Summary of G3d-lf: Generalizable 3d-language Feature Fields For Embodied Tasks, by Zihan Wang et al.
g3D-LF: Generalizable 3D-Language Feature Fields for Embodied Tasks
by Zihan Wang, Gim Hee Lee
First submitted to arxiv on: 26 Nov 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 The paper introduces Generalizable 3D-Language Feature Fields (g3D-LF), a pre-trained model that processes posed RGB-D images from agents to encode feature fields. These features enable novel view representation predictions, generation of bird’s eye views, and querying targets using multi-granularity language. The g3D-LF can be generalized to unseen environments, allowing real-time construction and dynamic updates. The authors prepare a large-scale 3D-language dataset to align the representations with language. Extensive experiments on Vision-and-Language Navigation, Zero-shot Object Navigation, and Situated Question Answering tasks demonstrate the effectiveness of g3D-LF for embodied tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to understand and communicate about 3D spaces using words. It creates a model that can learn from lots of images and text together, so it can recognize and describe things in different views and perspectives. This helps robots and computers better understand their surroundings and answer questions about what they see. |
Keywords
» Artificial intelligence » Question answering » Zero shot
