Summary of Beyond Bare Queries: Open-vocabulary Object Grounding with 3d Scene Graph, by Sergey Linok et al.
Beyond Bare Queries: Open-Vocabulary Object Grounding with 3D Scene Graph
by Sergey Linok, Tatiana Zemskova, Svetlana Ladanova, Roman Titkov, Dmitry Yudin, Maxim Monastyrny, Aleksei Valenkov
First submitted to arxiv on: 11 Jun 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 proposes a modular approach called BBQ (Beyond Bare Queries) to locate objects described in natural language for autonomous agents. The existing CLIP-based open-vocabulary methods are successful with simple queries, but struggle with ambiguous descriptions that require understanding object relations. BBQ constructs 3D scene graph representation and utilizes a large language model as an interface through deductive scene reasoning algorithm. It employs robust DINO-powered associations to construct 3D object-centric map and advanced raycasting algorithm for description. On Replica, ScanNet, Sr3D+, Nr3D, and ScanRefer datasets, BBQ outperforms zero-shot methods in open-vocabulary 3D semantic segmentation, particularly effective for scenes with multiple entities of the same class. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps robots better understand what people are saying about objects. It’s like teaching a robot to have a conversation! Right now, robots can only understand simple words and not complex sentences that describe many objects or their relationships. The authors developed a new way called BBQ (Beyond Bare Queries) that lets the robot build a 3D map of a scene and figure out what people are talking about. They tested it on different datasets and showed that it works better than other methods. This is important because robots need to understand complex instructions to do tasks like picking up objects or following directions. |
Keywords
» Artificial intelligence » Large language model » Semantic segmentation » Zero shot
