PCP-MAE: Learning to Predict Centers for Point Masked Autoencoders
Authors: Xiangdong Zhang, Shaofeng Zhang, Junchi Yan
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | 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 {zhangxiangdong, sherrylone, yanjunchi}@sjtu.edu.cn |
| 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. |