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
(\href{https://rongliu-leo.github.io/AtomGS/}{this https URL}).

AtomGS 通过原子化增殖和几何导向优化策略，解决了 3D 高斯扩散合成在优化大型高斯函数时疏忽小型高斯的问题，提高了对细节区域的表达能力，并通过边缘感知正常损失优化方法实现了对平坦表面的平滑处理，从而在图像渲染质量、几何重建准确性以及训练速度方面超越了现有的最先进方法。

AtomGS: 高保真光辐射场的原子高斯喷射

AtomGS: Atomizing Gaussian Splatting for High-Fidelity Radiance Field

We present MVSGaussian, a new generalizable 3D Gaussian representation
approach derived from Multi-View Stereo (MVS) that can efficiently reconstruct
unseen scenes. Specifically, 1) we leverage MVS to encode geometry-aware
Gaussian representations and decode them into Gaussian parameters. 2) To
further enhance performance, we propose a hybrid Gaussian rendering that
integrates an efficient volume rendering design for novel view synthesis. 3) To
support fast fine-tuning for specific scenes, we introduce a multi-view
geometric consistent aggregation strategy to effectively aggregate the point
clouds generated by the generalizable model, serving as the initialization for
per-scene optimization. Compared with previous generalizable NeRF-based
methods, which typically require minutes of fine-tuning and seconds of
rendering per image, MVSGaussian achieves real-time rendering with better
synthesis quality for each scene. Compared with the vanilla 3D-GS, MVSGaussian
achieves better view synthesis with less training computational cost. Extensive
experiments on DTU, Real Forward-facing, NeRF Synthetic, and Tanks and Temples
datasets validate that MVSGaussian attains state-of-the-art performance with
convincing generalizability, real-time rendering speed, and fast per-scene
optimization.

MVSGaussian 是一种新的通用化的三维高斯表示方法，通过多视点立体视觉 (MVS) 有效重建未见过的场景，具有实时渲染速度和良好的综合质量。