3D Gaussian Splatting (3DGS) has recently advanced radiance field reconstruction by offering superior capabilities for novel view synthesis and real-time rendering speed. However, its strategy of blending optimization and adaptive density control might lead to sub-optimal results; it can sometimes yield noisy geometry and blurry artifacts due to prioritizing optimizing large Gaussians at the cost of adequately densifying smaller ones. To address this, we introduce AtomGS, consisting of Atomized Proliferation and Geometry-Guided Optimization. The Atomized Proliferation constrains ellipsoid Gaussians of various sizes into more uniform-sized Atom Gaussians. The strategy enhances the representation of areas with fine features by placing greater emphasis on densification in accordance with scene details. In addition, we proposed a Geometry-Guided Optimization approach that incorporates an Edge-Aware Normal Loss. This optimization method effectively smooths flat surfaces while preserving intricate details. Our evaluation shows that AtomGS outperforms existing state-of-the-art methods in rendering quality. Additionally, it achieves competitive accuracy in geometry reconstruction and offers a significant improvement in training speed over other SDF-based methods.
3D高斯喷溅（3DGS）最近通过提供卓越的新视角合成能力和实时渲染速度，推进了辐射场重建技术。然而，其融合优化和自适应密度控制的策略可能导致次优结果；有时由于优先优化大的高斯而牺牲了适当增密小高斯，可能产生噪声几何和模糊的伪影。为了解决这个问题，我们引入了AtomGS，包括原子化扩增和几何引导优化。原子化扩增将不同大小的椭球高斯限制为更统一大小的原子高斯。这种策略通过在场景细节方面强调密度增加，增强了细微特征区域的表示。此外，我们提出了一种几何引导优化方法，其中包含了边缘感知的法线损失。这种优化方法有效地平滑了平坦表面，同时保留了复杂的细节。我们的评估显示，AtomGS在渲染质量上超过了现有的最先进方法。此外，它在几何重建的准确性上具有竞争力，并且相比其他基于SDF的方法在训练速度上有显著提高。