SpecGS: Frequency–Spatial Support Coupled Gaussian Splatting for Memory-Efficient Large-Scale Scene Reconstruction

Despite the success of 3D Gaussian Splatting (3DGS) in real-time rendering, reconstructing large-scale urban scenes remains challenging. Due to the inherent low-pass nature of Gaussian primitives, capturing high-frequency details (e.g., sharp edges and intricate textures) typically requires excessive primitive densification. Consequently, many existing methods rely on multi-GPU training or scene partitioning to scale reconstruction. We propose SpecGS, a 3DGS framework that improves scalability through frequency-guided Gaussian adaptation. SpecGS estimates Gaussian-level high-frequency evidence from image-space frequency responses and couples it with spatial-support regulation and densification prioritization, enabling representation capacity to be preferentially allocated to regions requiring fine-detail reconstruction. Moreover, to ensure stable optimization at scale, we introduce a three-stage optimization strategy with an Adaptive Convergence Trigger (ACT) that automatically schedules coarse-to-fine resolution progression based on optimization saturation. At the final stage, Differenceof-Gaussian (DoG) supervision further emphasizes high-frequency reconstruction while providing the frequency evidence used for Gaussian adaptation. Experiments on multiple urban datasets demonstrate that SpecGS achieves a favorable quality–efficiency trade-off with significantly fewer primitives and reduced memory consumption, enabling practical city-scale reconstruction on a single GPU.