Summary of Vocabulary-free 3d Instance Segmentation with Vision and Language Assistant, by Guofeng Mei and Luigi Riz and Yiming Wang and Fabio Poiesi
Vocabulary-Free 3D Instance Segmentation with Vision and Language Assistant
by Guofeng Mei, Luigi Riz, Yiming Wang, Fabio Poiesi
First submitted to arxiv on: 20 Aug 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 proposes a novel approach to 3D instance segmentation without relying on prior vocabulary or semantic categories. The method leverages a large vision-language assistant and an open-vocabulary 2D instance segmenter to discover and ground semantic categories in posed images, which are then used to form 3D instance masks. A novel superpoint merging strategy is introduced via spectral clustering, accounting for mask coherence and semantic coherence estimated from 2D object instance masks. The method outperforms existing methods in both vocabulary-free and open-vocabulary settings on ScanNet200 and Replica datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, the paper helps computers identify objects in 3D scenes without being told what to look for. It’s like asking a person to find all the objects in a room, not just specific ones. The method uses a combination of computer vision and language processing techniques to discover object categories from scratch. This is an important step towards enabling computers to understand and interact with the world more effectively. |
Keywords
» Artificial intelligence » Instance segmentation » Mask » Spectral clustering
