通过多尺度分解与小波采样，提升复杂场景的3D重建质量与扩展性。
Micro-macro Gaussian Splatting with Enhanced Scalability for Unconstrained Scene Reconstruction
- 将场景分解为全局、精细和内在三部分，实现多尺度建模。
- 在城市大场景中，重建质量优于现有方法，光照变化下仍稳定。
- 适合大规模复杂场景重建，尤其适用于光照多变的开放环境。
从非受限图像集合重建3D场景面临外观差异大等挑战。本文提出可扩展的微-宏小波高斯点渲染（SMW-GS），通过将场景表示分解为全局、精细和内在成分，提升跨尺度重建能力。SMW-GS引入两项创新：微-宏投影，使高斯点能以更高多样性采样多尺度细节；基于小波的采样，利用频域信息优化特征表示，更准确捕捉复杂外观。为实现可扩展性，进一步提出大规模场景优化策略，通过最大化相机视角对高斯点的贡献，智能分配视图至场景分区，在广阔环境中实现一致且高质量重建。大量实验表明，SMW-GS在重建质量与可扩展性上显著优于现有方法，尤其在具有挑战性光照变化的大规模城市环境中表现突出。项目代码已开源：https://github.com/Kidleyh/SMW-GS。
原文摘要 · Abstract (English)
Reconstructing 3D scenes from unconstrained image collections poses significant challenges due to variations in appearance. In this paper, we propose Scalable Micro-macro Wavelet-based Gaussian Splatting (SMW-GS), a novel method that enhances 3D reconstruction across diverse scales by decomposing scene representations into global, refined, and intrinsic components. SMW-GS incorporates the following innovations: Micro-macro Projection, which enables Gaussian points to sample multi-scale details with improved diversity; and Wavelet-based Sampling, which refines feature representations using frequency-domain information to better capture complex scene appearances. To achieve scalability, we further propose a large-scale scene promotion strategy, which optimally assigns camera views to scene partitions by maximizing their contributions to Gaussian points, achieving consistent and high-quality reconstructions even in expansive environments. Extensive experiments demonstrate that SMW-GS significantly outperforms existing methods in both reconstruction quality and scalability, particularly excelling in large-scale urban environments with challenging illumination variations. Project is available at https://github.com/Kidleyh/SMW-GS.
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