3D Gaussian Splatting (3DGS) achieves state-of-the-art image quality and real-time performance in novel view synthesis but often suffers from a suboptimal spatial distribution of primitives. This issue stems from cloning-based densification, which propagates Gaussians along existing geometry, limiting exploration and requiring many primitives to adequately cover the scene. We present ConeGS, an image-space-informed densification framework that is independent of existing scene geometry state. ConeGS first creates a fast Instant Neural Graphics Primitives (iNGP) reconstruction as a geometric proxy to estimate per-pixel depth. During the subsequent 3DGS optimization, it identifies high-error pixels and inserts new Gaussians along the corresponding viewing cones at the predicted depth values, initializing their size according to the cone diameter. A pre-activation opacity penalty rapidly removes redundant Gaussians, while a primitive budgeting strategy controls the total number of primitives, either by a fixed budget or by adapting to scene complexity, ensuring high reconstruction quality. Experiments show that ConeGS consistently enhances reconstruction quality and rendering performance across Gaussian budgets, with especially strong gains under tight primitive constraints where efficient placement is crucial.

翻译：3D高斯泼溅（3DGS）在新视角合成中实现了最先进的图像质量和实时性能，但其基元空间分布往往欠佳。该问题源于基于克隆的致密化策略，该策略会沿现有几何结构传播高斯分布，限制了空间探索能力，并需要大量基元才能充分覆盖场景。本文提出ConeGS——一个独立于现有场景几何状态的图像空间感知致密化框架。ConeGS首先创建快速的即时神经图形基元（iNGP）重建作为几何代理来估计逐像素深度。在后续的3DGS优化过程中，系统识别高误差像素，并沿对应视锥在预测深度值处插入新高斯分布，同时根据锥体直径初始化其尺寸。预激活不透明度惩罚机制可快速移除冗余高斯分布，而基元预算策略则通过固定预算或自适应场景复杂度来控制基元总数，从而确保高质量重建。实验表明，ConeGS在不同高斯预算下均能持续提升重建质量和渲染性能，在基元严格受限的场景中（此时高效布局至关重要）表现尤为突出。