image possible.…”

Section: Introduction 1motivationmentioning

confidence: 99%

Lift: An Educational Interactive Stochastic Ray Tracing Framework with AI-Accelerated Denoiser

Soares¹,

Pereira²

2021

CSRN

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Real-time physically based rendering has long been looked at as the holy grail in Computer Graphics. With the introduction of Nvidia RTX-enabled GPUs family, light transport simulations under real-time constraint started to look like a reality. This paper presents Lift, an educational framework written in C++ that explores the RTX hardware pipeline by using the low-level Vulkan API and its Ray Tracing extension, recently made available by Khronos Group. Furthermore, to accomplish low variance rendered images, we integrated the AI-based denoiser available from the Nvidia ́s OptiX framework. Lift’s development arose primarily in the context of the graduate 3D Programming course taught at Instituto Superior Técnico and Master Theses focused on Real-Time Ray Trac- ing and provides the foundations for laboratory assignments and projects development. The platform aims to make easier students to learn and to develop, by programming the shaders of the RT pipeline, their physically-based ren- dering approaches and to compare them with the built-in progressive unidirectional and bidirectional path tracers. The GUI allows a user to specify camera settings and navigation speed, to select the input scene as well as the rendering method, to define the number of samples per pixel and the path length as well as to denoise the generated image either every frame or just the final frame. Statistics related with the timings, image resolution and total number of accumulated samples are provided too. Such platform will teach that nowadays physically-accurate images can be rendered in real-time under different lighting conditions and how well a denoiser can reconstruct images rendered with just one sample per pixel.

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RTX ON – The NVIDIA TURING GPU

Cited by 30 publications

References 0 publications

WaveTF: A Fast 2D Wavelet Transform for Machine Learning in Keras

WaveTF: A Fast 2D Wavelet Transform for Machine Learning in Keras

Deep radiance caching: Convolutional autoencoders deeper in ray tracing

Lift: An Educational Interactive Stochastic Ray Tracing Framework with AI-Accelerated Denoiser

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