明智剪枝，精确重建：通过自适应剪枝和高斯差分原语实现紧凑的3D高斯溅射
Prune Wisely, Reconstruct Sharply: Compact 3D Gaussian Splatting via Adaptive Pruning and Difference-of-Gaussian Primitives
AI总结：
本文提出了一种自适应剪枝策略和高斯差分原语，通过减少高斯数量提升3D高斯溅射的紧凑性与渲染质量。
摘要：
最近在3D场景表示方面的重要进展是由3D高斯溅射（3DGS）驱动的，它使能够以逼真质量实现实时渲染。3DGS通常需要大量的原语来实现高保真度，导致冗余表示和高资源消耗，从而限制了其在复杂或大规模场景中的可扩展性。因此，有效的剪枝策略和更具表现力的原语，这些原语可以在减少冗余的同时保持视觉质量，对于实际部署至关重要。我们提出了一种高效、集成的重建感知剪枝策略，该策略根据重建质量自适应地确定剪枝时机和细化间隔，从而在减少模型大小的同时提高渲染质量。此外，我们引入了一种3D高斯差分原语，该原语在单一原语中联合建模正负密度，提高了在紧凑配置下高斯的表现力。我们的方法显著提高了模型的紧凑性，实现了高达90%的高斯数量减少，同时交付的视觉质量与最先进的方法产生的质量相似，有时甚至更好。代码将公开发布。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
