  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. More
interactive demos can be found in our website
(https://rongliu-leo.github.io/AtomGS/).
