Revolutionizing Ray Tracing with DLSS 3.5: AI-Powered Ray Reconstruction

Summary

Ed's presentation delves into the advancements in DLSS technology, focusing on Ray Reconstruction—an AI model enhancing real-time ray tracing in games by unifying denoising and super resolution processes. He explains the benefits, challenges, and integration of this technology, showcasing its ability to produce high-quality, noise-free, and alias-free images, even when upscaling. The talk highlights the AI's superior performance in dynamic lighting scenarios and its potential to approximate ground truth quality in visuals.

Takeaways

• 😀 Ray Reconstruction is an AI model designed to enhance image quality in real-time ray tracing games by combining denoising and super resolution processes.
• 🚀 The introduction of DLSS 3, alongside the Ada architecture, has significantly improved gaming performance, with AI models generating or reconstructing most of the pixels on screen for higher frame rates.
• 🔍 Ray Reconstruction addresses the challenge of integrating upscaling into the ray tracing reconstruction pipeline, which can be complex due to the need for accurate viewport jitter offset.
• 🛠️ The technology removes the need for separate denoisers, which can lead to performance improvements in games where denoising is computationally expensive.
• 🎮 Ray Reconstruction has been successfully implemented in several games, providing better image quality and performance, as demonstrated in Cyberpunk 2077.
• 🌟 The model is trained to deal with a wide range of input signals, from simple to advanced ray tracing, and is agnostic to the type of signal it processes.
• 🔧 The input to Ray Reconstruction must be random and not follow a fixed pattern, such as checkerboard rendering, to avoid confusing the model.
• 📈 Ray Reconstruction works best when the signal quality is high before denoising, suggesting that optimizing ray tracing parameters is still important for achieving good results.
• 📊 The model is sensitive to spatial and temporal reuse from ray tracing, which can cause correlation issues, and thus these aspects may need to be randomized for better performance.
• 🛑 Integration of Ray Reconstruction requires it to be placed before any screen space filters, such as depth of field or motion blur, to prevent the model from being unable to handle smeared noise.
• 🔄 Ray Reconstruction is an ongoing area of development, with potential for further improvements and adjustments in training and application to handle artifacts and other challenges.

Q & A

What is Ray Reconstruction and how does it improve image quality in real-time ray tracing games?

Ray Reconstruction is an AI model specifically trained to enhance the image quality in real-time ray tracing games. It unifies the reconstruction process, including denoising and super resolution, to provide a smoother, noise-free, and temporally stable image output.

How does the introduction of DLSS 3 impact the gaming experience?

DLSS 3, introduced alongside the Ada architecture, combines frame generation and super resolution, resulting in seven out of eight pixels on the screen being generated or reconstructed by AI models. This allows for significant performance breakthroughs, enabling gamers to enjoy ray tracing games at 120 FPS on current hardware.

What is the typical reconstruction pipeline for a ray tracing game today?

The typical pipeline involves the game engine rendering geometry buffers, textures, and materials, followed by rays being shot from these buffers. From this, noisy radiance buffers containing reflections, AO, and other traits are obtained. Denoisers are then applied to these buffers, each designed for specific signal types, and the results are combined into an alias-free image. Finally, a temporal anti-aliasing (TAA) filter removes aliasing, resulting in a smooth, noise-free image.

Why is integrating an upscaler into the ray tracing reconstruction pipeline not straightforward?

Integrating an upscaler into the ray tracing reconstruction pipeline is complex because upscalers require accurate viewport jitter offset to function correctly. However, denoisers, which are temporal and spatial filters, often remove this offset, causing the upscaler to fail in accurately reconstructing the high-resolution image.

How does Ray Reconstruction change the traditional reconstruction pipeline?

Ray Reconstruction simplifies the pipeline by removing the need for separate denoisers. Instead, it composites all noisy color buffers together without denoising them first. The Ray Reconstruction model then takes this low-resolution, aliased, and noisy input and provides a high-resolution, alias-free, and smooth output.

What are the benefits of using Ray Reconstruction over traditional denoising methods?

Ray Reconstruction offers several benefits, including improved image quality with better handling of challenging noise regions, maintaining detail in areas like neon signs, and providing a more perceptually accurate output closer to ground truth. It also removes the need for multiple denoisers, potentially offering performance benefits.

How does Ray Reconstruction handle the dynamic nature of signals in real-time ray tracing?

Ray Reconstruction is designed to handle highly dynamic signals, such as moving shadows, light sources, and objects, by using AI models that have been trained to manage these complexities. It can provide more responsive and stable results compared to handcrafted denoisers.

What are the technical requirements for integrating Ray Reconstruction into a product?

To integrate Ray Reconstruction, the application or engine should support the input buffers required by Ray Reconstruction, such as the input color buffer, motion vectors, depth, and viewport. Additionally, geometry buffers like normal, albedo, and specular hit distance are needed, with some being optional depending on the app's needs.

Why is it important for the input signal to Ray Reconstruction to be random?

A random input signal is crucial because Ray Reconstruction is trained to handle pixel-sized noise. Patterns like checkerboard rendering or screen percentage options for ray tracing, which involve stretching lower resolution signals, can confuse the model and lead to suboptimal results.

How does Ray Reconstruction address the challenge of achieving ground truth level quality in real-time ray tracing?

Ray Reconstruction, through its AI model, gets closer to the perceptual feel of ground truth quality by training with high-quality data that has correct lighting and detail. It can produce more detailed lighting and geometry, resulting in a more grounded and visually plausible output.

What are some of the common issues that developers might face when integrating Ray Reconstruction into their games?

Developers might need to adjust their renderer to work better with Ray Reconstruction, ensure that screen space filters like depth of field or motion blur are integrated before the reconstruction process, and modify kernels for effects like subsurface scattering to be more stochastic to avoid confusing the model.