An Efficient 3D Gaussian Splatting Pipeline: HDR Integration, Fast Acquisition, and No-Reference Quality Assessment

Recent advances in novel view synthesis have transformed 3D reconstruction, making image-based acquisition more accessible and efficient while preserving high visual quality. Among these methods, 3D Gaussian Splatting (3DGS) stands out for combining state-of-the-art rendering quality with real-time performance. However, limited attention has been devoted to complete reconstruction pipelines that preserve the efficiency goals of these methods from acquisition to evaluation. This thesis proposes an efficient end-to-end workflow for 3DGS-based reconstruction. First, High Dynamic Range (HDR) imaging is integrated into the pipeline to better capture luminance variations and preserve details in challenging lighting conditions. A dedicated HDR dataset was acquired, the impact of tone mapping on reconstruction quality was analyzed, and a consistent multi-view tone mapping strategy was introduced to ensure coherence across viewpoints. Second, a rapid multi-camera acquisition system based on three synchronized cameras was designed to accelerate data collection while maintaining reliable multi-view coverage. The system was validated in cultural heritage and biomedical case studies. Finally, the thesis investigates no-reference quality assessment methods for 3DGS reconstructions. A user study involving 32 participants enabled the development of a perceptually inspired metric capable of estimating reconstruction quality without ground truth data. Overall, the proposed modular pipeline jointly optimizes acquisition, reconstruction, and evaluation for practical real-world applications.