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..
PCP-MAE: Learning to Predict Centers for Point Masked Autoencoders
Authors: Xiangdong Zhang, Shaofeng Zhang, Junchi Yan
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments including object classification, few-shot learning and segmentation to demonstrate the superior performance of our method over Point-MAE. |
| Researcher Affiliation | Academia | Xiangdong Zhang , Shaofeng Zhang , Junchi Yan Dept. of CSE & School of AI & Mo E Key Lab of Al, Shanghai Jiao Tong University EMAIL |
| Pseudocode | No | The paper describes algorithms and modules in prose and through diagrams (Figure 2) but does not include a formally labeled 'Pseudocode' or 'Algorithm' block. |
| Open Source Code | Yes | The code is available at https://github.com/a Hap Bean/PCP-MAE . |
| Open Datasets | Yes | PCP-MAE is pre-trained on Shape Net [3] which consists of about 51,300 clean 3-D models, covering 55 common object categories. |
| Dataset Splits | No | The paper describes various datasets used for pre-training and fine-tuning (ShapeNet, Scan Object NN, ModelNet40, ShapeNet Part, S3DIS) and mentions augmentation and patching strategies, but it does not provide explicit numerical training, validation, or test splits (e.g., '80/10/10 split') in its text. It relies on 'established protocols' for these datasets. |
| Hardware Specification | Yes | We run all experiments with single GPU either using RTX 3090 (24GB) or V100 (32GB). |
| Software Dependencies | No | The paper mentions 'Adam W optimizer [22]' and 'A cosine learning rate decay scheduler [21]' but does not specify software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | The backbone of our pre-trained PCP-MAE consists of standard Transformer blocks where the encoder has 12 Transformer blocks and the decoder has 4, aligned with Point MAE [24]. The hidden dimension of Transformer blocks is 384 and the number of heads is 6. ... The PCP-MAE is pre-trained for 300 epochs using an Adam W optimizer [22] with a batch size of 128. The initial learning rate is set at 0.0005, with a weight decay of 0.05. A cosine learning rate decay scheduler [21] is utilized. |