3D Gaussian Splatting is revolutionizing the way we render photorealistic scenes in real-time. Originating from a need to render scenes from a minimal set of images, this technique is a breakthrough in the field of computer graphics.
At its heart, 3D Gaussian Splatting is a rasterization technique. Unlike the traditional triangle rasterization, this method uses gaussians characterized by their position, covariance, color, and transparency. These elements combine to form the detailed scenes we see on the screen.
The first step involves using the SfM method to create a 3D point cloud from 2D images. Tools like the COLMAP library are instrumental in this process.
Each point from the SfM data is converted into a gaussian. This conversion is key to the rasterization process.
To achieve high-quality results, the gaussians undergo a training process using Stochastic Gradient Descent. This process includes differentiable gaussian rasterization and steps for adjusting gaussian parameters for better detail and pruning unnecessary elements.
3D Gaussian Splatting's significance lies in its ability to produce high-quality scenes in real-time. This capability opens up a range of possibilities in animation, reflections, modeling, and notably, Embodied AI research. The dense representation of 3D space that this technique offers is particularly valuable in AI applications where understanding and interacting with 3D environments is crucial.
While initially not adapted to production rendering pipelines, adaptations for different platforms are emerging. The primary challenge lies in efficiently sorting millions of gaussians, a task crucial for integrating this technique into various rendering pipelines.
3D Gaussian Splatting stands on the precipice of transforming the graphics world. With its ability to render detailed, lifelike scenes rapidly, it holds the promise of significant advancements in both graphical rendering and AI research.
Created 2023-12-25T17:48:08-08:00 · Edit