Improving Robustness of 3D Point Cloud Recognition from a Fourier Perspective
Authors: Yibo Miao, Yinpeng Dong, Jinlai Zhang, Lijia Yu, Xiao Yang, Xiao-Shan Gao
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, we conducted extensive experiments with various network architectures to validate the effectiveness of FAT, which achieves the new state-of-the-art results. |
| Researcher Affiliation | Collaboration | Yibo Miao1,2, Yinpeng Dong3,6 , Jinlai Zhang4, Lijia Yu5, Xiao Yang3, Xiao-Shan Gao1,2 1 KLMM, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China 2 University of Chinese Academy of Sciences, Beijing 100049, China 3 Tsinghua University, Beijing 100084, China 4 Changsha University of Science and Technology, Changsha 410114, China 5 Institute of Software, Chinese Academy of Sciences, Beijing 100190, China 6 Real AI |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | We have provided our codes in the supplemental matrial. |
| Open Datasets | Yes | To validate the effectiveness of our FAT method in enhancing the corruption robustness of 3D point cloud recognition models, we train all models on the standard Model Net40 training set [67]. In addition to reporting the performance of the models on the original Model Net40 validation set, we also evaluate the corruption robustness on Model Net-C [41] in the main paper and Model Net40-C [51] in Appendix B. |
| Dataset Splits | Yes | In addition to reporting the performance of the models on the original Model Net40 validation set, we also evaluate the corruption robustness on Model Net-C [41] in the main paper and Model Net40-C [51] in Appendix B. The Model Net40 dataset [67] contains 12,311 CAD models with 40 common object categories in the real world. We use the official split [35], where 9,843 examples are used for training and the remaining 2,468 examples are used for testing. |
| Hardware Specification | Yes | All of the experiments are conducted on NVIDIA Tesla V100 GPUs. |
| Software Dependencies | No | The paper mentions software components like "Adam optimizer" and "smooth cross-entropy loss" but does not specify their version numbers or other specific software dependencies like Python or PyTorch versions. |
| Experiment Setup | Yes | For each method, we train 250 epochs using the smooth cross-entropy loss [65] and Adam optimizer [23], and select the best performant model for further evaluation. We follow the DGCNN protocol [16]. For our method, we set k = 30 for the k-nearest neighbor graph and λ = 100 for dividing high-frequency and low-frequency [29]. We use PGD [33] and AOF [27] to generate high-frequency adversarial examples and low-frequency adversarial examples, respectively. We constrain Sh and Sl by 0.3 and 0.5, respectively. For more detailed training settings, please refer to Appendix B. |