Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions
Authors: Jiachen Sun, Yulong Cao, Christopher B Choy, Zhiding Yu, Anima Anandkumar, Zhuoqing Morley Mao, Chaowei Xiao
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Through extensive experimentation, we demonstrate that appropriate applications of self-supervision can significantly enhance the robustness in 3D point cloud recognition, achieving considerable improvements compared to the standard adversarial training baseline. |
| Researcher Affiliation | Collaboration | Jiachen Sun 1, Yulong Cao 1, Christopher Choy 2, Zhiding Yu 2, Anima Anandkumar 2,3, Z. Morley Mao 1, and Chaowei Xiao 2,4 1 University of Michigan, 2 NVIDIA, 3 Caltech, 4 ASU |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement about open-source code availability or a link to a code repository for the methodology described. |
| Open Datasets | Yes | We leverage four datasets (D): Model Net40 [30] (40 classes), Model Net10 [30] (10 classes), Scan Object NN [41] (15 classes), and Shape Net Part [42] throughout our experiments. |
| Dataset Splits | No | We follow the default split of training and test sets in [5] and [43]. |
| Hardware Specification | Yes | All experiments are done on 1 to 4 NVIDIA V100 GPUs [45]. |
| Software Dependencies | No | The paper mentions using Adam [44] for optimization but does not list other specific software dependencies with version numbers (e.g., PyTorch, CUDA versions). |
| Experiment Setup | Yes | We use batch sizes of 32 for Point Net and DGCNN, and 128 for PCT. The initial learning rate is set to 0.001 for Point Net and DGCNN, and 5 10 4 for PCT. Both pre-training and fine-tuning take 250 epochs, where a 10 decay happens at the 100-th, 150-th, and 200-th epoch. |