Summary of Bpt: Binary Point Cloud Transformer For Place Recognition, by Zhixing Hou et al.
BPT: Binary Point Cloud Transformer for Place Recognition
by Zhixing Hou, Yuzhang Shang, Tian Gao, Yan Yan
First submitted to arxiv on: 2 Mar 2023
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: None
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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 proposed binary point cloud transformer for place recognition tackles the challenge of deploying transformer networks on mobile or embedded devices. Building upon existing works that leveraged MLP, CNN, and transformer frameworks for place recognition in robotics, this research aims to reduce memory consumption and computation costs while maintaining performance. The authors achieve a 1-bit model from a 32-bit full-precision model using binarized bitwise operations, making it feasible for online applications like place recognition on mobile devices. Experimental results on standard benchmarks demonstrate comparable or even superior performance compared to full-precision transformer models, with notable reductions in model size and floating point operations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The research proposes a new way to recognize places using “point cloud transformers” that can work on small devices like phones. Right now, these types of models are too big and slow for many devices, but this new approach makes them smaller and faster while still being accurate. The team tested their model on some well-known datasets and found it worked just as well as the bigger versions, even beating some of them! |
Keywords
* Artificial intelligence * Cnn * Precision * Transformer
