用全图监督重建大规模场景的精细表面，兼顾效率与细节。
3D Gaussian Splatting for Fine-Detailed Surface Reconstruction in Large-Scale Scene
- 分阶段构建粗略模型，再按图像块分割优化，提升大场景处理效率。
- 在GauU-Scene V2数据集上优于现有方法，实现高保真视觉效果和精准表面重建。
- 适合无人机航拍、自动驾驶等需高精度三维重建的场景应用。
3D高斯点阵在表面重建方面取得显著进展，但将其扩展至大规模场景仍面临计算开销大、户外环境动态外观复杂等挑战，限制了其在航测与自动驾驶中的应用。本文提出一种新方法，利用全尺寸图像监督实现大规模场景的精细表面重建。首先采用粗到精策略高效构建初始模型，随后通过自适应场景分割与子场景细化；引入解耦外观模型捕捉全局外观变化，以及瞬态遮罩模型抑制移动物体干扰；最后拓展多视角约束，并加入单视角正则化以改善无纹理区域。实验基于公开数据集GauU-Scene V2（无人机拍摄）进行，结果表明，本方法在视觉保真度与表面精度上均超越现有基于NeRF和高斯的方法。开源代码将发布于GitHub。
原文摘要 · Abstract (English)
Recent developments in 3D Gaussian Splatting have made significant advances in surface reconstruction. However, scaling these methods to large-scale scenes remains challenging due to high computational demands and the complex dynamic appearances typical of outdoor environments. These challenges hinder the application in aerial surveying and autonomous driving. This paper proposes a novel solution to reconstruct large-scale surfaces with fine details, supervised by full-sized images. Firstly, we introduce a coarse-to-fine strategy to reconstruct a coarse model efficiently, followed by adaptive scene partitioning and sub-scene refining from image segments. Additionally, we integrate a decoupling appearance model to capture global appearance variations and a transient mask model to mitigate interference from moving objects. Finally, we expand the multi-view constraint and introduce a single-view regularization for texture-less areas. Our experiments were conducted on the publicly available dataset GauU-Scene V2, which was captured using unmanned aerial vehicles. To the best of our knowledge, our method outperforms existing NeRF-based and Gaussian-based methods, achieving high-fidelity visual results and accurate surface from full-size image optimization. Open-source code will be available on GitHub.
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