Summary of Contrastive Gaussian Clustering: Weakly Supervised 3d Scene Segmentation, by Myrna C. Silva et al.
Contrastive Gaussian Clustering: Weakly Supervised 3D Scene Segmentation
by Myrna C. Silva, Mahtab Dahaghin, Matteo Toso, Alessio Del Bue
First submitted to arxiv on: 19 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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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 introduces Contrastive Gaussian Clustering, a novel approach for generating segmentation masks from any viewpoint and enabling 3D scene segmentation. Building on recent works in novel-view synthesis, the authors train a model that includes a segmentation feature vector for each Gaussian. These vectors are used for 3D scene segmentation by clustering Gaussians according to their features, and for generating 2D segmentation masks by projecting Gaussians on a plane and blending over their features. The method combines contrastive learning and spatial regularization, allowing it to be trained on inconsistent 2D segmentation masks and still learn to generate consistent masks across all views. The resulting model achieves an improved IoU accuracy of +8% over the state of the art. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to segment scenes from different angles and in three dimensions. It uses a combination of old ideas, like cloud computing and color blending, to make accurate images and masks. The authors also figured out how to train their model using inconsistent data, which is cool because it means they can use real-world data that might not be perfect. Overall, the method works really well and could be used in lots of different areas, like robotics or video games. |
Keywords
* Artificial intelligence * Clustering * Regularization
