InfoLoD: Training-Data-Free Hierarchical 3D Gaussian Splatting via Fisher-Guided View Synthesis

Real-time visualization of massive 3D virtual environments, such as city-scale digital twins and immersive simulations, remains a core challenge in computer graphics. While 3D Gaussian Splatting (3DGS) achieves photorealistic rendering at interactive rates, its explicit Gaussian representation incurs substantial GPU memory overhead, limiting deployment on resource-constrained devices. Existing 3DGS Level-of-Detail (LoD) methods are often data-dependent, requiring access to large collections of original training images, and they rarely adapt to dynamic hardware budgets, leading to unstable performance on low-VRAM GPUs. We present InfoLoD, a data-free and hardware-aware framework for hierarchical LoD construction and large-scale 3DGS rendering. InfoLoD introduces a Fisher-guided self-distillation scheme that uses the Fisher Information Matrix (FIM) to select geometrically valid, information-rich pseudo viewpoints, enabling LoD training directly from a pre-trained 3DGS model without any original images. To operate under strict VRAM constraints, we further propose a budget-aware out-of-core streaming system with spatio-temporal working-set management and resource-adaptive LoD scheduling to balance fidelity and throughput. Experiments on challenging outdoor scenes show that InfoLoD enables stable real-time rendering on resource-constrained GPUs, delivering higher efficiency than prior baselines while maintaining competitive visual quality.