3D Gaussian Splatting! - Computerphile

Summary

This video script explores the advancements in 3D scene rendering with a focus on Gaussian Splats, an alternative to Neural Radiance Fields (NeRF). It highlights the speed and efficiency of Gaussian Splats, which use a series of 3D Gaussian distributions to represent scenes, allowing for rapid rendering at 100 FPS. The script contrasts this with NeRF's slower, neural network-based approach. It also discusses the process of optimizing Gaussians using techniques like gradient descent and cell-like division for better scene representation. The script concludes with a demonstration of importing Gaussians into Unity, showcasing their potential for real-time rendering and interactive applications.

Takeaways

• 📈 The script discusses an improvement in rendering quality, achieving a significant speed increase when moving objects in a scene.
• 🖥️ It mentions the limitations of older technology, such as the need to wait for rendering when the camera moves, compared to near-instant rendering with newer methods.
• 🚀 The potential for achieving 100 FPS with the latest computer hardware is highlighted, emphasizing the real-time capabilities of the discussed technology.
• 🌐 The script introduces a comparison between traditional neural rendering techniques like NeRF and newer methods like Gaussian Splats, which are more akin to traditional graphics rendering.
• 🎨 Gaussian Splats are described as reconstructing 3D scenes from RGB images using neural networks, creating a series of points that represent the scene.
• 🌳 The process of using Gaussian Splats involves starting with a point cloud and refining it with Gaussian distributions that represent different parts of the scene.
• 🔍 The script explains how Gaussian Splats use rasterization for rendering, which is faster than ray tracing or ray marching used in NeRF, allowing for real-time rendering capabilities.
• 🌐 The limitations of NeRF with unconstrained views and the need for complete scene capture are contrasted with the flexibility of Gaussian Splats.
• 🔧 The script touches on the optimization of Gaussian Splats through techniques like gradient descent, cloning, and division to better fit the scene.
• 🎮 Finally, the potential for using Gaussian Splats in game development and other interactive environments like Unity is showcased, highlighting the ease of manipulation and integration.

Q & A

What is the main difference between the visual quality before and after increasing the quality in the video?

The main difference is that after increasing the quality, the visuals appear much nicer and the rendering is almost instant, even on a not-so-powerful computer, which was not possible with the previous Nerf technology where a 5-second wait was required for rendering a new image when the camera moved.

What is Gaussian Splatting and how does it differ from Nerf?

Gaussian Splatting is a technique that reconstructs a 3D scene from a series of RGB images using a neural network. It differs from Nerf in that it represents the scene as a series of points, where each point is not a single spec but a small Gaussian distribution. This allows for faster rendering and more traditional graphics techniques.

How does the rendering process in Gaussian Splatting compare to that of Nerf?

In Gaussian Splatting, the rendering process is much faster than Nerf. It uses rasterization, a technique that has been around for decades, to quickly paint the Gaussians onto the pixels of the screen. This is in contrast to Nerf, which uses ray marching or ray tracing, a more time-consuming process for real-time rendering.

What is a Gaussian in the context of Gaussian Splatting?

In Gaussian Splatting, a Gaussian is a mathematical function that represents a point in the scene as a small ellipsoid. It is characterized by a bright center that fades out towards the edges, and it can be thought of as a 'clever circle' that can stretch and change to accurately represent the scene.

How does the optimization of Gaussians work in the scene?

The optimization of Gaussians involves adjusting their size, opacity, and position to better fit the reference images. This is done using gradient descent, where the Gaussians are iteratively adjusted to minimize the difference between the rendered scene and the actual images. Additionally, Gaussians can split or duplicate to avoid overfitting or underfitting the scene.

What is the advantage of using Gaussian Splatting over Nerf when it comes to rendering new views of a scene?

Gaussian Splatting has the advantage of being able to render new views of a scene much faster than Nerf. Since the scene is represented by physical Gaussians rather than a neural network, it can quickly determine which Gaussians are visible from a new viewpoint and render them without the need for retraining or querying along rays.

How does the speaker demonstrate the flexibility of Gaussians compared to Nerf in the Unity example?

The speaker demonstrates the flexibility of Gaussians by showing an example in Unity where the Gaussians representing a Christmas tree can be easily moved within the scene. This is in contrast to Nerf, where moving an object would require retraining the neural network, which is a much more time-consuming process.

What is the significance of the depth buffer in the rendering process of Gaussian Splatting?

The depth buffer in Gaussian Splatting is used to prevent the rendering of unnecessary Gaussians that are occluded by ones in front. It helps in determining which Gaussians need to be rendered by checking the depth of each Gaussian relative to the camera, thus improving the efficiency of the rendering process.

How does Gaussian Splatting handle transparency and blending in the rendering process?

Gaussian Splatting handles transparency and blending by using alpha blending, a common technique in graphics. This allows for the proper rendering of Gaussians that have different levels of opacity, ensuring that the final image accurately represents the scene's appearance from the given viewpoint.

What is the potential issue with Gaussian Splatting when capturing scenes that are not fully represented in the training data?

The potential issue with Gaussian Splatting when capturing scenes is that areas not captured in the training data will not be rendered, resulting in black or blank areas in the final image. This is similar to the issue faced with Nerf, emphasizing the importance of capturing comprehensive and detailed scenes for accurate rendering.