Summary of Click-gaussian: Interactive Segmentation to Any 3d Gaussians, by Seokhun Choi et al.
Click-Gaussian: Interactive Segmentation to Any 3D Gaussians
by Seokhun Choi, Hyeonseop Song, Jaechul Kim, Taehyeong Kim, Hoseok Do
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Graphics (cs.GR)
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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 This paper proposes Click-Gaussian, a method for interactive segmentation of 3D scenes that learns distinguishable feature fields of two-level granularity. The approach facilitates segmentation without time-consuming post-processing and is designed to overcome challenges stemming from inconsistently learned feature fields resulting from 2D segmentation obtained independently from a 3D scene. The authors develop Global Feature-guided Learning (GFL) to construct clusters of global feature candidates from noisy 2D segments across the views, smoothing out noises when training the features of 3D Gaussians. Click-Gaussian runs in 10 ms per click, significantly faster than previous methods while maintaining high segmentation accuracy. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Interactive segmentation of 3D scenes is important for real-time manipulation of 3D scenes. The current methods have limitations like requiring post-processing to deal with noisy output and struggling to provide detailed segmentation. This paper proposes a new method called Click-Gaussian that can segment 3D scenes quickly and accurately. |
Keywords
» Artificial intelligence » Stemming
