Summary of Rctrans: Radar-camera Transformer Via Radar Densifier and Sequential Decoder For 3d Object Detection, by Yiheng Li et al.
RCTrans: Radar-Camera Transformer via Radar Densifier and Sequential Decoder for 3D Object Detection
by Yiheng Li, Yang Yang, Zhen Lei
First submitted to arxiv on: 17 Dec 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 A novel query-based detection method, Radar-Camera Transformer (RCTrans), is introduced to overcome the challenges of fusing camera and radar modalities in 3D object detection. The approach combines Radar Dense Encoder and Pruning Sequential Decoder to predict 3D boxes from radar point clouds and images. A pruning training strategy is used to save time during inference and maintain query distinctiveness. Experimental results on nuScenes dataset demonstrate the superiority of RCTrans, achieving new state-of-the-art radar-camera 3D detection results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Radar-Camera Transformer (RCTrans) is a new way to detect objects in 3D space using cameras and radar. The method combines two steps: making radar points more useful by adding more information, and then using that information to predict the location of an object. This helps to avoid mistakes caused by empty spaces between objects. The results show that RCTrans works better than other methods. |
Keywords
» Artificial intelligence » Decoder » Encoder » Inference » Object detection » Pruning » Transformer
