SAS-LoD: Selective Attribute Sharing for Efficient Level-of-Detail Gaussian Splatting

High-fidelity view synthesis using 3D Gaussian Splatting (3DGS) delivers exceptional visual realism with interactive performance. However, rendering large-scale scenes with millions of Gaussian primitives incurs substantial memory usage and computational overhead, often leading to out-of-memory failures and increased rendering latency on resource-constrained devices. Level-of-detail (LoD) techniques alleviate these bottlenecks, yet existing designs face a trade-off: optimizing separate Gaussian sets inflates the resident memory footprint, whereas sharing a single set causes noticeable quality degradation. In this paper, we overcome this trade-off by observing that scale and opacity, accounting for only 4 of 59 parameters, primarily adapt Gaussian footprints across LoD granularities, while all remaining attributes can be shared without quality loss. Building on these insights, we present an efficient 3DGS framework with Selective Attribute Sharing LoD (SAS-LoD). SAS-LoD maintains a single base Gaussian set with compact level-wise residual offsets for scale and opacity, enabling high-fidelity per-level adaptation with minimal resident memory overhead. Coupled with codebook compression and on-demand decoding, SAS-LoD substantially reduces peak memory footprint and improves rendering throughput with high visual fidelity, enabling real-time deployment even on mobile devices.