The emergence of 3D Gaussian Splatting (3DGS) has significantly advanced Novel View Synthesis (NVS) 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) 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.

翻译：3D高斯泼溅（3DGS）的出现通过显式场景表示显著推进了新视角合成（NVS）领域，实现了实时逼真渲染。然而，现有方法在复杂场景中表现出两个关键局限：（1）过重建现象发生在持续存在的大高斯分布无法满足密度控制中的自适应分裂阈值时。这一问题因梯度方向冲突而加剧，阻碍了这些高斯分布的有效分裂；（2）在梯度方向对齐的聚合区域会出现高斯分布过密化，导致冗余组件激增。这种冗余由于不必要的数据保留显著增加了内存开销。我们提出梯度方向感知高斯泼溅（GDAGS）以应对这些挑战。我们的核心创新包括：通过归一化梯度向量范数计算的梯度一致性比率（GCR），可明确区分具有一致性与冲突性梯度方向的高斯分布；以及一种非线性动态加权机制，利用GCR实现梯度方向感知的密度控制。具体而言，GDAGS在分裂操作中优先处理冲突梯度方向的高斯分布以增强几何细节，同时抑制冗余的一致性方向高斯分布。相反，在克隆过程中，GDAGS促进一致性方向高斯分布的致密化以完成结构重建，同时防止冲突方向高斯分布的过度增殖。跨多个真实世界基准的综合评估表明，GDAGS在实现卓越渲染质量的同时，有效缓解了过重建问题，抑制了过密化现象，并构建了紧凑的场景表示。