3D Gaussian Splatting (3DGS) has demonstrated strong performance in high-quality novel view synthesis and real-time rendering by representing scenes as dense sets of Gaussians with associated attributes [1]. This representation captures fine geometric detail and view-dependent appearance efficiently at render time, contributing to its practical success. However, trained 3DGS models often contain millions of Gaussians, resulting in substantial storage and memory requirements that limit scalability and deployment on resource-constrained systems.

This project focuses on exploiting geometric relationships among Gaussians (i.e., primitives) to reduce redundancy in the attribute space. The goal is to apply sampling and sparsification techniques to decrease the number of stored primitives while preserving perceptual and structural fidelity. The proposed methodologies will be evaluated against existing compression approaches [2]. The project aims to disseminate the results in highly recognized scientific venues; hence, the applicants should be motivated to do research.

[1] Kerbl, Bernhard, et al. "3d gaussian splatting for real-time radiance field rendering." ACM Trans. Graph. 42.4. 2023.

[2] Bagdasarian, Milena T., et al. "3dgs. zip: A survey on 3d gaussian splatting compression methods." Computer Graphics Forum. Vol. 44. No. 2. 2025.

Voraussetzungen