Summary of Nerfdeformer: Nerf Transformation From a Single View Via 3d Scene Flows, by Zhenggang Tang et al.
NeRFDeformer: NeRF Transformation from a Single View via 3D Scene Flows
by Zhenggang Tang, Zhongzheng Ren, Xiaoming Zhao, Bowen Wen, Jonathan Tremblay, Stan Birchfield, Alexander Schwing
First submitted to arxiv on: 15 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 The proposed method for modifying a NeRF representation involves defining the transformation as a weighted linear blending of rigid transformations of 3D anchor points on the scene’s surface. This requires identifying anchor points using a novel correspondence algorithm that first matches RGB-based pairs, then leverages multi-view information and 3D reprojection to filter false positives in two steps. The algorithm is tested on a new dataset containing 113 synthetic scenes leveraging 47 3D assets, outperforming NeRF editing methods and diffusion-based methods. The paper also explores different methods for filtering correspondences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a way to change the shape of an object in a virtual scene by looking at one version of the scene from many angles. They used “anchor points” on the surface of the scene to figure out how each part moved. This allowed them to transform the original scene into the new version, which is useful for tasks like editing 3D models or simulating real-world scenarios. |
Keywords
* Artificial intelligence * Diffusion
