The Future of 3D

HuggingFace
5 Dec 202306:55

TLDRThe video introduces a novel rendering technique called Gaussian Splatting, which promises high-fidelity, real-time rendering at 144 FPS. It differs from traditional graphics pipelines by using a point cloud transformed into a matrix of Gaussians, which are then rendered into images and trained for accuracy. The technique is compared to photogrammetry but is more direct and efficient, requiring significant VRAM. Community implementations have addressed sorting bottlenecks, and the presenter shares their own library, Gplat, which combines optimizations for web use and machine learning applications, showcasing the potential for AI compatibility and innovation in 3D graphics.

Takeaways

  • 🎥 The presentation introduces a new rendering technique called Gaussian Splatting, which is capable of high-fidelity, real-time rendering at 144 FPS.
  • 📜 Gaussian Splatting is fundamentally different from existing Graphics Pipelines and is based on the research paper '3D Gaussian Splatting for Realtime Radiance Field Rendering'.
  • 📸 The process starts by taking multiple photos from different angles and using Structure from Motion to estimate a point cloud.
  • 🤔 Each point in the point cloud is then represented as a Gaussian, which is a 3D distribution that can be skewed and assigned a color and alpha value.
  • 🗂️ These Gaussians are organized into a large matrix, which represents the scene data needed for rendering.
  • 🖼️ The rendering process involves projecting the Gaussians into 2D, sorting them by depth, and blending their contributions to each pixel to create an image.
  • 🏋️ Training is required to adjust the Gaussian values, similar to training a neural network but with a much faster process due to zero layers.
  • 🌳 The training process includes automated densification and pruning, allowing Gaussians to split or be removed based on their performance.
  • 📈 Gaussian Splatting is a very new technique and is considered a reinvention of the Graphics Pipeline, similar to the introduction of traditional rasterization.
  • 💻 Community research has led to various viewer implementations, but they often suffer from low frame rates due to sorting bottlenecks.
  • 🔧 Optimizations and the use of technologies like AMD Parallel Reduce Sword and WebAssembly have made Gaussian Splatting more practical and accessible.

Q & A

  • What is the main topic of the video?

    -The main topic of the video is GAN (Gaussian) splatting, a novel rendering technique for high fidelity and fast graphics.

  • How does GAN splatting differ from traditional graphics pipelines?

    -GAN splatting is different from traditional graphics pipelines because it doesn't rely on ray tracing, path tracing, or diffusion. Instead, it uses a collection of Gaussians to represent and render scenes.

  • What is the significance of the research paper mentioned in the script?

    -The research paper, titled '3D GAN splatting for realtime Radiance field rendering', introduces the concept of using GANs for real-time rendering of high-quality graphics, which is a significant advancement in the field.

  • What are the steps involved in GAN splatting?

    -The steps involved in GAN splatting include: 1) Capturing images from different angles, 2) Estimating a point cloud using structure from motion, 3) Assigning Gaussian distributions (including color and alpha) to each point, 4) Rasterizing the Gaussians into an image from the camera perspective, and 5) Training the Gaussians to produce images that resemble the original ones.

  • How is the training process of Gaussians similar to neural networks?

    -The training process of Gaussians is similar to neural networks in that it involves adjusting values to improve the output. However, it's noted for having 'zero layers', making it significantly faster than training neural networks.

  • What is the challenge with implementing GAN splatting?

    -The main challenge with implementing GAN splatting is the need to sort millions of Gaussians for each frame, which can be very resource-intensive and cause performance bottlenecks, especially on platforms other than CUDA-enabled GPUs.

  • How does the Unity GAN splatting project address the sorting issue?

    -The Unity GAN splatting project addresses the sorting issue by using AMD parallel radix sort, which, combined with various optimizations, allows the project to run efficiently despite being slower than CUDA radix sort.

  • What is the potential of GAN splatting in the context of 3D graphics?

    -GAN splatting has the potential to revolutionize 3D graphics by providing an AI-friendly method for generating high-quality visuals from images or volumetric data, unlike traditional mesh constructions which have remained largely unchanged since the 1980s.

  • What are some of the recent developments in the field of 3D graphics that are relevant to GAN splatting?

    -Recent developments in 3D graphics relevant to GAN splatting include advancements in mesh generation that could potentially integrate with GAN splatting to produce more usable 3D models from raw data.

  • How can the GAN splatting technique be made more accessible to a broader audience?

    -The GAN splatting technique can be made more accessible by implementing solutions like CPU counting sort combined with web optimizations and web assembly, which can improve performance and allow for more user-friendly viewer implementations.

  • What are some of the future directions for research in GAN splatting?

    -Future research directions for GAN splatting include compression, animation, generative modeling, and language grounding, as well as further integration with traditional 3D graphics techniques.

Outlines

00:00

🎥 Introduction to Gaussian Splatting

The paragraph introduces the concept of Gaussian Splatting, a novel rendering technique that offers high-fidelity images at rapid speeds. It contrasts this method with traditional graphics pipelines and highlights its ability to render complex scenes at 144 FPS. The explanation begins with the foundational research paper and outlines the four-step process of Gaussian Splatting, including image capture, point cloud estimation, Gaussian distribution assignment, and image synthesis. The paragraph also touches on the training phase, which optimizes the Gaussian values for accurate image reproduction, and the potential of this technology to revolutionize graphics, comparing its impact to past advancements like traditional rasterization and the introduction of shadows in video games.

05:02

🤖 Advancements and Applications in Gaussian Splatting

This paragraph delves into the ongoing research and development in the field of Gaussian Splatting. It discusses the challenges faced by early community implementations, such as sorting bottlenecks and low frame rates, and how these were overcome with optimizations and different rendering techniques. The paragraph highlights the Unity Gaussian Splatting project and its significant improvements, as well as the creation of a custom web library (gplat) that combines optimizations for web use. It showcases demos that utilize Gaussian Splatting and discusses the broader context of 3D modeling, comparing it to traditional mesh techniques. The paragraph concludes with a forward-looking perspective on the potential integration of AI and the dynamic evolution of 3D technologies.

Mindmap

Keywords

💡Gaussian Splatting

Gaussian Splatting is a novel rendering technique described in the video as a high-fidelity, fast rendering process distinct from traditional graphics pipelines. It involves representing scene elements as 3D Gaussians (distributions that can be skewed, called 'multivars'), which are then rasterized to create an image. This method stands out for its ability to render complex scenes at high frame rates (144 FPS), indicating a significant departure from existing methods like rasterization or ray tracing. The video emphasizes its potential to revolutionize graphics by rendering scenes from millions of these Gaussians.

💡3D Gaussian

A 3D Gaussian, in the context of this video, is a mathematical representation of points in a point cloud, where each point is considered to have a distribution that looks spherical (or ellipsoidal if skewed), which is termed as 'multivar'. This concept is central to Gaussian Splatting, allowing for the representation of complex three-dimensional objects and scenes with a high degree of detail and depth, leveraging the properties of Gaussians for rendering.

💡Point Cloud

A point cloud is a set of data points in space, often used in the context of 3D scanning or photogrammetry. The video describes using structure from motion, an old algorithm, to estimate a point cloud from pictures taken at different angles. This point cloud serves as the foundational data for Gaussian Splatting, where each point is later transformed into a 3D Gaussian for rendering purposes.

💡Rasterization

Rasterization, as mentioned in the video, is the process of converting the Gaussian representations into a 2D image, based on the camera perspective. This involves projecting the 3D Gaussians into 2D, sorting them by depth, and then calculating and blending their contributions for each pixel. The video distinguishes Gaussian Splatting's use of rasterization from traditional methods by its unique approach of handling and rendering Gaussians.

💡Training

In the context of Gaussian Splatting, 'training' refers to adjusting the values of the Gaussians to produce images that match the original scenes from which the point cloud was derived. This process is likened to training a neural network but is described as being incredibly fast due to having 'zero layers'. It involves automated densification and pruning of Gaussians for optimizing detail representation and efficiency.

💡Automated Densification and Pruning

This refers to a process during the training phase of Gaussian Splatting where Gaussians are automatically split into two when they cannot adequately fit detailed parts of the scene, and removed when their significance (alpha value) becomes too low. This process ensures that the representation remains efficient and effective, focusing detail where it's needed and removing unnecessary parts.

💡Photogrammetry

Photogrammetry is a technique for creating 3D models from photographs. The video contrasts Gaussian Splatting with photogrammetry by highlighting that while both involve creating 3D representations from images, Gaussian Splatting uses a distinct rasterization technique for direct image conversion, bypassing the need for traditional 3D model rendering methods like ray tracing or path tracing.

💡Parallel GPU Radix Sort

A sorting algorithm optimized for GPUs, mentioned as a solution to the sorting bottleneck in Gaussian Splatting. Radix sort is used to efficiently sort millions of Gaussians by depth, a critical step for accurate rasterization. The video notes this as a straightforward operation with CUDA on Nvidia platforms, but challenging on others, highlighting the importance of sorting in the rendering process.

💡Unity Gaussian Splatting

This is highlighted in the video as a project that successfully implemented Gaussian Splatting with optimizations to overcome the limitations of sorting Gaussians on platforms other than Nvidia's CUDA. It represents a significant community effort to make Gaussian Splatting more accessible and performant across a wider range of hardware, indicating the collaborative nature of development in this new field.

💡Gradio Component

Gradio is a framework for quickly creating web demos for machine learning models. The video describes a custom Gradio component developed to showcase Gaussian Splatting through an interactive web interface. This development illustrates how Gaussian Splatting can be integrated into machine learning workflows, making it more accessible for demonstration and experimentation purposes.

Highlights

Gaussian splatting is a novel rendering technique for high fidelity and fast image generation.

It is distinct from existing Graphics Pipelines and can render scenes at 144 FPS.

The original research paper is titled '3D Gaussian splatting for realtime Radiance field rendering'.

Gaussian splatting involves taking multiple images from different angles and using structure for motion to estimate a point cloud.

Each point in the point cloud is treated as a 3D Gaussian, with color and alpha assigned to it.

These Gaussians are organized into a large matrix, representing the scene data.

The process involves converting Gaussians into an image by projecting them into 2D, sorting by depth, and blending contributions to pixels.

Gaussians are trained with a method similar to neural network training but with zero layers, making it extremely fast.

The training process uses automated densification and pruning to optimize the Gaussians.

Gaussian splatting is a new concept, akin to the invention of traditional rasterization.

It differs from photogrammetry as it is a rendering technique that doesn't require ray tracing, path tracing, or diffusion.

The technique was not previously feasible due to the need for millions of Gaussians and significant VRAM.

Community research has led to various viewer implementations, though they often struggle with frame rate due to sorting bottlenecks.

Unity Gaussian splatting is a notable project that overcomes some of these challenges using AMD parallel radix sort.

There have been impressive demos created with Gaussian splatting, such as a lining example and an explosion simulation.

Gaussian splatting is not yet practical for a broader audience but has potential for future development.

A new library, gplat, combines Unity optimizations with web assembly and CPU counting sort for better performance.

A custom gradio component was created for gplat, making it easier to showcase Gaussian splatting results.

The 'dream Gan mini' demo allows users to generate Gaussian splatting results from any image.

Gaussian splatting and traditional 3D research are both exciting fields with much potential for innovation and practical application.

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