Summary of Robust 3d Point Clouds Classification Based on Declarative Defenders, by Kaidong Li et al.
Robust 3D Point Clouds Classification based on Declarative Defenders
by Kaidong Li, Tianxiao Zhang, Cuncong Zhong, Ziming Zhang, Guanghui Wang
First submitted to arxiv on: 13 Oct 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 research paper addresses the challenge of bridging the domain gap between 2D images and 3D point clouds, allowing for model interchangeability. The authors explore three distinct algorithms to map 3D point clouds into 2D images, examining their performance and defense mechanisms through extensive experiments. Leveraging large foundation models, they analyze feature disparities between regular 2D images and projected 2D images. The proposed approaches demonstrate superior accuracy and robustness against adversarial attacks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper is about making it possible to use the same machine learning models for both 2D image classification and 3D point cloud classification. The authors develop new methods to transform 3D point clouds into 2D images that can be used with existing models. They test these approaches and show they work well even when trying to trick them. |
Keywords
» Artificial intelligence » Classification » Image classification » Machine learning
