Gaussian Splatting is a computational framework designed to transform sparse sets of two-dimensional photographs into photorealistic, interactive three-dimensional scene representations. The system functions as a reconstruction tool and rendering engine, enabling the conversion of image data into volumetric models that support novel view synthesis.
The project represents scenes as a collection of anisotropic three-dimensional Gaussians, which store position, opacity, color, and covariance data. It distinguishes itself through a differentiable tile-based rasterization process that projects these primitives into image space, combined with adaptive density control that dynamically splits or prunes primitives to maintain high-fidelity detail. View-dependent lighting is managed through spherical harmonics, allowing color information to shift based on the camera angle.
The framework utilizes stochastic gradient descent to iteratively refine scene geometry and appearance by minimizing the difference between rendered outputs and ground truth images. This approach supports the development of digital models for spatial analysis and research in computer vision, while enabling real-time rendering of complex environments.
