AtomGS: Atomizing Gaussian Splatting for High-Fidelity Radiance Field


Rong Liu (University of Southern California), Rui Xu (USC Institute for Creative Technologies, University of Southern California), Yue Hu (University of Southern California), Meida Chen (University of Southern California), Andrew Feng (Institute for Creative Technologies, University of Southern California)
The 35th British Machine Vision Conference
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Abstract

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/).

Citation

@inproceedings{Liu_2024_BMVC,
author    = {Rong Liu and Rui Xu and Yue Hu and Meida Chen and Andrew Feng},
title     = {AtomGS: Atomizing Gaussian Splatting for High-Fidelity Radiance Field},
booktitle = {35th British Machine Vision Conference 2024, {BMVC} 2024, Glasgow, UK, November 25-28, 2024},
publisher = {BMVA},
year      = {2024},
url       = {https://papers.bmvc2024.org/0577.pdf}
}


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