Summary of Semantic Is Enough: Only Semantic Information For Nerf Reconstruction, by Ruibo Wang et al.
Semantic Is Enough: Only Semantic Information For NeRF Reconstruction
by Ruibo Wang, Song Zhang, Ping Huang, Donghai Zhang, Wei Yan
First submitted to arxiv on: 24 Mar 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 paper extends the Semantic Neural Radiance Fields (Semantic-NeRF) model to focus solely on semantic output, removing the RGB output component. By reformulating the model and its training procedure using cross-entropy loss between the model’s semantic output and ground truth semantic images, the authors aim to investigate the impact of this modification on the model’s performance in tasks such as scene understanding, object detection, and segmentation. The results provide valuable insights into rendering scenes with semantic labels and offer avenues for further research. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper takes a well-known 3D modeling technique called Neural Radiance Fields (NeRF) and makes it better by focusing on the meaning of what’s being shown, rather than just the colors. This helps machines understand scenes in a more meaningful way, like detecting objects or understanding what’s happening in a picture. |
Keywords
» Artificial intelligence » Cross entropy » Object detection » Scene understanding
