Summary of Osn: Infinite Representations Of Dynamic 3d Scenes From Monocular Videos, by Ziyang Song et al.
OSN: Infinite Representations of Dynamic 3D Scenes from Monocular Videos
by Ziyang Song, Jinxi Li, Bo Yang
First submitted to arxiv on: 8 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Graphics (cs.GR); Machine Learning (cs.LG); Robotics (cs.RO)
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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 OSN framework aims to learn all plausible 3D scene configurations that match a given monocular RGB video, rather than just inferring a single most likely solution. This is achieved through the introduction of an object scale network and joint optimization module, which enables the sampling of multiple faithful 3D scene configurations. The approach surpasses baselines in dynamic novel view synthesis on both synthetic and real-world datasets, with a notable advantage in learning fine-grained 3D scene geometry. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand scenes from videos by figuring out all possible 3D versions of what we see. Right now, most methods just pick one version that looks best, but this can be wrong. The new method called OSN tries to find all the right versions, not just one. It works by looking at how big each object in the scene is and using that information to create lots of possible 3D scenes. This helps it get more details about the scene than other methods do. |
Keywords
* Artificial intelligence * Optimization
