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..
Joint-MAE: 2D-3D Joint Masked Autoencoders for 3D Point Cloud Pre-training
Authors: Ziyu Guo, Renrui Zhang, Longtian Qiu, Xianzhi Li, Pheng-Ann Heng
IJCAI 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate the performance of Joint-MAE on various downstream tasks, i.e., shape classification, few-shot classification, and part segmentation. Joint-MAE achieves superior performance on multiple downstream tasks, e.g., 92.4% accuracy for linear SVM on Model Net40 and 86.07% accuracy on the hardest split of Scan Object NN. |
| Researcher Affiliation | Academia | 1 Department of Computer Science and Engineering, The Chinese University of Hong Kong 2 CUHK MMLab 3Huazhong University of Science and Technology 4Institute of Medical Intelligence and XR, The Chinese University of Hong Kong 5Shanghai Tech University |
| Pseudocode | No | The paper describes its method using textual descriptions and architectural diagrams (e.g., Figure 2) but does not provide any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the public availability of its source code. |
| Open Datasets | Yes | Following existing works [Pang et al., 2022; Zhang et al., 2022a], we pre-train our Joint-MAE on Shape Net [Chang et al., 2015], which covers 57,448 3D shapes of 55 categories. We utilize a simple a classification head of linear layers and evaluate the accuracy on Model Net40 [Wu et al., 2015a] and Scan Object NN [Uy et al., 2019] datasets, which contain synthetic objects and real-world instances, respectively. |
| Dataset Splits | Yes | we pre-train our Joint-MAE on Shape Net [Chang et al., 2015]... we train on 9,843 instances and test on 2,468 instances with 40 categories. Scan Object NN dataset, which consists of 2,304 ob-jects for training and 576 objects for testing. |
| Hardware Specification | No | The paper does not specify any particular hardware components (e.g., GPU models, CPU types, or memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or library versions used for implementation or experimentation. |
| Experiment Setup | Yes | The input point number N is set as 2,048 and the depth map size H W is set as 224 224. We adopt a feature dimension C as 384. Please refer to the Supplementary Material for detailed implementation details. |