Summary of 3dgs-enhancer: Enhancing Unbounded 3d Gaussian Splatting with View-consistent 2d Diffusion Priors, by Xi Liu et al.
3DGS-Enhancer: Enhancing Unbounded 3D Gaussian Splatting with View-consistent 2D Diffusion Priors
by Xi Liu, Chaoyi Zhou, Siyu Huang
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The novel-view synthesis paper presents 3DGS-Enhancer, a novel pipeline that enhances the representation quality of 3D Gaussian splatting (3DGS) representations. The pipeline addresses the challenging 3D view consistency problem by reformulating it as achieving temporal consistency within a video generation process. It restores view-consistent latent features of rendered novel views and integrates them with input views through a spatial-temporal decoder, resulting in superior reconstruction performance and high-fidelity rendering results compared to state-of-the-art methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper’s goal is to improve 3D-view synthesis by creating more realistic images. They created a new way to make the process better using video information. This helps when there are not many input views, which can lead to poor results. The method is tested on big datasets and shows good results. |
Keywords
» Artificial intelligence » Decoder
