Summary of Prunerf: Segment-centric Dataset Pruning Via 3d Spatial Consistency, by Yeonsung Jung et al.
PruNeRF: Segment-Centric Dataset Pruning via 3D Spatial Consistency
by Yeonsung Jung, Heecheol Yun, Joonhyung Park, Jin-Hwa Kim, Eunho Yang
First submitted to arxiv on: 2 Jun 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 In this paper, the researchers propose a novel framework called PruNeRF to improve the performance of Neural Radiance Fields (NeRF) by removing distractions from the training images. NeRF is a powerful tool for learning 3D scenes, but it can be vulnerable to unexpected objects in the training data. The authors develop a segment-centric approach that uses 3D spatial consistency to identify and remove distractions, such as pedestrians or birds. They also introduce new metrics for measuring pixel-wise distraction and assess the effectiveness of their approach on benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary NeRF is a technology that can learn 3D scenes from images. But sometimes it gets confused by unexpected objects in the pictures. This paper shows how to fix this problem by removing those distractions. The researchers created a new way to do this called PruNeRF, which looks at the images in 3D and finds the distracting parts. They also came up with new ways to measure how well NeRF is doing. By using these techniques, they were able to make NeRF work better when there are unexpected objects in the pictures. |
