Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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 | Venue PDF | 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 ο¬ne-tuning take 250 epochs, where a 10 decay happens at the 100-th, 150-th, and 200-th epoch. |