用生成模型将3D点云转为规则2D图像，提升分类鲁棒性
Robust 3D Point Clouds Classification based on Declarative Defenders
- 用生成模型替代传统投影，生成连贯的2D图像
- 在ModelNet40上准确率达98.7%，对抗攻击下仍保持高鲁棒性
- 适合需要跨域泛化的3D点云分类任务
3D点云分类因输入数据无结构、稀疏，与2D图像的有结构、稠密特性差异显著，难以直接复用2D模型。尽管基于格点的点云分类器（LPC）展示了跨域可行性，但其格点投影会产生不连续像素，加剧域差距。本文探索三种将3D点云映射为2D图像的方法，通过大量实验评估性能与防御能力。结合现有大模型分析常规2D图像与投影图像的特征差异，结果表明：基于生成模型的映射方法能生成连续2D图像，显著缩小与标准2D分类任务的域差距，在ModelNet40数据集上实现98.7%准确率，并在对抗攻击下表现出更强鲁棒性。代码已开源。
原文摘要 · Abstract (English)
3D point cloud classification requires distinct models from 2D image classification due to the divergent characteristics of the respective input data. While 3D point clouds are unstructured and sparse, 2D images are structured and dense. Bridging the domain gap between these two data types is a non-trivial challenge to enable model interchangeability. Recent research using Lattice Point Classifier (LPC) highlights the feasibility of cross-domain applicability. However, the lattice projection operation in LPC generates 2D images with disconnected projected pixels. In this paper, we explore three distinct algorithms for mapping 3D point clouds into 2D images. Through extensive experiments, we thoroughly examine and analyze their performance and defense mechanisms. Leveraging current large foundation models, we scrutinize the feature disparities between regular 2D images and projected 2D images. The proposed approaches demonstrate superior accuracy and robustness against adversarial attacks. The generative model-based mapping algorithms yield regular 2D images, further minimizing the domain gap from regular 2D classification tasks. The source code is available at https://github.com/KaidongLi/pytorch-LatticePointClassifier.git.
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