Summary of Point-detr3d: Leveraging Imagery Data with Spatial Point Prior For Weakly Semi-supervised 3d Object Detection, by Hongzhi Gao et al.
Point-DETR3D: Leveraging Imagery Data with Spatial Point Prior for Weakly Semi-supervised 3D Object Detection
by Hongzhi Gao, Zheng Chen, Zehui Chen, Lin Chen, Jiaming Liu, Shanghang Zhang, Feng Zhao
First submitted to arxiv on: 22 Mar 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 This paper proposes a novel approach to training high-accuracy 3D detectors using point annotations, which are more accessible and less expensive than traditional 7-degree-of-freedom annotations. The authors introduce Point-DETR3D, a teacher-student framework for weakly semi-supervised 3D detection that leverages point-wise supervision within a constrained instance-wise annotation. The model encodes 3D positional information through a point encoder and employs an explicit positional query initialization strategy to enhance the positional prior. Additionally, the detector’s perception is enhanced by incorporating dense imagery data through Cross-Modal Deformable RoI Fusion (D-RoI). Furthermore, the authors propose a point-guided self-supervised learning technique that allows for fully exploiting point priors. Experiments on the nuScenes dataset demonstrate significant improvements compared to previous works, with Point-DETR3D achieving over 90% performance of its fully supervised counterpart using only 5% labeled data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper finds a way to train 3D detectors more easily and accurately by using points instead of detailed 7-degree-of-freedom annotations. The authors create a new model called Point-DETR3D that uses point information to help with object localization. They also come up with ways to make the model better, such as adding imagery data and using self-supervised learning. Tests on a real-world dataset show that this approach works really well and can even beat traditional methods when only a small amount of labeled data is available. |
Keywords
» Artificial intelligence » Encoder » Self supervised » Semi supervised » Supervised
