Gradient-Direction-Aware Density Control for 3D Gaussian Splatting

• URL: http://arxiv.org/abs/2508.09239v1
• Date: Tue, 12 Aug 2025 13:12:54 GMT
• Title: Gradient-Direction-Aware Density Control for 3D Gaussian Splatting
• Authors: Zheng Zhou, Yu-Jie Xiong, Chun-Ming Xia, Jia-Chen Zhang, Hong-Jian Zhan,
• Abstract summary: 3D Gaussian Splatting (3DGS) has significantly advanced novel view synthesis through explicit scene representation.<n>Existing approaches manifest two critical limitations in complex scenarios.<n>We present Gradient-Direction-Aware Gaussian Splatting (GDAGS), a gradient-direction-aware adaptive density control framework.
• Score: 7.234328187876289
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: The emergence of 3D Gaussian Splatting (3DGS) has significantly advanced novel view synthesis through explicit scene representation, enabling real-time photorealistic rendering. However, existing approaches manifest two critical limitations in complex scenarios: (1) Over-reconstruction occurs when persistent large Gaussians cannot meet adaptive splitting thresholds during density control. This is exacerbated by conflicting gradient directions that prevent effective splitting of these Gaussians; (2) Over-densification of Gaussians occurs in regions with aligned gradient aggregation, leading to redundant component proliferation. This redundancy significantly increases memory overhead due to unnecessary data retention. We present Gradient-Direction-Aware Gaussian Splatting (GDAGS), a gradient-direction-aware adaptive density control framework to address these challenges. Our key innovations: the gradient coherence ratio (GCR), computed through normalized gradient vector norms, which explicitly discriminates Gaussians with concordant versus conflicting gradient directions; and a nonlinear dynamic weighting mechanism leverages the GCR to enable gradient-direction-aware density control. Specifically, GDAGS prioritizes conflicting-gradient Gaussians during splitting operations to enhance geometric details while suppressing redundant concordant-direction Gaussians. Conversely, in cloning processes, GDAGS promotes concordant-direction Gaussian densification for structural completion while preventing conflicting-direction Gaussian overpopulation. Comprehensive evaluations across diverse real-world benchmarks demonstrate that GDAGS achieves superior rendering quality while effectively mitigating over-reconstruction, suppressing over-densification, and constructing compact scene representations with 50\% reduced memory consumption through optimized Gaussians utilization.

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