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arXiv · 2506.13516

Micro-macro Gaussian Splatting with Enhanced Scalability for Unconstrained Scene Reconstruction

Yihui Li·Chengxin Lv·Hongyu Yang·Di Huang
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Abstract

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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Yihui Li, Chengxin Lv, Hongyu Yang, Di Huang. 2025-06-16. Micro-macro Gaussian Splatting with Enhanced Scalability for Unconstrained Scene Reconstruction. https://arxiv.org/abs/2506.13516

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