Summary of Uncertainty-guided Optimal Transport in Depth Supervised Sparse-view 3d Gaussian, by Wei Sun et al.
Uncertainty-guided Optimal Transport in Depth Supervised Sparse-View 3D Gaussian
by Wei Sun, Qi Zhang, Yanzhao Zhou, Qixiang Ye, Jianbin Jiao, Yuan Li
First submitted to arxiv on: 30 May 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 proposed method, UGOT, improves novel view synthesis from RGB images by introducing a novel supervision technique for 3D Gaussian splatting. The method utilizes depth priors with integrated uncertainty estimates to address localized errors in monocular depth estimation models. This is achieved by incorporating patch-wise optimal transport strategy into traditional L2 loss in depth supervision. UGOT outperforms state-of-the-art methods on the LLFF, DTU, and Blender datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary UGOT helps computers generate new views of objects from a single image, making it useful for applications like augmented reality. The method uses special types of mathematical functions called 3D Gaussians to do this. However, these functions can get confused when trying to work with images that have unclear or uncertain depth information. To fix this problem, UGOT introduces a new way to supervise the depth distribution of 3D Gaussians using prior knowledge about how depth should look in certain situations. |
Keywords
» Artificial intelligence » Depth estimation
