Improving Densification in 3D Gaussian Splatting for High-Fidelity Rendering

• URL: http://arxiv.org/abs/2508.12313v1
• Date: Sun, 17 Aug 2025 10:13:21 GMT
• Title: Improving Densification in 3D Gaussian Splatting for High-Fidelity Rendering
• Authors: Xiaobin Deng, Changyu Diao, Min Li, Ruohan Yu, Duanqing Xu,
• Abstract summary: We present a comprehensive improvement to the densification pipeline of 3DGS.<n>Specifically, we propose an Edge-Aware Score to effectively select candidate Gaussians for splitting.<n>We also introduce a Long-Axis Split strategy that reduces geometric distortions introduced by clone and split operations.
• Score: 3.6379656024631215
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although 3D Gaussian Splatting (3DGS) has achieved impressive performance in real-time rendering, its densification strategy often results in suboptimal reconstruction quality. In this work, we present a comprehensive improvement to the densification pipeline of 3DGS from three perspectives: when to densify, how to densify, and how to mitigate overfitting. Specifically, we propose an Edge-Aware Score to effectively select candidate Gaussians for splitting. We further introduce a Long-Axis Split strategy that reduces geometric distortions introduced by clone and split operations. To address overfitting, we design a set of techniques, including Recovery-Aware Pruning, Multi-step Update, and Growth Control. Our method enhances rendering fidelity without introducing additional training or inference overhead, achieving state-of-the-art performance with fewer Gaussians.

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