Summary of Towards Open-set Camera 3d Object Detection, by Zhuolin He et al.
Towards Open-set Camera 3D Object Detection
by Zhuolin He, Xinrun Li, Heng Gao, Jiachen Tang, Shoumeng Qiu, Wenfu Wang, Lvjian Lu, Xuchong Qiu, Xiangyang Xue, Jian Pu
First submitted to arxiv on: 25 Jun 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 OS-Det3D framework enhances the ability of camera 3D object detectors to recognize both known and unknown objects. The two-stage training approach involves a 3D Object Discovery Network (ODN3D) that learns geometric cues for general 3D object detection, followed by a Joint Objectness Selection (JOS) module to improve accuracy in identifying unknown objects. The ODN3D is trained class-agnostically and leverages data noisy region proposals. The JOS module combines ODN3D objectness with camera feature attention objectness to select pseudo ground truth for unknown objects. Experimental results on nuScenes and KITTI datasets show the effectiveness of OS-Det3D in detecting both known and unknown objects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper solves a problem with traditional 3D object detectors that can’t recognize new objects they haven’t been trained on. The researchers created a two-part system called OS-Det3D that helps camera 3D detectors find both familiar and unfamiliar objects. The first part is the ODN3D, which uses special features like location and size to spot general 3D objects. The second part is the JOS module, which picks out the correct answer for unknown objects from the noisy region proposals. This system was tested on two big datasets and showed that it can successfully find both known and unknown objects. |
Keywords
» Artificial intelligence » Attention » Object detection
