Summary of Uc-nerf: Uncertainty-aware Conditional Neural Radiance Fields From Endoscopic Sparse Views, by Jiaxin Guo et al.
UC-NeRF: Uncertainty-aware Conditional Neural Radiance Fields from Endoscopic Sparse Views
by Jiaxin Guo, Jiangliu Wang, Ruofeng Wei, Di Kang, Qi Dou, Yun-hui Liu
First submitted to arxiv on: 4 Sep 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 Novel View Synthesis is a crucial technique for reconstructing surgical scenes, enabling better understanding, planning, and decision-making. While Neural Radiance Field (NeRF) has achieved impressive results, its direct application to surgical scenes faces two challenges: sparse views and photometric inconsistencies. To tackle these issues, this paper proposes uncertainty-aware conditional NeRF (UC-NeRF), which incorporates multi-view uncertainty estimation to condition the neural radiance field for modeling photometric inconsistencies adaptively. The approach first builds a consistency learner using multi-view stereo network to establish geometric correspondence from sparse views and generate uncertainty estimation and feature priors. In neural rendering, UC-NeRF designs a base-adaptive NeRF network to exploit uncertainty estimation for handling photometric inconsistencies. Additionally, an uncertainty-guided geometry distillation is employed to enhance geometry learning. Experimental results on SCARED and Hamlyn datasets demonstrate superior performance in rendering appearance and geometry, outperforming current state-of-the-art approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Surgical scenes are crucial for understanding internal anatomical structures during minimally invasive procedures. A new technique called Novel View Synthesis helps with this by reconstructing scenes from different views. But right now, the best approach, Neural Radiance Field (NeRF), doesn’t work well when used directly on surgical scenes because of two big problems: there are only a few views to look at, and those views don’t match up well. To solve these issues, scientists came up with a new idea called uncertainty-aware conditional NeRF (UC-NeRF). It uses special tools to figure out how unsure the model is about what it’s seeing, and then adjusts its predictions based on that uncertainty. This helps the model deal with the tricky inconsistencies between views. The results of this approach are very good, beating other current methods in reconstructing surgical scenes. |
Keywords
» Artificial intelligence » Distillation
