Summary of Spotlesssplats: Ignoring Distractors in 3d Gaussian Splatting, by Sara Sabour et al.
SpotlessSplats: Ignoring Distractors in 3D Gaussian Splatting
by Sara Sabour, Lily Goli, George Kopanas, Mark Matthews, Dmitry Lagun, Leonidas Guibas, Alec Jacobson, David J. Fleet, Andrea Tagliasacchi
First submitted to arxiv on: 28 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper presents SpotLessSplats, a novel approach for 3D reconstruction that addresses the limitations of current methods. The authors introduce 3D Gaussian Splatting (3DGS), which offers efficient training and rendering speeds, making it suitable for real-time applications. However, traditional methods require highly controlled environments to meet the inter-view consistency assumption of 3DGS, rendering them ineffective for capturing real-world scenes. SpotLessSplats leverages pre-trained features and robust optimization to ignore transient distractors and achieve state-of-the-art reconstruction quality both visually and quantitatively on casual captures. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to reconstruct 3D images that can handle real-world scenes, like people moving around or wind blowing. Currently, methods need super-controlled environments to work well, but this approach uses pre-trained features and clever optimization to ignore distracting objects. This makes it possible to get high-quality 3D reconstructions even from casual captures. |
Keywords
» Artificial intelligence » Optimization
