SparseStreet: Sparse Gaussian Splatting for Real-Time Street Scene Simulation

2026-06-02

AI Summary

This work addresses the high storage overhead and low rendering efficiency of existing 3D Gaussian splatting methods in street-view reconstruction, which stem from their reliance on a large number of Gaussian primitives. To tackle this issue, the authors propose a general-purpose compression framework tailored for street scenes, introducing a hierarchical strategy that exploits the distinct characteristics of dynamic objects and static backgrounds. The framework employs a learnable node pruning mechanism to eliminate low-contribution primitives and applies secondary compression specifically to static regions. Evaluated on the Waymo and nuScenes datasets, the method achieves up to 80% reduction in the number of Gaussians while preserving high-fidelity reconstruction quality, thereby significantly improving both storage efficiency and rendering performance.

Abstract

While 3D Gaussian Splatting has shown promising results in street scene reconstruction, existing methods require massive numbers of Gaussian primitives to capture fine details, leading to prohibitive storage costs and slow rendering speeds. We observe that dynamic objects (e.g., vehicles and pedestrians) demand high-fidelity representations to maintain temporal consistency, while static background regions often contain substantial redundancy. Motivated by this, we propose SparseStreet, a general compression framework specifically designed for street scenes. First, we introduce a node-based learnable pruning strategy that systematically removes low-contributing Gaussian primitives while preserving visually critical regions. Second, after the scene representation stabilizes, we apply background compression, further reducing redundancy in static regions. Our method effectively preserves the geometry and appearance of dynamic objects while significantly reducing the total number of Gaussian primitives. Extensive experiments on the Waymo and nuScenes demonstrate that SparseStreet achieves up to 80% compression ratio with minimal quality degradation, enabling resource-efficient, high-fidelity dynamic scene reconstruction. Project website: https://sparsestreet.github.io/.

Problem

• 3D Gaussian Splatting
• street scene reconstruction
• storage cost
• rendering speed
• redundancy

Innovation

• Sparse Gaussian Splatting
• learnable pruning
• background compression
• dynamic scene reconstruction
• street scene simulation