Summary of Hybridgs: Decoupling Transients and Statics with 2d and 3d Gaussian Splatting, by Jingyu Lin et al.
HybridGS: Decoupling Transients and Statics with 2D and 3D Gaussian Splatting
by Jingyu Lin, Jiaqi Gu, Lubin Fan, Bojian Wu, Yujing Lou, Renjie Chen, Ligang Liu, Jieping Ye
First submitted to arxiv on: 5 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 introduces HybridGS, a hybrid representation that leverages 2D Gaussians to model transient objects in 3D Gaussian Splatting (3DGS) scenes. The traditional 3DGS approach is suited for static scenes with multi-view consistency, but transient objects don’t adhere to this assumption. By decomposing the scene based on viewpoint consistency, HybridGS represents transient objects as planar objects from a single view. A novel multi-view regulated supervision method is proposed, which leverages co-visible regions to enhance distinctions between transients and statics. The paper also presents a straightforward training strategy for robust learning and high-quality view synthesis. Experimental results demonstrate state-of-the-art performance in indoor and outdoor scenes, even with distracting elements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to take a picture of a moving object. This is hard because the object changes shape and position quickly. Scientists have developed a way to represent these changing objects using 2D Gaussian curves. They’ve combined this with traditional 3D Gaussian Splatting, which works well for still scenes. The new method is called HybridGS. It can take pictures of moving objects in both indoor and outdoor settings, even when there are distractions like people or trees in the background. This technology could be useful for applications like video games or virtual reality. |
