Summary of Openobj: Open-vocabulary Object-level Neural Radiance Fields with Fine-grained Understanding, by Yinan Deng et al.
OpenObj: Open-Vocabulary Object-Level Neural Radiance Fields with Fine-Grained Understanding
by Yinan Deng, Jiahui Wang, Jingyu Zhao, Jianyu Dou, Yi Yang, Yufeng Yue
First submitted to arxiv on: 12 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Robotics (cs.RO)
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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 OpenObj, an innovative approach to build open-vocabulary object-level Neural Radiance Fields (NeRF) with fine-grained understanding. The method establishes a robust framework for efficient and watertight scene modeling and comprehension at the object-level, incorporating part-level features into neural fields to enable nuanced representations of object interiors. OpenObj captures object-level instances while maintaining a fine-grained understanding, achieving superior performance in zero-shot semantic segmentation and retrieval tasks on multiple datasets. Additionally, it supports real-world robotics tasks at multiple scales, including global movement and local manipulation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a way to make 3D models of objects using special computer programs called Neural Radiance Fields (NeRF). The program, called OpenObj, can look inside objects and understand their parts. It’s better than other methods because it can do all this without needing lots of training data or being limited to just one type of object. The results show that OpenObj is very good at recognizing objects and doing tasks like robotics. |
Keywords
» Artificial intelligence » Semantic segmentation » Zero shot
