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arXiv · 2406.20055

SpotlessSplats: Ignoring Distractors in 3D Gaussian Splatting

Sara Sabour·Lily Goli·George Kopanas·Mark Matthews·Dmitry Lagun·Leonidas Guibas·Alec Jacobson·David J. Fleet·Andrea Tagliasacchi
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Abstract

3D Gaussian Splatting (3DGS) is a promising technique for 3D reconstruction, offering efficient training and rendering speeds, making it suitable for real-time applications.However, current methods require highly controlled environments (no moving people or wind-blown elements, and consistent lighting) to meet the inter-view consistency assumption of 3DGS. This makes reconstruction of real-world captures problematic. We present SpotLessSplats, an approach that leverages pre-trained and general-purpose features coupled with robust optimization to effectively ignore transient distractors. Our method achieves state-of-the-art reconstruction quality both visually and quantitatively, on casual captures. Additional results available at: https://spotlesssplats.github.io

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Sara Sabour, Lily Goli, George Kopanas, Mark Matthews, Dmitry Lagun, Leonidas Guibas, Alec Jacobson, David J. Fleet, Andrea Tagliasacchi. 2024-06-28. SpotlessSplats: Ignoring Distractors in 3D Gaussian Splatting. https://arxiv.org/abs/2406.20055

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