Summary of Desplat: Decomposed Gaussian Splatting For Distractor-free Rendering, by Yihao Wang et al.
DeSplat: Decomposed Gaussian Splatting for Distractor-Free Rendering
by Yihao Wang, Marcus Klasson, Matias Turkulainen, Shuzhe Wang, Juho Kannala, Arno Solin
First submitted to arxiv on: 29 Nov 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 paper proposes DeSplat, a method for fast and accurate novel view synthesis in static 3D environments. The existing methods rely on external semantic information from pre-trained models, introducing additional computational overhead. DeSplat directly separates distractors and static scene elements based on volume rendering of Gaussian primitives. It initializes Gaussians within each camera view to reconstruct the view-specific distractors and model the static 3D scene in alpha compositing stages. This yields an explicit scene separation of static elements and distractors, achieving comparable results to prior distractor-free approaches without sacrificing rendering speed. DeSplat is demonstrated on three benchmark datasets for distractor-free novel view synthesis. The paper’s novelty lies in its ability to directly separate distractors and static scene elements without relying on external semantic information. This approach can potentially be applied to various computer vision tasks, such as scene understanding and image generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Gaussian splatting helps make new views of 3D scenes quickly. But when there are lots of distractions or things in the way, it gets harder. Most methods use extra help from computers to figure out what’s what. This paper suggests a new way called DeSplat that separates the distractions and the real scene without needing extra help. It does this by using special math that helps it understand what’s static (not moving) and what’s not. The results are pretty good, just like other methods that don’t include distractions. This method can be used for things like making fake images or understanding scenes. |
Keywords
» Artificial intelligence » Image generation » Scene understanding
