Summary of A Nerf-based Color Consistency Method For Remote Sensing Images, by Zongcheng Zuo et al.
A Nerf-Based Color Consistency Method for Remote Sensing Images
by Zongcheng Zuo, Yuanxiang Li, Tongtong Zhang
First submitted to arxiv on: 8 Nov 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 NeRF-based method offers a novel approach to color consistency correction for multi-view images, addressing the issue of varying photometric conditions in acquired images. By weaving together image features using implicit expressions and re-illuminating feature space, this method generates fusion images with improved visual effects and smooth color transitions at edges. The experiment utilizes Superview-1 satellite images and UAV images with large range and time differences, demonstrating excellent results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper solves a problem in combining different images into one. When we take pictures of the same thing from different angles or at different times, the lighting can look very different. This makes it hard to join the pictures together without noticeable seams. The researchers suggest using something called NeRF to fix this issue. They show that their method works well with satellite and drone photos, making the joined images look much better. |
