Prune Wisely, Reconstruct Sharply: Compact 3D Gaussian Splatting via Adaptive Pruning and Difference-of-Gaussian Primitives

• URL: http://arxiv.org/abs/2602.24136v1
• Date: Fri, 27 Feb 2026 16:12:58 GMT
• Title: Prune Wisely, Reconstruct Sharply: Compact 3D Gaussian Splatting via Adaptive Pruning and Difference-of-Gaussian Primitives
• Authors: Haoran Wang, Guoxi Huang, Fan Zhang, David Bull, Nantheera Anantrasirichai,
• Abstract summary: 3D Gaussian Splatting (3DGS) has enabled real-time rendering with photorealistic quality.<n>3DGS often requires a large number of primitives to achieve high fidelity.<n>We propose an efficient, integrated reconstruction-aware pruning strategy that determines pruning timing and refining intervals.<n>We also introduce a 3D Difference-of-Gaussians primitive that jointly models both positive and negative densities in a single primitive.
• Score: 14.295266671241004
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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