PhD Defense: From 3D Reconstruction to Usable Scene Representations

3D Gaussian Splatting (3D-GS) has emerged as a powerful representation for reconstructing 3D scenes from posed image collections, enabling high-quality novel view synthesis at real-time rendering rates. However, standard 3D-GS reconstructions remain difficult to use in downstream settings: they are often too large to store efficiently, slower than necessary to render, and limited by the pixel resolution of the input images.This defense presents three methods that make 3D-GS models smaller, faster, and more flexible. The first is a principled post-hoc pruning method, PUP 3D-GS, that scores the sensitivity of each Gaussian and enables aggressive compression, removing 90% of Gaussians while preserving high visual fidelity. The second is a rendering acceleration framework, Speedy-Splat, that corrects an inefficiency in Gaussian localization and integrates pruning directly into training, achieving an average rendering speed-up of 6.71x across real-world scenes. The third is a selective super-resolution framework, SplatSuRe, that generates higher-than-pixel-resolution 3D-GS reconstructions by applying super-resolved supervision only where scene geometry and camera pose indicate that additional high-frequency detail is useful.Together, these contributions advance 3D Gaussian Splatting toward practical downstream use as an efficient, compact, and high-quality 3D scene representation.