Summary of Segment Any 3d Object with Language, by Seungjun Lee et al.
Segment Any 3D Object with Language
by Seungjun Lee, Yuyang Zhao, Gim Hee Lee
First submitted to arxiv on: 2 Apr 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 This paper introduces Segment any 3D Object with LanguagE (SOLE), a novel framework for open-vocabulary 3D instance segmentation that leverages multimodal semantics and geometric information. The authors address limitations in earlier works, which rely on annotated base categories or generate class-agnostic masks, by proposing a multimodal fusion network that incorporates linguistic and visual cues. This approach enables SOLE to generate semantic-related masks directly from 3D point clouds, achieving superior performance on ScanNetv2, ScanNet200, and Replica benchmarks. The framework’s versatility is demonstrated through extensive qualitative results, showcasing its ability to accommodate various language instructions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps computers better understand what objects look like in 3D environments by using words. Most previous attempts used simple masks or relied on labeled training data. But this new approach, called SOLE, uses both words and geometric information to create more accurate masks. The result is a system that can segment objects into instances even when shown new, unseen categories. This means SOLE can be trained without knowing all the possible object types in advance. In tests, SOLE outperformed other methods and came close to achieving the same results as if it had been fully supervised. |
Keywords
» Artificial intelligence » Instance segmentation » Semantics » Supervised
