SpotlessSplats: Ignoring Distractors in 3D Gaussian Splatting
- URL: http://arxiv.org/abs/2406.20055v1
- Date: Fri, 28 Jun 2024 17:07:11 GMT
- Title: SpotlessSplats: Ignoring Distractors in 3D Gaussian Splatting
- Authors: Sara Sabour, Lily Goli, George Kopanas, Mark Matthews, Dmitry Lagun, Leonidas Guibas, Alec Jacobson, David J. Fleet, Andrea Tagliasacchi,
- Abstract summary: 3D Gaussian Splatting (3DGS) is a promising technique for 3D reconstruction, offering efficient training and rendering speeds.
Current methods require highly controlled environments to meet the inter-view consistency assumption of 3DGS.
We present SpotlessSplats, an approach that leverages pre-trained and general-purpose features coupled with robust optimization to effectively ignore transient distractors.
- Score: 44.42317312908314
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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