Summary of Mose: Monocular Semantic Reconstruction Using Nerf-lifted Noisy Priors, by Zhenhua Du et al.
MOSE: Monocular Semantic Reconstruction Using NeRF-Lifted Noisy Priors
by Zhenhua Du, Binbin Xu, Haoyu Zhang, Kai Huo, Shuaifeng Zhi
First submitted to arxiv on: 21 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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 The paper proposes a novel neural field-based approach called MOSE (neural field semantic reconstruction) to reconstruct dense and semantically annotated 3D meshes from monocular images. The method leverages generic class-agnostic segment masks as guidance to promote local consistency of rendered semantics during training, achieving mutual benefits of geometry and semantics. Experiments on the ScanNet dataset show that MOSE outperforms relevant baselines across all metrics for tasks such as 3D semantic segmentation, 2D semantic segmentation, and 3D surface reconstruction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MOSE uses neural fields to lift inferred image-level noisy priors to 3D, producing accurate semantics and geometry in both 3D and 2D space. The approach proposes a novel way to reconstruct 3D scenes from monocular images by leveraging generic class-agnostic segment masks as guidance. |
Keywords
» Artificial intelligence » Semantic segmentation » Semantics
