Summary of Bip3d: Bridging 2d Images and 3d Perception For Embodied Intelligence, by Xuewu Lin et al.
BIP3D: Bridging 2D Images and 3D Perception for Embodied Intelligence
by Xuewu Lin, Tianwei Lin, Lichao Huang, Hongyu Xie, Zhizhong Su
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 novel image-centric 3D perception model, BIP3D, leverages expressive image features with explicit 3D position encoding to overcome limitations of point-centric methods. It combines pre-trained 2D vision foundation models for semantic understanding and a spatial enhancer module for spatial understanding, enabling multi-view, multi-modal feature fusion and end-to-end 3D perception. The model outperforms current state-of-the-art results on the EmbodiedScan benchmark in both 3D detection and visual grounding tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this research, scientists created a new way for robots to understand their surroundings. They developed an algorithm that uses images instead of just points to create a 3D picture of the environment. This helps robots make better decisions by combining what they see with where things are located in space. The new method worked better than previous methods on tests, showing promise for future applications. |
Keywords
» Artificial intelligence » Grounding » Multi modal
