StitchGS: Towards Seamless and Lightweight Large-Scale 3D Gaussian Splatting

While 3D Gaussian Splatting enables real-time rendering of large-scale scenes, its explicit representation leads to near-linear growth in storage requirements as scene scale expands. Furthermore, existing block-based strategies often suffer from geometric discontinuities and storage redundancy. To address these limitations, we present StitchGS, a high-fidelity and lightweight reconstruction scheme tailored for city-scale environments. To mitigate boundary artifacts caused by physical segmentation, we design a Stochastic Interwoven Stitching mechanism. This technique utilizes Oriented Bounding Boxes to define soft transition zones and employs a confidence-driven competition strategy to achieve smooth sub-pixel fusion of primitives within overlapping regions. To alleviate high storage costs, we further introduce a Spectral-Aware Adaptive Compression strategy. By analyzing the energy spectrum distribution of Spherical Harmonics, this method adaptively prunes redundant high-frequency parameters in diffuse regions. Moreover, it incorporates Quantization-Aware Fine-Tuning to balance storage efficiency with visual fidelity. Experiments demonstrate that StitchGS achieves 1.7×–4.0× storage reduction across our benchmarks while maintaining rendering quality competitive with state-of-the-art methods, enabling efficient deployment of large-scale scenes.