GauStudio is designed for 3D Gaussian Splatting techniques, offering a modular approach for constructing specialized pipelines in 3D scene modeling tasks. The framework consists of four main components: Initialization, Optimization, Enhancement, and Compression. Initialization involves generating initial Gaussians and parameters from the input image and projection function. Optimization refines these Gaussians based on the input image and optional parameters. Enhancement densifies and completes the optimized Gaussians for better representation, while Compression aims to balance representation quality and computational efficiency by removing insignificant Gaussians or compacting parameters.

Various methods are considered for GauStudio, including Fixed Basis Functions, Neural Feature Vectors, Triplane, Hash Grid, and Codebook. These methods offer different approaches to modeling view-dependent appearance and enhancing the representation of specific parts of the scene. The Enhancement stage plays a crucial role in improving the representation, especially for textureless surfaces and convergence speed, by incorporating depth information and novel completion methods.

For surface reconstruction from 3D Gaussians, the GauS module is introduced, which efficiently converts Gaussians into textured meshes. Traditional approaches like Poisson surface reconstruction are discussed, highlighting challenges such as noisy Gaussians and floater-like artifacts. The project also explores the use of mask loss and depth information to improve surface reconstruction quality and remove artifacts in specific regions.