Summary of Polarbevdet: Exploring Polar Representation For Multi-view 3d Object Detection in Bird’s-eye-view, by Zichen Yu et al.
PolarBEVDet: Exploring Polar Representation for Multi-View 3D Object Detection in Bird’s-Eye-View
by Zichen Yu, Quanli Liu, Wei Wang, Liyong Zhang, Xiaoguang Zhao
First submitted to arxiv on: 29 Aug 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 The proposed PolarBEVDet method utilizes a polar Bird’s-Eye-View (BEV) representation to improve multi-view 3D object detection in autonomous driving applications. This approach adapts image information distribution and preserves view symmetry through regular convolution, replacing traditional Cartesian BEV representations. The method comprises three modules: a polar view transformer, a polar temporal fusion module, and a polar detection head. Additionally, it incorporates a 2D auxiliary detection head and spatial attention enhancement module to enhance feature extraction. Experimental results on nuScenes demonstrate superior performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PolarBEVDet is a new way to detect objects in 3D using multiple cameras. Normally, these cameras are shown as a big grid (Cartesian BEV) but this method uses a different way of showing the images called polar BEV. This change helps with two problems: uneven information and view symmetry. The method has three parts that work together to improve detection: a transformer, a fusion module, and a detection head. It also adds some extra help for detecting objects in perspective views (2D) and BEV views. The results show it works better than other methods on the nuScenes dataset. |
Keywords
» Artificial intelligence » Attention » Feature extraction » Object detection » Transformer
