Recent significant advances in 3D scene representation have been driven by 3D Gaussian Splatting (3DGS), which has enabled real-time rendering with photorealistic quality. 3DGS often requires a large number of primitives to achieve high fidelity, leading to redundant representations and high resource consumption, thereby limiting its scalability for complex or large-scale scenes. Consequently, effective pruning strategies and more expressive primitives that can reduce redundancy while preserving visual quality are crucial for practical deployment. We propose an efficient, integrated reconstruction-aware pruning strategy that adaptively determines pruning timing and refining intervals based on reconstruction quality, thus reducing model size while enhancing rendering quality. Moreover, we introduce a 3D Difference-of-Gaussians primitive that jointly models both positive and negative densities in a single primitive, improving the expressiveness of Gaussians under compact configurations. Our method significantly improves model compactness, achieving up to 90\% reduction in Gaussian-count while delivering visual quality that is similar to, or in some cases better than, that produced by state-of-the-art methods. Code will be made publicly available.

翻译：近年来，三维场景表示领域的重要进展由3D高斯泼溅（3DGS）所驱动，该技术已实现具有照片级真实感的实时渲染。3DGS通常需要大量基元以实现高保真度，这导致表示冗余与高资源消耗，从而限制了其在复杂或大规模场景中的可扩展性。因此，能够减少冗余同时保持视觉质量的有效剪枝策略与更具表现力的基元，对于实际部署至关重要。我们提出一种高效、集成的重建感知剪枝策略，该策略根据重建质量自适应确定剪枝时机与优化间隔，从而在提升渲染质量的同时减小模型规模。此外，我们引入一种三维高斯差分基元，可在单个基元内联合建模正负密度，从而提升紧凑配置下高斯基元的表达能力。我们的方法显著提升了模型紧凑性，在实现高斯数量减少高达90%的同时，提供的视觉质量与现有最先进方法相当，甚至在某些情况下更优。代码将公开发布。