Summary of Learning Segmented 3d Gaussians Via Efficient Feature Unprojection For Zero-shot Neural Scene Segmentation, by Bin Dou et al.
Learning Segmented 3D Gaussians via Efficient Feature Unprojection for Zero-shot Neural Scene Segmentation
by Bin Dou, Tianyu Zhang, Zhaohui Wang, Yongjia Ma, Zejian Yuan
First submitted to arxiv on: 11 Jan 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 Compact Segmented 3D Gaussians (CoSegGaussians) model addresses the issue of inefficient neural scene segmentation by introducing a Feature Unprojection and Fusion module. This module utilizes high-level features to generate semantic-aware image-based features, which are then combined with spatial information to produce segmentation identities for all Gaussians. The CoSeg Loss function is designed to improve robustness against 3D-inconsistent noises. Experimental results show that CoSegGaussians outperforms baselines on the zero-shot semantic segmentation task by ~10% mIoU. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CoSegGaussians is a new way for computers to understand scenes without being taught what the scene looks like. The problem with current methods is that they take up too much space and aren’t very good at dealing with mistakes. Our solution is a special module that combines information from the image and the 3D scene to make better predictions. We also created a new way to measure how well our model does, called CoSeg Loss. This helps us make sure our model is robust against errors. Our results show that CoSegGaussians is better than other methods at understanding scenes without being taught what they look like. |
Keywords
» Artificial intelligence » Loss function » Semantic segmentation » Zero shot
