Summary of Td-nerf: Novel Truncated Depth Prior For Joint Camera Pose and Neural Radiance Field Optimization, by Zhen Tan et al.
TD-NeRF: Novel Truncated Depth Prior for Joint Camera Pose and Neural Radiance Field Optimization
by Zhen Tan, Zongtan Zhou, Yangbing Ge, Zi Wang, Xieyuanli Chen, Dewen Hu
First submitted to arxiv on: 11 May 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper proposes Truncated Depth NeRF (TD-NeRF), a novel approach that enables training Neural Radiance Fields (NeRF) from unknown camera poses. The existing method introduces monocular depth priors to jointly optimize camera poses and NeRF, but neglects the impact of inherent noise. TD-NeRF jointly optimizes learnable parameters of the radiance field and camera poses, utilizing monocular depth priors through three key advancements: a novel depth-based ray sampling strategy, a coarse-to-fine training strategy, and a robust inter-frame point constraint. The method is evaluated on three datasets, demonstrating superior performance in joint optimization and generating more accurate depth geometry. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary TD-NeRF is a new way to train Neural Radiance Fields (NeRF) from unknown camera poses. Right now, cameras need to know their exact position to work well with NeRF. This limits the use of NeRF for tasks like 3D reconstruction and SLAM. The current method tries to fix this by adding information about depth. However, it doesn’t account for the noise in that depth information. TD-NeRF fixes this by proposing a new way to sample rays based on depth, using coarse-to-fine training to refine depth geometry, and improving point constraints to make the method more robust. The results show that TD-NeRF works better than current methods. |
Keywords
» Artificial intelligence » Optimization
