The Future of 3D
TLDRThe video introduces a groundbreaking graphics technique called Gaussian Splatting, which revolutionizes rendering high-fidelity images at rapid speeds. It explains the process involving capturing images, creating a point cloud, and transforming these into a matrix of Gaussians that can be trained to produce detailed images. The technique is compared to photogrammetry but is more direct and efficient, requiring significant VRAM due to the millions of Gaussians used. The video also discusses community research and optimization challenges, highlighting the potential for AI compatibility and future developments in 3D graphics.
Takeaways
- 🎥 The presentation introduces a novel 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 concept of 3D Gaussian distributions.
- 🌟 The technique involves taking multiple images from different angles, estimating a point cloud, and then transforming these points into Gaussians.
- 🖼️ A key step in the process is 'rization,' which projects the Gaussians into a 2D space, sorts them by depth, and blends them together to create an image.
- 📈 The training phase of Gaussian Splatting adjusts the values of the Gaussians to produce images that closely resemble the original, similar to training a neural network but with a much faster process due to zero layers.
- 🌐 The script discusses the challenges of implementing Gaussian Splatting, such as the sorting bottleneck and the need for significant VRAM due to the millions of Gaussians required.
- 🔄 The community has been working on various viewer implementations, with some optimizations and solutions to the sorting problem, such as CPU counting sort and GPU radix sort.
- 🔧 A notable project mentioned is the Unity Gaussian Splatting, which, despite initial skepticism, turned out to be highly effective with AMD parallel radix sort and various optimizations.
- 🌐 The script also mentions the development of a custom library 'gplat' that combines Unity optimizations with WebAssembly and CPU counting sort for better performance in web applications.
- 🚀 The future of 3D graphics is highlighted as being in flux, with new research emerging in both Gaussian Splatting and traditional 3D modeling, presenting opportunities for innovation and advancement in the field.
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 existing graphics pipelines?
-GAN splatting is different from existing graphics pipelines because it doesn't rely on traditional methods like ray tracing, path tracing, or diffusion. Instead, it uses a collection of Gaussian distributions 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.
How are Gaussians used in GAN splatting?
-In GAN splatting, Gaussians are used to represent points in a point cloud with associated colors and alpha values. These Gaussians are then combined into a large matrix to represent the entire scene.
What is the process of 'rization' in GAN splatting?
-Rization is the process of converting the Gaussians into a 2D image. It involves projecting the Gaussians into the image plane, sorting them by depth, and then blending their contributions to each pixel.
What is the purpose of training the Gaussians in GAN splatting?
-The purpose of training the Gaussians is to adjust their values so that they produce images that closely resemble the original images. This process is similar to training a neural network but with a much faster computation due to the absence of layers.
What are some challenges faced in implementing GAN splatting?
-One of the main challenges in implementing GAN splatting is the sorting bottleneck. Every frame requires sorting millions of Gaussians, which can be very computationally expensive and lead to low frame rates.
How has the community contributed to the development of GAN splatting?
-The community has contributed to GAN splatting by creating various viewer implementations and optimizations. Notable examples include a Unity implementation using AMD parallel radix sort and a web-based library that combines CPU counting sort with web assembly for better performance.
What is the potential impact of GAN splatting on the field of 3D graphics?
-GAN splatting has the potential to significantly impact 3D graphics by providing an AI-compatible method for generating high-quality visuals from images or volumetric data. It could lead to advancements in compression, animation, generative modeling, and language grounding.
What are some future directions for research in GAN splatting?
-Future research directions in GAN splatting may include improving compression techniques, developing animations, enhancing generative modeling capabilities, and establishing language grounding for more interactive and controllable graphics generation.
How does GAN splatting compare to traditional 3D mesh generation methods?
-GAN splatting is easier for AI to generate compared to traditional 3D mesh generation methods like marching cubes. It offers a new approach that could potentially produce more usable and higher quality meshes, although recent research is still in the experimental phase.
Outlines
🎥 Introduction to Gaussian Splatting
The paragraph introduces the concept of Gaussian Splatting, a novel rendering technique that enables high-fidelity and rapid visualization. It differentiates itself from traditional graphics pipelines and is capable of rendering scenes at 144 FPS. The explanation begins with the reference to the original research paper and outlines the four-step process of Gaussian Splatting, including image capture from various angles, point cloud estimation, transformation of points into Gaussians, and the final rasterization into an image. The paragraph also touches on the training process of Gaussians, which is akin to neural network training but without layers, and the optimization techniques that allow for faster processing. The discussion concludes with a comparison to traditional rasterization and the potential impact of this technology on the future of graphics.
🤖 Advancements and Community Research in Gaussian Splatting
This paragraph delves into the recent developments and community-driven research in Gaussian Splatting. It highlights the challenges of the technique, such as the sorting bottleneck that affects frame rates, and the solutions proposed by the community, including parallel GPU and CPU sorting methods. The paragraph specifically mentions the Unity Gaussian Splatting project, which, despite initial skepticism, proved to be highly effective through optimizations and the use of AMD parallel reduced instruction set computing (PRIME). The speaker also shares their own contribution by creating a library, gpLAT, which combines Unity optimizations with web assembly and CPU counting sort to make Gaussian Splatting more accessible and practical. The paragraph concludes with a discussion on the broader context of 3D modeling and the potential for AI compatibility in the future of this technology.
Mindmap
Keywords
💡Gaussian
💡Splatting
💡Real-time Radiance Field Rendering
💡Structure from Motion
💡Point Cloud
💡Rasterization
💡Training
💡Automated Densification and Pruning
💡WebAssembly
💡GPlat
💡Dream Gan Mini
Highlights
Gaussian splatting is a novel rendering technique for high fidelity and fast image generation.
It represents a significant departure from traditional graphics pipelines.
The technique can render scenes at an impressive 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 represented as a Gaussian, which includes a distribution and color information.
These Gaussians are organized into a large matrix, which represents the scene data.
The process involves rization, projecting the Gaussians into 2D and sorting them by depth to create an image.
Gaussians are trained to produce images resembling the original, similar to neural network training but with zero layers for speed.
The training process includes automated densification and pruning for optimization.
Gaussian splatting is a very new technique, akin to the invention of traditional rasterization.
It differs from photogrammetry as it is a rization technique, not requiring ray tracing or path tracing.
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 has made significant optimizations for better performance.
There have been impressive demos created with Gaussian splatting, showcasing its potential.
A new library, gpLat, combines Unity optimizations with WebAssembly and CPU counting sort for web usability.
The gpLat library has gained popularity and will be further developed, particularly for machine learning demos.
The future of 3D graphics is very exciting with Gaussian splatting and traditional 3D techniques both undergoing significant innovation.