MS-GS: Multi-Appearance Sparse-View 3D Gaussian Splatting in the Wild

• URL: http://arxiv.org/abs/2509.15548v4
• Date: Fri, 24 Oct 2025 02:17:58 GMT
• Title: MS-GS: Multi-Appearance Sparse-View 3D Gaussian Splatting in the Wild
• Authors: Deming Li, Kaiwen Jiang, Yutao Tang, Ravi Ramamoorthi, Rama Chellappa, Cheng Peng,
• Abstract summary: In-the-wild photo collections often contain limited volumes of imagery and exhibit multiple appearances.<n>We present MS-GS, a novel framework designed with Multi-appearance capabilities in Sparse-view scenarios using 3DGS.<n>We demonstrate that MS-GS achieves photorealistic renderings under various challenging sparse-view and multi-appearance conditions.
• Score: 47.52462219920857
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In-the-wild photo collections often contain limited volumes of imagery and exhibit multiple appearances, e.g., taken at different times of day or seasons, posing significant challenges to scene reconstruction and novel view synthesis. Although recent adaptations of Neural Radiance Field (NeRF) and 3D Gaussian Splatting (3DGS) have improved in these areas, they tend to oversmooth and are prone to overfitting. In this paper, we present MS-GS, a novel framework designed with Multi-appearance capabilities in Sparse-view scenarios using 3DGS. To address the lack of support due to sparse initializations, our approach is built on the geometric priors elicited from monocular depth estimations. The key lies in extracting and utilizing local semantic regions with a Structure-from-Motion (SfM) points anchored algorithm for reliable alignment and geometry cues. Then, to introduce multi-view constraints, we propose a series of geometry-guided supervision steps at virtual views in pixel and feature levels to encourage 3D consistency and reduce overfitting. We also introduce a dataset and an in-the-wild experiment setting to set up more realistic benchmarks. We demonstrate that MS-GS achieves photorealistic renderings under various challenging sparse-view and multi-appearance conditions, and outperforms existing approaches significantly across different datasets.

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